diff --git a/.github/workflows/check-working-examples.yaml b/.github/workflows/check-working-examples.yaml index 2c19a341e..55ae812fb 100644 --- a/.github/workflows/check-working-examples.yaml +++ b/.github/workflows/check-working-examples.yaml @@ -8,7 +8,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: ["3.10"] + python-version: ["3.9", "3.10", "3.11"] os: [ubuntu-latest] #, macos-latest, windows-latest] fail-fast: False @@ -25,9 +25,14 @@ jobs: pip install nbconvert # For converting Jupyter notebook to python script in the next step - name: Run examples # Run all examples and test that they finish successfully. Do not evaluate the results. + # Copy the examples to a new directory outside of the repo to ensure that there is no + # reliance on the repo directory structure. run: | - cd examples/ + mkdir -p temp1/temp2/temp3 + cp -r examples/ temp1/temp2/temp3/. + cd temp1/temp2/temp3/examples/ + error_found=0 # 0 is false error_results="Error in example:" @@ -48,27 +53,6 @@ jobs: fi done - # Run all Jupyter notebooks - for i in *.ipynb; do - - # Convert this notebook to a Python script - if ! jupyter nbconvert --to script $i; then - # On conversion error, report and go to the next notebook - error_results="${error_results}"$'\n'" - Error converting ${i} to Python script" - continue - fi - - # Get the basename of the notebook since the converted script will have the same basename - script_name=`basename $i .ipynb` - - # Run the converted script - if ! python "${script_name}.py"; then - error_results="${error_results}"$'\n'" - ${i}" - error_found=1 - fi - - done - if [[ $error_found ]]; then echo "${error_results}" fi diff --git a/.github/workflows/continuous-integration-workflow.yaml b/.github/workflows/continuous-integration-workflow.yaml index 6da584b19..5e27b3c38 100644 --- a/.github/workflows/continuous-integration-workflow.yaml +++ b/.github/workflows/continuous-integration-workflow.yaml @@ -8,7 +8,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.8", "3.9", "3.10", "3.11"] os: [ubuntu-latest] #, macos-latest, windows-latest] fail-fast: False env: diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index d0eca0642..436f17b97 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -77,4 +77,4 @@ submit a new feature, let us know in We rely heavily on git and GitHub, so be sure to review the contributing guidelines in the -[online documentation](https://floris.readthedocs.io/en/main/source/developers.html). +[online documentation](https://nrel.github.io/floris/dev_guide.html). diff --git a/README.md b/README.md index 3e410e0cc..5a30881bd 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ FLORIS is a controls-focused wind farm simulation software incorporating steady-state engineering wake models into a performance-focused Python framework. It has been in active development at NREL since 2013 and the latest -release is [FLORIS v3.5](https://github.com/NREL/floris/releases/latest). +release is [FLORIS v3.6](https://github.com/NREL/floris/releases/latest). Online documentation is available at https://nrel.github.io/floris. The software is in active development and engagement with the development team @@ -71,7 +71,7 @@ and importing FLORIS: version VERSION - 3.5 + 3.6 FILE ~/floris/floris/__init__.py diff --git a/docs/_config.yml b/docs/_config.yml index 9819e86fd..9a3c991d0 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -44,13 +44,13 @@ sphinx: - 'sphinx.ext.viewcode' - 'sphinx_autodoc_typehints' - 'sphinxcontrib.autoyaml' - - 'sphinx.ext.napoleon' # Formats google and numpy docstring styles - 'sphinxcontrib.mermaid' config: - html_theme: sphinx_book_theme - templates_path: - - '_templates' language: 'python' + nb_execution_show_tb: true # Shows the stack trace in stdout; its suppressed otherwise. + nb_execution_raise_on_error: true # Stops the Sphinx build if there is an error in a notebook. See https://github.com/executablebooks/jupyter-book/issues/2011 + suppress_warnings: + - etoc.toctree # autodoc output contains toctrees, so suppress this warning. See https://github.com/executablebooks/sphinx-external-toc/issues/36 autoyaml_level: 3 autosummary_generate: true @@ -60,7 +60,7 @@ sphinx: members: true member-order: bysource undoc-members: true - private-members: true + private-members: false # special-members: true # inherited-members # show-inheritance @@ -70,3 +70,4 @@ sphinx: # class-doc-from # no-value autodoc_typehints: both + mermaid_version: "10.8" diff --git a/docs/_templates/custom-class-template.rst b/docs/_templates/custom-class-template.rst deleted file mode 100644 index f73eda50e..000000000 --- a/docs/_templates/custom-class-template.rst +++ /dev/null @@ -1,34 +0,0 @@ -{{ fullname | escape | underline}} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - :members: - :show-inheritance: - :inherited-members: - :special-members: __call__, __add__, __mul__ - - {% block methods %} - {% if methods %} - .. rubric:: {{ _('Methods') }} - - .. autosummary:: - :nosignatures: - {% for item in methods %} - {%- if not item.startswith('_') %} - ~{{ name }}.{{ item }} - {%- endif -%} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block attributes %} - {% if attributes %} - .. rubric:: {{ _('Attributes') }} - - .. autosummary:: - {% for item in attributes %} - ~{{ name }}.{{ item }} - {%- endfor %} - {% endif %} - {% endblock %} diff --git a/docs/_templates/custom-module-template.rst b/docs/_templates/custom-module-template.rst deleted file mode 100644 index d066d0e4d..000000000 --- a/docs/_templates/custom-module-template.rst +++ /dev/null @@ -1,66 +0,0 @@ -{{ fullname | escape | underline}} - -.. automodule:: {{ fullname }} - - {% block attributes %} - {% if attributes %} - .. rubric:: Module attributes - - .. autosummary:: - :toctree: - {% for item in attributes %} - {{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block functions %} - {% if functions %} - .. rubric:: {{ _('Functions') }} - - .. autosummary:: - :toctree: - :nosignatures: - {% for item in functions %} - {{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block classes %} - {% if classes %} - .. rubric:: {{ _('Classes') }} - - .. autosummary:: - :toctree: - :template: custom-class-template.rst - :nosignatures: - {% for item in classes %} - {{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - - {% block exceptions %} - {% if exceptions %} - .. rubric:: {{ _('Exceptions') }} - - .. autosummary:: - :toctree: - {% for item in exceptions %} - {{ item }} - {%- endfor %} - {% endif %} - {% endblock %} - -{% block modules %} -{% if modules %} -.. autosummary:: - :toctree: - :template: custom-module-template.rst - :recursive: -{% for item in modules %} - {{ item }} -{%- endfor %} -{% endif %} -{% endblock %} diff --git a/docs/_toc.yml b/docs/_toc.yml index c354c1b84..91199ffc0 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -2,16 +2,16 @@ # Learn more at https://jupyterbook.org/customize/toc.html format: jb-book -root: intro +root: index parts: - caption: Getting Started chapters: - # - file: intro - file: installation - caption: User Reference chapters: - - file: floris_101 + - file: intro_concepts + - file: advanced_concepts - file: floating_wind_turbine - file: turbine_interaction - file: input_reference_main @@ -21,6 +21,8 @@ parts: - caption: Theory and Background chapters: - file: wake_models + sections: + - file: empirical_gauss_model - file: bibliography - caption: Developer Reference diff --git a/docs/advanced_concepts.ipynb b/docs/advanced_concepts.ipynb new file mode 100644 index 000000000..aae2869fb --- /dev/null +++ b/docs/advanced_concepts.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(concepts_advanced)=\n", + "\n", + "# Advanced Concepts\n", + "\n", + "More information regarding the numerical and computational formulation in FLORIS\n", + "are detailed here. See [](concepts_intro) for a guide on the basics." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a basic FLORIS model for use later\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from floris.tools import FlorisInterface\n", + "fi = FlorisInterface(\"gch.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data structures\n", + "\n", + "FLORIS adopts a structures of arrays data modeling paradigm (SoA, relative to array of structures {AoS})\n", + "for nearly all of the data in the `floris.simulation` package.\n", + "This data model enables vectorization (SIMD operations) through Numpy array broadcasting\n", + "for many operations.\n", + "In general, there are two types of array shapes:\n", + "- Field quantities have points throughout the computational domain but in context-specific locations\n", + " and have the shape `(N wind directions, n wind speeds, n turbines, n grid, n grid)`.\n", + "- Scalar quantities have a single value for each turbine and typically have the shape\n", + " `(N wind directions, n wind speeds, n turbines, 1, 1)`. For scalar quanities, the arrays\n", + " may be created with the shape `(N wind directions, n wind speeds, n turbines)` and\n", + " then expanded to the 5-dimensional shape prior to running the wake calculation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Grids\n", + "\n", + "FLORIS includes a number of grid-types that create sampling points within the computational\n", + "domain for different contexts. In the typical use case, AEP or some other metric of wind\n", + "farm energy yield is the end result. Since the mathematical models in FLORIS are all\n", + "analytical, we only need to create points on the turbines themselves in order to calculate\n", + "the incoming wind speeds given all of the upstream conditions. In this case, we use\n", + "the {py:meth}`floris.simulation.grid.TurbineGrid` or {py:meth}`floris.simulation.grid.TurbineCubatureGrid`.\n", + "Each of these grid-types put points only on the turbine swept area, so all other\n", + "field-quantities in FLORIS have the same shape." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the grid point locations for TurbineGrid and TurbineCubatureGrid\n", + "\n", + "fi.reinitialize(layout_x=[0.0], layout_y=[0.0])\n", + "rotor_radius = fi.floris.farm.rotor_diameters[0] / 2.0\n", + "hub_height = fi.floris.farm.hub_heights[0]\n", + "theta = np.linspace(0, 2*np.pi, 100)\n", + "circlex = rotor_radius * np.cos(theta)\n", + "circley = rotor_radius * np.sin(theta) + hub_height\n", + "\n", + "# TurbineGrid is the default\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(0, hub_height, marker=\"+\", color=\"r\")\n", + "ax.scatter(fi.floris.grid.y_sorted[0,0,0], fi.floris.grid.z_sorted[0,0,0], marker=\"+\", color=\"r\")\n", + "ax.plot(circlex, circley)\n", + "ax.set_aspect('equal', 'box')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "floris", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/api_docs.rst b/docs/api_docs.md similarity index 57% rename from docs/api_docs.rst rename to docs/api_docs.md index add2940c1..e94478f75 100644 --- a/docs/api_docs.rst +++ b/docs/api_docs.md @@ -1,16 +1,15 @@ -API Documentation -================= +# API Documentation FLORIS is divided into two primary packages. -:py:mod:`floris.simulation` is the core code that models the wind turbines -and wind farms. It is low-level code that generally is nto accessed -by typical users. :py:mod:`floris.tools` is the set of analysis routines +{py:mod}`floris.simulation` is the core code that models the wind turbines +and wind farms. It is low-level code that generally is not accessed +by typical users. {py:mod}`floris.tools` is the set of analysis routines that define, drive, and post process a simulation. This is where more users will interface with the software. +```{eval-rst} .. autosummary:: :toctree: _autosummary - :template: custom-module-template.rst :recursive: floris.logging_manager @@ -19,3 +18,4 @@ more users will interface with the software. floris.type_dec floris.turbine_library floris.utilities +``` diff --git a/docs/architecture.md b/docs/architecture.md index 682aa5c8b..cdfa60bb4 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -7,9 +7,10 @@ be violated, and ongoing work should strive to meet these ideas and expand on th as possible. - Modularity in wake model formulation: - - New mathematical formulation should be straightforward to incorporate. - - Requisite solver and grid data structures should not conflict with other existing - wake models. + - New mathematical formulation should be straightforward to incorporate by non-expert + software developers. + - Solver and grid data structures for one wake model should not conflict with the data + structures for other wake models. - Any new feature or work should not affect an existing feature: - Low level code should be reused as much as possible, but high level code should rarely be repurposed. @@ -31,12 +32,12 @@ packages. The internal structure and hierarchy is described below. ```{mermaid} classDiagram - class tools { - +FlorisInterface + class simulation["floris.simulation"] { + +Floris } - class simulation { - +Floris + class tools["floris.tools"] { + +FlorisInterface } class logging_manager @@ -45,9 +46,7 @@ classDiagram tools <-- logging_manager simulation <-- logging_manager - tools <-- type_dec simulation <-- type_dec - tools <-- utilities simulation <-- utilities tools <-- simulation ``` @@ -75,54 +74,61 @@ classDiagram class Farm class FlowField { - array u - array v - array w + u: NDArrayFloat + v: NDArrayFloat + w: NDArrayFloat } class Grid { <> + x: NDArrayFloat + y: NDArrayFloat + z: NDArrayFloat } class TurbineGrid + class TurbineCubatureGrid class FlowFieldPlanarGrid + class PointsGrid class WakeModelManager { <> } class WakeCombination { - dict parameters + parameters: dict function() } class WakeDeflection { - dict parameters + parameters: dict function() } class WakeTurbulence { - dict parameters + parameters: dict function() } class WakeVelocity { - dict parameters + parameters: dict function() } class Solver { <> - dict parameters + parameters: dict } - Floris o-- Farm - Floris o-- FlowField - Floris o-- Grid - Floris o-- WakeModelManager - Floris *-- Solver - WakeModelManager o-- WakeCombination - WakeModelManager o-- WakeDeflection - WakeModelManager o-- WakeTurbulence - WakeModelManager o-- WakeVelocity + Floris *-- Farm + Floris *-- FlowField + Floris *-- Grid + Floris *-- WakeModelManager + Floris --> Solver + WakeModelManager *-- WakeCombination + WakeModelManager *-- WakeDeflection + WakeModelManager *-- WakeTurbulence + WakeModelManager *-- WakeVelocity Grid <|-- TurbineGrid + Grid <|-- TurbineCubatureGrid Grid <|-- FlowFieldPlanarGrid + Grid <|-- PointsGrid Solver --> Farm Solver --> FlowField diff --git a/docs/code_quality.ipynb b/docs/code_quality.ipynb index d6a4322ee..fc7fa9374 100644 --- a/docs/code_quality.ipynb +++ b/docs/code_quality.ipynb @@ -57,72 +57,85 @@ "\n", "output_notebook()\n", "\n", - "COLORS = ['blue', 'green', 'red', 'cyan', 'm', 'y', 'k']\n", + "COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'y', 'k']\n", "\n", - "columns = [\"commit_hash\", \"commit_hash_8char\", \"date\", \"jensen\", \"gauss\", \"gch\", \"cc\", \"code_coverage\", \"tooltip_label\"]\n", + "columns = [\"commit_hash\", \"commit_hash_8char\", \"date\", \"jensen\", \"gauss\", \"gch\", \"cc\", \"emgauss\", \"code_coverage\", \"tooltip_label\"]\n", "data = [\n", - " (\"df25a9cfacd3d652361d2bd37f568af00acb2631\", \"df25a9cf\", datetime(2021, 12, 29), 1.2691, 1.2584, 1.6432, None, 0.4344, \"df25a9cf\"),\n", - " (\"b797390a43298a815f3ff57955cfdc71ecf3e866\", \"b797390a\", datetime(2022, 1, 3), 0.6867, 1.2354, 1.8026, None, 0.2993, \"b797390a\"),\n", - " (\"01a02d5f91b2f4a863eebe88a618974b0749d1c4\", \"01a02d5f\", datetime(2022, 1, 4), 0.3697, 0.8080, 1.3633, None, 0.3022, \"01a02d5f\"),\n", - " (\"dd847210082035d43b0273ae63a76a53cb8d2e12\", 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@@ -224,8 +224,188 @@ jupyter-book build docs/ open docs/_build/html/index.html ``` +## Release guide + +Follow the process outlined here to "release" FLORIS. +After completing these steps, a few additional automated processes +are launched to deploy FLORIS to PyPI and conda-forge. +Be sure to complete each step in the sequence as described. + +1. Merge the `develop` branch into `main` with a pull request. + Create a pull request titled `FLORIS vN.M` with version number filled in + as appropriate. + The body of the pull request should a brief summary of the changes + as well as a listing of the major changes and their associated pull requests. + Since creating the pull request does not mean it is merged, it is + reasonable to create the pull request, and edit the body of the pull + request later. + The pull request template has a checklist at the bottom that should be + uncommented for this PR. + +2. Update the version number and commit to the `develop`` branch + with a commit message such as "Update version to vN.M". + The version number must be updated in the following two files: + - [floris/README.md](https://github.com/NREL/floris/blob/main/README.md) + - [floris/floris/version.py](https://github.com/NREL/floris/blob/main/floris/version.py) + Note that a `.0` version number is left off meaning that valid versions + are `v3`, `v3.1`, `v3.1.1`, etc. + +3. Verify that the documentation is building correctly. + The docs build for every commit to `develop`, so there should be no + surprises in this regard prior to a release. However, it's a good + opportunity to ensure that the documentation is up to date and there + are no obvious issues. + Check this by opening the documentation website at https://nrel.github.io/floris + and scrolling through the pages. + Also, verify that the automated build process has successfully completed + for the commits to `develop` in [GitHub Actions](https://github.com/NREL/floris/actions/workflows/deploy-pages.yaml). + +4. The changes since the prior commit can be gotten from GitHub by going through the + process to create a release, but stopping short of actually publishing it. + In this form, GitHub provides the option to autogenerate release notes. + Be sure to choose the correct starting tag, and then hit "Generate release notes". + Then, copy the generated text into the pull request body, and format it + as appropriate. A good reference is typically the previous release. + +5. Merge the pull request into `main`. Select "Create a merge commit" from the merge + dropdown, and hit "Merge pull request". + +6. Create a [new release](https://github.com/NREL/floris/releases/new) on GitHub + with the title "vN.M". Choose to create a new tag on publish with the same + name. Also, autogenerate the release notes again. If you autogenerated the release + notes in step 4, make sure to start this step from a new browser window. + Be sure that the "Set as latest release" radio button is enabled. + +7. Double check everything. + +8. Hit "Publish release". + +9. Go to GitHub Actions and watch the [Upload Python Package](https://github.com/NREL/floris/actions/workflows/python-publish.yml) + job complete. Upon success, FLORIS will be uploaded to PyPI for installation with pip. + If it fails, the latest release will not be distributed. + +10. Merge the main branch into develop to align all branches on all remotes. + +11. That's it, well done! + ## Deploying to pip Generally, only NREL developers will have appropriate permissions to deploy FLORIS updates. When the time comes, here is a great reference on doing it is available [here](https://medium.freecodecamp.org/how-to-publish-a-pyton-package-on-pypi-a89e9522ce24). + +## Extending the models + +The FLORIS architecture is designed to support adding new wake models relatively easily. +Each of the following components have a general API that support plugging in to the rest of the +FLORIS framework: +- Velocity deficit +- Wake deflection +- Added turbulence due to the turbine wake +- Wake combination +- Solver algorithm +- Grid-points + +Initially, it's recommended to copy an existing model as a starting point, and the +[Jensen](https://github.com/NREL/floris/blob/main/floris/simulation/wake_velocity/jensen.py) and +[Jimenez](https://github.com/NREL/floris/blob/main/floris/simulation/wake_deflection/jimenez.py) +models are good choices due to their simplicity. +New models must be registered in +[Wake.model_map](https://github.com/NREL/floris/blob/main/floris/simulation/wake.py#L45) +so that they can be enabled via the input dictionary. + +```{mermaid} +classDiagram + + class Floris + + class Farm + + class FlowField { + u: NDArrayFloat + v: NDArrayFloat + w: NDArrayFloat + } + + class Grid { + <> + x: NDArrayFloat + y: NDArrayFloat + z: NDArrayFloat + } + + class WakeModelManager { + <> + combination_model: BaseModel + deflection_model: BaseModel + velocity_model: BaseModel + turbulence_model: BaseModel + } + + class Solver { + <> + parameters: dict + } + + class BaseModel { + prepare_function() dict + function() None + } + + Floris *-- Farm + Floris *-- FlowField + Floris *-- Grid + Floris *-- WakeModelManager + Floris --> Solver + + Solver --> Farm + Solver --> FlowField + Solver --> Grid + Solver --> WakeModelManager + + WakeModelManager -- BaseModel + + style Grid stroke:#FF496B, stroke-width:2px + style WakeModelManager stroke:#FF496B, stroke-width:2px + style Solver stroke:#FF496B, stroke-width:2px +``` + +All of the models have a `prepare_function` and a `function` method. +The `prepare_function` allows the model classes to extract any information from the `Grid` and +`FlowField` data structures, and this is generally used for sizing the data arrays. +The `prepare_function` should return a dictionary that will ultimately be passed to the +`function`. +The `function` method is where the actual calculation is performed. +The API is dependent on the type of model, but generally it requires some indicationg of +the location of the current turbine in the solve step and some other information about the +atmospheric conditions and operation of the turbine. +Note the `*` in the function signature, which is a Python feature that allows +any number of arguments to be passed to the function after the `*` as keyword arguments. +Typically, these arguments are the ones returned from the `prepare_function`. + +```python +def prepare_function(self, grid: Grid, flow_field: FlowField) -> Dict[str, Any] + +def function( + self, + x_i: np.ndarray, + y_i: np.ndarray, + z_i: np.ndarray, + axial_induction_i: np.ndarray, + deflection_field_i: np.ndarray, + yaw_angle_i: np.ndarray, + turbulence_intensity_i: np.ndarray, + ct_i: np.ndarray, + hub_height_i: np.ndarray, + rotor_diameter_i: np.ndarray, + *, + variables_from_prepare_function: dict +) -> None: +``` + +Some models require a special grid and/or solver, and that mapping happens in +[floris.simulation.Floris](https://github.com/NREL/floris/blob/main/floris/simulation/floris.py#L145). +Generally, a specific kind of solver requires one or a number of specific grid-types. +For example, `full_flow_sequential_solver` requires either `FlowFieldGrid` or +`FlowFieldPlanarGrid`. +So, it is often the case that adding a new solver will require adding a new grid type, as well. diff --git a/docs/examples.md b/docs/examples.md index e87627cd4..eebb3c89d 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -7,7 +7,7 @@ intended to describe most features as well as provide a starting point for various analysis methods. These are generally ordered from simplest to most complex. The examples and their content are described below. Prior to exploring the examples, it is highly recommended to review -[](background_concepts). +[](concepts_intro). ## Basic setup and pre and post processing @@ -146,6 +146,14 @@ mast across all wind directions (at a fixed free stream wind speed). Try different values for met_mast_option to vary the location of the met mast within the two-turbine farm. +### 32_plot_velocity_deficit_profiles.py +This example illustrates how to plot velocity deficit profiles at several locations +downstream of a turbine. Here we use the following definition: + + velocity_deficit = (homogeneous_wind_speed - u) / homogeneous_wind_speed + , where u is the wake velocity obtained when the incoming wind speed is the + same at all heights and equal to `homogeneous_wind_speed`. + ### 29_floating_vs_fixedbottom_farm.py Compares a fixed-bottom wind farm (with a gridded layout) to a floating @@ -172,6 +180,12 @@ This example builds on example 30. Specifically, fictional data for varying Cp/C wave period, Ts, and wave height, Hs, is used to show the difference in power performance for different wave heights. +### 32_specify_turbine_power_curve.py + +This example demonstrates how to generate a turbine dictionary or yaml input file based on +a specified power and thrust curve. The power and thrust curves may be specified as power +and thrust coefficients or as absolute values. + ## Optimization These examples demonstrate use of the optimization routines diff --git a/docs/floris_101.ipynb b/docs/floris_101.ipynb deleted file mode 100644 index f87b52bf7..000000000 --- a/docs/floris_101.ipynb +++ /dev/null @@ -1,952 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "86e53920", - "metadata": {}, - "source": [ - "(background_concepts)=\n", - "# Background and Concepts\n", - "\n", - "FLORIS is a command-line program written in Python. There are two primary packages that make up the software:\n", - "- `floris.simulation`: simulation framework including wake model definitions\n", - "- `floris.tools`: utilities for pre and post processing as well as driving the simulation\n", - "\n", - "\n", - "\n", - "Users of FLORIS will develop a Python script with the following sequence of steps:\n", - "\n", - "1. Load inputs and preprocess data\n", - "2. Run the wind farm wake simulation\n", - "3. Extract data and postprocess results\n", - "\n", - "Generally, users will only interact with `floris.tools` and most often through\n", - "the `FlorisInterface` class. Additionally, `floris.tools` contains functionality\n", - "for comparing results, creating visualizations, and developing optimization cases. \n", - "\n", - "**NOTE `floris.tools` is under active design and development. The API's will change and additional functionality from FLORIS v2 will be included in upcoming releases.**\n", - "\n", - "This notebook steps through the basic ideas and operations of FLORIS while showing\n", - "realistic uses and expected behavior." - ] - }, - { - "cell_type": "markdown", - "id": "699c51dd", - "metadata": {}, - "source": [ - "## Initialize FlorisInterface\n", - "\n", - "The `FlorisInterface` provides functionality to build a wind farm representation and drive\n", - "the simulation. This object is created (instantiated) by passing the path to a FLORIS input\n", - "file as the only argument. After this object is created, it can immediately be used to\n", - "inspect the data." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "602f311c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " x y\n", - " 0.0, 0.0\n", - " 630.0, 0.0\n", - "1260.0, 0.0\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from floris.tools import FlorisInterface\n", - "\n", - "fi = FlorisInterface(\"gch.yaml\")\n", - "x, y = fi.get_turbine_layout()\n", - "\n", - "print(\" x y\")\n", - "for _x, _y in zip(x, y):\n", - " print(f\"{_x:6.1f}, {_y:6.1f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e1eaeb53", - "metadata": {}, - "source": [ - "## Build the model\n", - "\n", - "At this point, FLORIS has been initialized with the data defined in the input file.\n", - "However, it is often simpler to define a basic configuration in the input file as\n", - "a starting point and then make modifications in the Python script. This allows for\n", - "generating data algorithmically or loading data from a data file. Modifications to\n", - "the wind farm representation are handled through the `FlorisInterface.reinitialize()`\n", - "function with keyword arguments. Another way to think of this function is that it\n", - "changes the value of inputs specified in the input file.\n", - "\n", - "Let's change the location of turbines in the wind farm. The code below changes the\n", - "initial 3x1 layout to a 2x2 rectangular layout." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d040b810", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " x y\n", - " 0.0, 0.0\n", - " 0.0, 400.0\n", - " 800.0, 0.0\n", - " 800.0, 400.0\n" - ] - } - ], - "source": [ - "x_2x2 = [0, 0, 800, 800]\n", - "y_2x2 = [0, 400, 0, 400]\n", - "fi.reinitialize(layout_x=x_2x2, layout_y=y_2x2)\n", - "\n", - "x, y = fi.get_turbine_layout()\n", - "\n", - "print(\" x y\")\n", - "for _x, _y in zip(x, y):\n", - " print(f\"{_x:6.1f}, {_y:6.1f}\")" - ] - }, - { - "cell_type": "markdown", - "id": "63f45e11", - "metadata": {}, - "source": [ - "Additionally, we can change the wind speeds and wind directions.\n", - "These are lists of wind speeds and wind directions that will be\n", - "combined so that a wake calculation will happen for every wind\n", - "direction with each speed.\n", - "\n", - "Notice that we can give `FlorisInterface.reinitialize()` multiple keyword arguments at once." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6f9d834a", - "metadata": {}, - "outputs": [], - "source": [ - "# One wind direction and one speed -> one atmospheric condition\n", - "fi.reinitialize(wind_directions=[270.0], wind_speeds=[8.0])\n", - "\n", - "# Two wind directions and one speed -> two atmospheric conditions\n", - "fi.reinitialize(wind_directions=[270.0, 280.0], wind_speeds=[8.0])\n", - "\n", - "# Two wind directions and two speeds -> four atmospheric conditions\n", - "fi.reinitialize(wind_directions=[270.0, 280.0], wind_speeds=[8.0, 9.0])" - ] - }, - { - "cell_type": "markdown", - "id": "da4f3309", - "metadata": {}, - "source": [ - "`FlorisInterface.reinitialize()` creates all of the basic data structures required\n", - "for the simulation but it does not do any aerodynamic calculations. The low level\n", - "data structures have a complex shape that enables faster computations. Specifically,\n", - "most data is structured as a many-dimensional Numpy array with the following dimensions:\n", - "\n", - "```python\n", - "np.array(\n", - " (\n", - " wind directions,\n", - " wind speeds,\n", - " turbines,\n", - " grid-1,\n", - " grid-2\n", - " )\n", - ")\n", - "```\n", - "\n", - "For example, we can see the shape of the data structure for the grid point x-coordinates\n", - "for the all turbines and get the x-coordinates of grid points for the third turbine in\n", - "the first wind direction and first wind speed. We can also plot all the grid points in\n", - "space to get an idea of the overall form of our grid." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "01ea3a98", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensions of grid x-components\n", - "(2, 2, 4, 3, 3)\n", - "\n", - "Turbine 3 grid x-components for first wind direction and first wind speed\n", - "[[800. 800. 800.]\n", - " [800. 800. 800.]\n", - " [800. 800. 800.]]\n" - ] - }, - { - "data": { - "image/png": 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meqIfkoSQWc65/i+Ao0tfqgCUcn60ZOm1oFgzRKf5j91uh0QiQU9PD3g8Xtgy1lBED1RVjzTkBxYfGPPz86ivrw9bgRcpuIUhbn6v1+vR1dXFdq4pFIoVze+j2eng8XheHWu0os9NYej3MzMz2c8oWUQPFeHFY0uRYZhCQsj00pfXAuha+verAP7IMMzjWCzG1QL4ONTx1gTR6d64Xq+HWq2G0WhERUUFioqKwj5GIMKGEsBEuqc9PT2NkZERtmc8GkQTRfjb5/YlB9eSKpk5eqwE9FfRp1ZbAwMDEIlEkEgkfuseiQC1sA6GSK6DYZg/Afg7ADkMw0wB+CGAv2MYpgWLofsYgK8sHbebYZjnAfQAcAH4eqiKO7AGiM7tG9doNJibm8OOHTsiznf9ET0cAUwkuX1fXx/sdju2bt0alQUVRTxuVF8vOofDAZ1Ox0o9AbCWVYncv08E8UQikZd+3WazYWJiAvPz8/j444+9tvJitdvyh1BEd7lcEUVQhJCb/Lz8VJCffxjAw2GfAKuY6Ny9cYfDge7ubggEAuTl5UVMcmA5YcMVwPhW0P3BarWio6MD+fn5qKurYwtxwUBvPqfbg1mjHRliAbLSPi06xXvFFYlEXvk9bVwZHh6G1WpNWH6fjL53KlMVCoWoqKiAxWKBXq9n97a5Gv0I9rYDwu12B41SaEPQasKqJDp3b1yn06G/vx8bNmyAWCzG+Ph4VMfk8/nweDwRmzWGCqM1Gg0GBgZQX1/P5pRA+ET9aMyA/lkzxAIe9jXlI0OSeF93hmEgEokgEolQXFwMj8cDk8kEnU4X9/w+mRJY2mTC1SbQ2o5Op0NfXx8cDoeXRj+ah1ooPQb18ltNWFVE97V4GhkZwfz8PCtjNZvNcLtDpiN+wePxYLFYMDw8jPz8/LAdZQKt6IQQDA0NwWAweE1SBSILvfVmJ9KEPNhdHticbmRIkvORcAnI4/G8qt40v6cmlLQ4RvfvI8m5k9nU4u+6uHZb5eXlrPkkddb1eDzLBmiEQqjQPbWiBwF3b9xms6GzsxM5OTnYunXrMhlrNDCbzdDpdGhpaQmpVefC34rucDjQ0dGBrKwsr+sLB4QQGKxOSAQMGAAXVmWjfXIeeRli5MhE7DmTUSwLdN3+8ntuK6dEImELe6Fy4GSv6KHgz3zSd4AGtznH3zFDEd1kMqVWdH+ge+O0eWRkZGRZKAws3oCRrujcEUgVFRURkRxY/nChbZobNmxAbm5uRMcCgA9GdOiYWkCmmMHn6pTIkYlxRV3kx0kmuD3c3P17bg5MFXu+oXAyiR7OauwLfwM06O7O4OCg3wEaoXJ0KvFdTVhRovuG6v39/XA4HAEnldI8O1xYLBa2SEaH/EUKuqITQjA+Po6ZmRm0tbVF7bgypDYjje9Gx8AUsi1TUKYLWZLQZo+VMJ4IF77791xVW3d3N1wuF5vfUy++1bSih4KvIw13gAbtT6euM4EeYrS5ZTVhxYjO7RunMtaSkhKUlJQElbGGu6L7VtVVKhXrFBIJ6Gyws2fPQiQSYfv27RHfUNwbolrmwhsdKuxsqMClm/Lhcbug0+nYZg+ZTMY6mCQS8XqQ+KrafPN7i8WC0dHRqPL7SJAowYy/ARq0Bfrjjz/2O0Ajkhz99ttvx9NPP62Gd4vqvwHYA8ABYBjAbYQQw1LjSy+A/qVf/5AQ8tVwzrMiROfKWFUqFVQqFRoaGkKK/8NZGQJV1aPN7+12OyYnJ7Fx48aIBDoUH4/q8NGYHvUFMhQSLdJddvzg4MVgGAZOpxNCgfeNZDKZMDk5Ca1WC71en1B1WyJWWt9Q+KOPPmLbOiPN7yNBMttUMzMzoVAoUFBQsGyAxvT0NE6cOBG2WOpLX/oSnn766avg3aL6JoDvEkJcDMP8FMB3Ady/9L1hQkhLpNeeVKJTHzKDwYCsrCz09PRAIBBErAUPBG6o7ltVj4boKpUKExMTyM/Pj4rkHg/Bh6N6KNJ4+J9P+nHztiK0tNSxeZ4vaIU4NzcXaWlpKCsr81K3CQSCZWH+agePx1uW39M9bqvVCplMxhI/llnxyZTAut1uiMVir4o+HaDR0dGBw4cP469//SteffVVPPLII7j88ssDHmvXrl0AoOO+Rgh5g/PlhwBuiPWak0Z0ujdusVgwPj4Ou92O6upq1qsrVoQSwERCdLfbjb6+PrhcLmzcuBELCwtRXROPx6BExuCD3jE0VhWjfkN1WOSkObrv6kjzRW6YT4kfC0mSBW5+Tw0ofbvWAk2WCYVkE93fuXg8HlpaWtDc3IyDBw9i3759UaWLPrgdwHOcrysZhmkHsADgQULIe+EcJOFE5xbcCCGYnp7G/Pw8LrzwwqgUbr4IVwATLtFpVFBYWIiysjJotdqo8llavCtwz+If925FTpYM/ACur+HCN1+kQhAuScIN81dDsS9Yfj82NhbR/n2iiL5gc+HZTxabww5tLUamRBC2YCbW+5thmO9jUc/+h6WXpgGUEUK0DMNsAfAywzCbCSEhV6KEEt1XxtrZ2Yn09HRkZmbGheTBQnVfhEN0uqXCjQqiCfmpFsBoNGJHgLQk2LWGU3X3FYL4NrGEE+avttA/nP37QCOjEyW1fXdQi1MT8wCAoiwJrmnMD0swE2vVnWGYL2HRR+4ysnQzLLnK2Jf+fYphmGEAG7BoTBEUCSM6V8aq1WoxMDCAjRs3Qi6X49SpU1Efl5JArVaHbdYIBCcsHbRoNBqXRQXhaN25sFgsOHv2LPh8PhoaGsL+PS6iuWHPtzAf8L9/Hyi/T4Q5JADkyETgLX0eVNAUiuixbq8xDHMVgO8AuJQQYuG8ngtARwhxMwxThcUW1ZFwjhl3ovuG6kNDQzAajaxMlH4/WjAMg56eHtjt9oimnwYiut1uR0dHB7Kzs7Flyxa/bijhruhU997Q0IDu7u6wficQYg2tfcN8qgzs7e2F0+lkZa+5ublrwosuVH5vNBrB5/ORm5sbcX7vC5XBhlmjHZsLM7CjQo5s6aKmY0Pe4pZZPAUzN910E7Do98ZtUf0uADGAN5fuR7qNtgvAPzMM4wTgAfBVQojO74F9EFeic2WsVqsVnZ2dyM/P9yJQLOGVxWKByWRCTk4O6uvrIzqWP7ENvfE3btzI9jr7IpzQnRCC4eFh6PV6r4dPtKqweAtmGM4whrKyMrjdbgwMDMBiseD06dPsWCKlUrlmqvm++X17ezsUCgUMBkPE+T0XaqMdP/6fIdicbmwrl+Orl5RjY7736hxOjh6uT9yf/vQn/OlPfyr0edlviyoh5AiAI2Ed2AdxIzohBHa7HYQQzMzMYGxsDPX19RFLTgOBVtUzMjJQXFwclWsJJSxt01Sr1WzDTCCEIp3T6URHRwcyMjK8dO/091Yjafh8PtuLnp+fz3rRTU5Owmg0Ij09nQ3z49HWmQwQQqBUKtlRSzS/n56eRn9/f9D8ngu9xQmHywOxgIdz8/496cMJ3VebXXfciE73hnt7e+HxeMK2eAoF36p6b29vVKE/VdU5nU50dXVBIpFg27ZtIZ/0wVZ0o9GIzs5OVFdXszcYRSyrcrIlsL5edDTM7+vrg9PpRFZWFpsLr9Yw37fq7jtjzZ8+35/rbG1eOq6sz8Wwxowb2/xrJ0JV+M/77rWuri7k5OREteL6A62qFxQUsFX1aBVuPN6it/rJkydRVVUV9v59oGLcuXPnMDY2hqamJr+Fl9WsVwcC1wD8hfnUcpqGxOFaTifz/YequvsOkOTOkXtrzIazWgaX1ihw68WVuKHVN5L2RqhIze12x2WRiyfiejWtra1hfbi0wBXsqRhIABNNBxuwSEyLxYKLLroo4mmqvkMWqYFBsKglVLXebrfDYDBAJM3AGZURaUI+mksyIeTzVrxNlQvf0UvUkmpqaipkmJ/s1CUCjzY2v88tLMETnZ3IkhH874AOteJ5yEQ8NoKh5pPhnme1PtzjSvRwb1BKVn9EDyWAibSDze12o6enB4QQSKXSiEMqLmFpc0teXl5QD3kg+N/CYDCgu7sbMpkMpyYGMGvjQ5wmRYawCBsKs/3+zmqBryVVoDBfLpeznXjJQLTnkQh5qM1Lx5DGgk3FCuzcUQu3y+llPikWi9mHWTj6j2S+73ARd6KHA0p031ZUf6G6LyLpYDObzejo6GC74k6cCDq1xi/oik7DvE2bNrF71eH8ni/oQIeWlpbFcbs5FnwwqIHVYsbs1ATmp4aQlpbGCo1WWwjIRagwn2EY2O12LCwsxDxZJlHgMQy+c0U1VAYbCrMk4PMY8H3MJ2l+Pzo6CrPZzDavBNIkRLKqB+heU2BR9lqBRQfYA4QQPbP4B3wCwOcBWAB8iRByOpzzrMhd5C/8DtesMdzQnR6voaGB7YuOBgzDwGq1YmBgIGSF3vf3uB+4x+NBb28vbE4XMsvqMGvxoCiDoCZHCrmkEAI+D9lSITuYQqVS4cyZMxHlxJEgESGmb5hvNpvR2dkZVpi/khDyeahQBl6pufm92+3GyZMn4XA4vObE0yjG3wIWDAG61x4A8BYh5BGGYR5Y+vp+AJ/DokimFsAOLE5z2RHOeVac6JGaNYYqxnk8HnaPOJCBRbhwuVzo7u6G2+0Oq0LPBZfodrudDfmtyMSHowZ4iAeXbVCgWJ6G3IxPVwUqZFlYWEBdXR2bE09OTnop3JRKZcxurYleYYVCISQSCerr60OG+bFGLsn0qReJRCgvL2elx3SqTFdXF77//e/D7Xbjb3/7G3bs2BHy/vPXvYbFQYp/t/TvZwC8g0Wi7wPwuyVJ7IcMw8gZ70EPAbGioXs4oXqg3/UHm82Gjo4O5OTkYOPGjTHdyDTsLy0thdlsjlheSYlO83Ea8mvG9ABDAAKEc2/65sQmkwlarRZdXV3weDzsFlEgf7OVBLcYF89q/krCdw+dG8VUV1fjF7/4Bb797W/j97//PV544QU88cQT0Zwmn0PeGQB077YYwCTn5+iAxeQSPVzw+XzMzc1BrVaHpVU32VzQmh0oU6SBz+fD4XAs+xmtVou+vr6wc+hgoM0tNOyfmJiI+BgMw2BmZgazs7Mo27AZ4vTFLbjmkixIhDyI+AwKM/0/7QMV8riNLBUVFXC5XF7+ZtTUIdyiUaIRrOoeSzU/kvPEG6HEMhKJBJWVlfjVr34Vl/MRQgjDMDGHK0knOi1sEULCCtUNFie+fbgTBqsTV23Ox54aideKTgjByMgItFptRDm0P1Bt/vz8fEQ6el/QIYFOpxNpxZvwWp8eIsEC9jcXQi4VorE4i236iQUCgQC5ubnIzc31Mm0cHByE3W5nRzD7C42TQY5IzhGomt/f3w+73e7Vguv7XlZDLzpFnMQyszQkZximEIB66fWoBiwCSQ7daahOVUvhEGlSb4XB6oJUxMdHo3rs31jC5ui09VUmk2Hr1q1hfdiBbj56rIyMDL/NLeGC5uNCoRC1tbX4UGWDRMiH1enGgs0J+VKDRKxtqv5+x3foIm1bHR0dZbvbaNtqMhCL1p8b5nPfi78wP5lET9LwhlcB3ArgkaX/v8J5/W6GYZ7FYhFuPpz8HEjiis6tqhuNxrC3yDbkpaOxOBN9M0bcdmEJu702Pz+Prq4u1NTULJOfBgIt5Pl+UAsLC+jq6vIrZY0E9Jo2btyI2dnFqbdbyuR4d1CL0uw0FGSGX7GPFVwyAMvbVoHF1SkzMzNhI5bjFTX4vhffMF8ikcDhcMBqtUbtzhsuwvF0j2RFD9C99giA5xmGuQPAOIADSz9+HItba0NY3F67LdzzJJzo/qrqFosFdrvd788Pqk34c8cMtpbLsas2B2IhHw9ds4n9vl6vZ6ucra2tEeWiVGzD/aCoL1xzc3NMIRc9Dr0mtVoNj8eDnCwxrmuN3G8u3lVk37ZVqu7jFvWUSqVfJVi0SFR64Bvm0xnx1KAxWJgfKTRGO3QWJ6pypBDyeXE3nQjQvQYAl/m+sFRt/3rYB+cgoUQPVFUPVjn/52N9WLC68O7gHGrzZCjM+nQVdLlcGBkZgd1ux8UXXxxxgwV3a44rZd22bVvUNwR9kNH+eHqc1dzUwjCLs9fkcjmUSiVcrkXLaaoEk0gkbJgfywqZLGdWiUQCmUyGzZs3s/URbphPH2KRVvPnTA78+19GYXO6cUFFNg5tLUqKu0wikLAcPZgAJhjR04R86MxOSIQ8CDgeayaTie1v5/P5UXVR0bCfSlnp9NNoVx2Hw4GzZ89CqVQu2x5c7U0tXNAptXl5eSCEwGKxQKfTLVshI+1eS+aUFnoeSmw65Ye2q6pUKiwsLLDtueE8xAxWJ+xON6QiPlQGK4DwinFUUbeaEPcVPRwBDJfoDpcHPAYQ8Bf/eP+8pw7vDc2hvjCTFZJMT09jdHQUDQ0NEIlEMBgMUV0bj8djjQfr6urYnC8c+N60NB8PNJppNa/ooc5Np5GWlpay9RCtVovR0VHWi06pVCI9PT1kg0eyiB7oAeRrR+X7EOO24PpGdZVKKS6pUWJcZ8H+5gL2XMGiv9XYogrEmehOpxMff/xxSAEMJfonY3r88/F+pIv4eOyGBhTL01CQJcGNW0oAfPrQsNls2LZtG4RCIRwOR1Tda/RDHh8fj3gbztdE4ty5cxgfH0dLS0vADzUUWbVaLSYmJtjwOdFFpGjhu99ts9nYsJg6qdAw31cFthIrejD4PsS4Yf74+Dh4PB5GrRJ8ovbgoppcXNOQh+tavNuZqad7IKyL0F0oFKKhoSHkG6VE/3PnzGIxxeLEyXEDiuWf3uxWqxUdHR3Iy8sLO78PBJfLxc79rq+vj3ivnZvbDwwMsNFKsCd7IKJTG+jZ2VlUVVVhYWEBAwMDcDgcbIgslUoTvqJHS0KJRIKioiIUFRWBEOI1ghgAq9TLzMxc8ZHJoeAb5lttdvzqhW6IGBeePzGIbPsMKgtzvML8RBtDJgpxz9HDeZOUrJdvysWpCQNkYj6aizPZ78/NzaG/v9/vRNVIjSeolLWsrCxq91Mejwe73Y6enh4oFIqw5LX++tE9Hg96enoAAFu3bmUbImiIbDAYoNVqMTQ0BIfDgampqVWjcvMHhmGQlZWFrKwsVFZWspNIqX0TnSFns9kS2sQSr6KfRCxCfUk2hjRm1Cjk2FxbCNOCwSvMt9lsQZuk1kXoDoSXXwoEArhcLlxSm4PG4iyIBDxIRXwvk0XqGuvv+OGCSkMbGxuRmZkJk8kUlTuN2+1Ge3s7Nm7cGPaoZN+/g91ux5kzZ1BQUOB3sivXrpmKdwCwKjca4idiBlu8wJ1ESgiBSqWCRqNhm1gSNUcuFqK7PAR9MyZkSgQoU6Thq5eUY0JnRVGWGOliAbKzMrzC/IGBAQwPD2NiYsKv+WS0oTvDMBvhPZGlCsAPAMgB/D0AzdLr3yOEHI/0+CumdaeEo0oxh8OBjo4OZGVleZksRoNAUtZobKimp6dhMpnQ1tYWUfGO249OC3dcHX6ohyGfz2f76LkFsZGREQiFQvahsJpXe7FYDLlcjsrKymUDJujccYVCEbKoFwqxEP3PHTN4vWcOAj6D+y6vQqVSitq85SsyDfNlMhkqKiogFAq9hkukpaVheHg4amNIQkg/gBYAYBiGj0Vp60tYFMX8jBDyWFRvcAkrQnTf1Y52eAWqYEcC7gPDV8oaCdFpu6vVakV2dnbEoSd9j3THIBJxj+/fx7cgRjXtQ0NDbCipVCoj2v5Kttbdd8AELerR0cqZmZnse4i0tTgWok8v2CHgMXC6PdCaHagM0pcOfJqj+6vmv/fee+jv78fnPvc5XHLJJXjssceiLbJehsWpqePx+oxWJHSnIIRgYmIC09PTEavc/GFhYQGdnZ2ora31u5cZLtHpw0Iul6OlpQUdHR1RhfwzMzNgGCZujrgUXCMEX027QCDwWu1Xst0z2MOEW9TzeDwwGo3QarWYnFzswqQPtszMzJDvIRaiX9tcALvrHHLSRWgqygz58/6KcbSa/7WvfQ3PPfcc/va3v+H06dOx1CUOAfgT5+u7GYa5BYujl75NCNFHesAV8ykihLDNH9u3b4+5mEIlqMG2vMIhutFoREdHh9fDItJ9bZfLBZVKBbFYjLa2tojJFsn5fHXgNpuNDfGtVmvQfeJEI9yogcfjsUU9YHGbVqfT4dy5c+jr62NbVpVKpd+6Tai97WAozJLgm7urwv75UIIZj8eDtLQ07Ny5M6rrYRhGBGAvFqe1AIsuMv8CgCz9/9+xOGE1IqwI0Y1GI8xmMyorK6OaO851kaUWTS6XK+SWVyii0zC7ubnZq6ASSchPZ69lZWWFtRr5QyyrsEQi8VrtaW4/NjbGrvYKhSIpgpxo0wOhUOgVFtOWVe7UWKVSiaysLLbek8zutUDnitPf9HMAThNCZpeOOUu/wTDM/wVwNJqDJiR0Dwbqhy6VSlFYGNw/OxAo8agENT8/H+Xl5SHPHci0ghCCgYEBmM1mVpjje75wPkRqftHQ0BBRh54/xOOm8d0nttvtrMLNYDDAZrPB5XIlbLWPRx3AnzONXq/H3NwchoaG2EKrQCBISt0hlMNrHBxgbwInbPexiroWQFc0B03aik5XXqfTie3bt+PUqVNRG91Th5rh4eGIpKz+Vman08muwK2trX4/pECOrhS01jAzM8NuC5rNZrhcrsjeGOd8iYBYLGbz4v7+fqSnp2NhYYFVhdHcPtYqOEUiVlo+n4+cnBx2Vp7VakVfXx9mZ2dx7tw5ZGZmsqnMSqQqsTygGYZJB3AFgK9wXn6UYZgWLIbuYz7fCxtJ+UvQLrbCwkKUlZWBYRgIBIKwie72EHw4qgMDYHtFNmw2G0ZHRyOWsvpaRQcbqeT7e8FGLlPfeK6BZKx69USH1nSlLClZlBvTfnUqbaVV8FgIk4wVNi0tDTKZDDk5OcjKysLCwgIrL2YYhs3t4+VDF+xzcTgcMY2kJoSYASh9Xrs56gNykPDQnYpWYpm48kavGk+/Pw4AuLyEYJPMg6amppikrDMzMxgZGQk4UomLQKTlurv6pg5roamFe73cfnVuFXxiYoIt+EU6bTWZWncejwcejwe5XM7eZ/586OjDKxEz4lerKg5I4IpOCMHg4CAWFhYCTlwJexCDzQW3xwOLxQxBWgEUivSoiEBX9IGBARiNRr/5eKDf813R6VZeoJHLochKHWIzMjJWZEhDsGvjVsGrqqrgcDhY0ptMpqCNLL7nSCbRfeHPQZdb1KPKNrlcHlaKESoVidRdJplIyB1mt9vR0dGB7OzsgP5rfD4/7Bx2Sz6DjgwbijeW4tDF1Rgb6o9qX9vj8UCtVqO4uDiibS9f0tJoIFT3WqBrdLlc6OjoACEENpttmdJttVkdi0QiL3caGh7TRpZA4fFKE50LroNueXk5XC4XDAYDNBoNhoaG2LFLtJOQYRZFNBqTAznpIogE8XeXSSbiTvSFhQVWF+5vpaMItKITQvDbExM4NWHAoa3FyPPoYDQa8U8HLmZXj2g62EwmE3p7eyGVSlFbWxvR79IVnUpraZQSbDULtKJbrVacOXMGZWVlbIhJ942p0k0ul8PlcoW8sVYC3EYWAMsGTGRkZLCEWU1E94VAIFhW1KMNRTabDZmZmXhjkmDaDJQppPj7nWVhGUOumxVdIpGgra0tpPQvEFnHtBa81j0LiYDBvx/vxKOfK1m2+kaqWZ+dncXw8DA2bNiAmZmZ8N8M53xOpxNnzpyBVCoNKxrwR3Qq9a2rq2PdSz0eD9sMQguC8/PzmJ2dxalTp1hbJ6VSGfcOsHiQ0Dc8prl9R0cH+9ASi8UJHcoQj+p+Wloa21vg8Xig0ekxdGoEaXDgrF6PvgI38rODv4d1taKLxeKw/uiBiK5MF0HCBzS6BbSWK/yuvuGu6L4rsNPpxLlz58J7Ixw4nU6oVCrU1taGLfDxJfr09DTGxsbQ3NwMsVjM7jwAYKMFSvy0tDSIRCK0tbXBZrNBr9ezHWDU/yzWySyJKPYxzKfjiCsrKzEwMAAej8cWw2QyGZvbx9N5Nt7beDweD/k5Sly/HXh3SIddxTIoM8DaUXV3d/sdixVLjs4wzBgAIwA3ABchZCsTYNhiNMdPumCGgpKVEIIpgw3KdBGkIj4WtLP4YrUL8uIGtJT7D/3DmajqdDrR0dGBjIwMdgV2u90R5/a0altQUBCRio+KbLgPm7a2NvB4vGWiCnqT8vl8mEwm9PT0oLq6GsDig5O7YhoMBnYnQyqVJrSKHCu48lx/46QoWaJVEFLEi+jzVife6NUgQyLA5ZtycWmtEpfWfrrblZaWhtnZWRQVFS0biyWRSDA/Px/riv4ZQsgc5+tAwxYjxopp3fl8PpxOJ3713hje6FEjWyrElxsEkPAJLt+5I2guFGpGuslkQkdHB6qqqlBQ8KkVUKQhP224qaysjGqggtvtxtmzZyGRSNDc3MzmrIFuaq1Wy46CojcMd7V3u93ejihWK/R6Pbq7u+NKnHiBm6Nzi2EVFRWsSQVXz05TlEhX+3CtpELh9R4NTo4Z4AZQkClBS4l3kwvVffgbi/X666/j0UcfRUZGBrKysnDdddd53XtRItCwxYixokS32Wz4YMSANCGDSY0BJhRje9OGsKSsgXzh1Wo1hoaG0NjYuKwvOFyiUxWf2+3G1q1boVarYbVaw39zWCxSqdVq1NbWstXqYCSfnJzEzMwM2travG507movFApZwhNCkJaWBolEwh6fup329fWxIpJAW2ArPZLJ16TC3/DISHzm47GiZ0oEcIOAzzBIEy4/nr/IgY7Fuvnmm2EwGNhFiEaBEYAAeINZnLP2K0LIfyPwsMWIsaJEd7lcuGZTFp56fxytZUpc0lQbdreTL2GpO43BYMDWrVv9rgrhEJ3q53NyclBRUQGGYcLWulPMz8+jr68PmZmZbMgdrBGCesa1tbWFrLJTYQjw6WpPUxJuvzd1O6VbYPR7kQheYkW4DxPf1Z76zFNLqlDda7HUG+wuDxwuz1K4noOCTDGkIj42+DGfCMcvrrm5GTfeeGM0l7KTEKJiGCYPwJsMw/Rxv0lIbMMWVzRH1+v1qODx8PLXLoqoQd+3GEf3pdPT04POTQtFWNqi6muAEUrrzgXdY6+vr0d3dzcmJyeRm5vrt9fe5XKhs7MTmZmZ2LAhdCTj7/0AYG8+utp7PB6vOWw0vKSCl8zMTNhstpiabsJBtKaNvj7zZrMZWq0WPT09bPoSj6kyWrMD//HOGIw2F27aWoRt5XK0lgb2gwtnHFO0OTohRLX0fzXDMC8B2I7AwxYjRkJW9FCqMJfLheHhYTidTlx88cURf1jcYpzZbMbZs2dRWVkZdTcc8OkWnG+LKj1fKKKTpamuer2eXZnb2tqg1WrZSS4KhQI5OTmQy+WsuUVpaWlM1+17nf5We+7UVYZhYDQaMTAwgP7+fohEooSJdeKRO3O716jQRa/Xs1Nl0tLS4HQ6YbfbIy5IjuusMFicSBfzcXLcgG3l8qA/73a7g2ononWAXWpm4RFCjEv//iyAf0bgYYsRI+mhOy2UFRYWYn5+PqonMs2DfM0fowE35A8kggn14HK73ejq6oJIJEJLSwsbstJVlbq86nQ6zM7OoqenB06nE6WlpTHPcg+EYKu9TCaDRCJBeXk5xGLxMqMK+jCKRaxzYlSP/3jPgMs3S3DHzvjtLfuOirZYLGhvb/eStYa7/VidI0Vehgh6ixO7akJ3QLrd7qBahhgEM/kAXlp6KAoA/JEQ8jrDMCfhf9hixEgq0WlY29jYCKFQCJ1OF9VxeDweDAYDLBZLTHPMqd+7RCIJGfIHWtGpu2thYSHrde6v6Mbn85Gbm8uaQWzatAlGoxFnz54FAFallagcmq72tAdBIBBAJpOBEMKaPABgfdqHh4chFovZ1T5S77PvvNQLi92FsRPT+MymAlTlxN/Eklo4iUQitLa2srJWugCEEhtlpQnx3Str4PYQCPnhad1DKeOiNIYcAdDs53Ut/AxbjAZJCd09Hg8GBwdhMpnYVdPpdEaVI7pcLvT398PtdmPLli1R52hcKWpxcXHQnw2U23MbW7KyskIW3cbGxqDX67FlyxYIhULk5OSgsrKSbRoZHR2F2WxGVlYWcnNzoVAo4m6L3N3dDYlEgsbGRrb2wBXrcAUvdrsder3ea8AEtZwO9XeXpwlhdbjA4wEyceJkvNzPhStrpau9Tqfzspv2vX4ew4DHD+/BmsgcPdFI+IpOq9jZ2dle0tFo9Op0GENRURHm5uaiJrler0dPT4/fAZD+4K8YR3P6xsZGtgki0EpMt+t4PB5aWlqWXTe3aYSu+NRYQyQSITc3Fzk5OTGNbaICory8PJSWlrKvBwvxfZ1O5+fn2SaQtLQ0drX0lxv/+otNeOYvHbiitQp5GYkT8wR6uNLVnjtDjutMI5FIvJpYwkE4VfdoVvRkIKFEDzaIMNItK41Gg4GBATQ2NkIikUCtjq4A6XA40N/fH5FpBTd0J4RgdHQUWq2WLboFIzkdxpCbm4vS0tKQYTnX/qm2thZWqxUajYZ156EFvUgksHR6bGVlZchJn8G277i93lSa29PTA4fTiSOjPPRo3fjG7ipcvikX+ZliXFkpwqaCxK5w4arifJ1pLBYLtFrtsnFYwWoToYwhrVbrqp2hlzCiT05OYmpqKmYbZy6xNjW2QpouAQMScTRA56HTkD8S73Caing8HnR1dUEgEHgV3QKR12w2sw420frVp6WloaysjPVL02q1mJ6e9hLF5OTkBHw/RqMRXV1dqKurCyt64SLQak8IYYU6BQUFOKtawF/f6YPd5cGDr/ai0K2GUqmE2+1O+J59tJV930IpHYfFrU34jsMKlaNHu52YDCSE6ENDQzCZTNi+fXtMOSYtlonFYkwKivDky30oU6Th/s/WRiRl5c4xj8YPjcfjweVy4eTJkygoKEBxcTH7oQY6lk6nQ39/PxoaGuIWzvH5/GVqMo1GgzNnzgBYLOjl5uay75GOB25qaopL+2Sg1b4gUwIej4FYyEOVcnHvXq/XszssNMRPRAdbKPKFA9/hErRl1XcclsvlCniuZLXkRouEEL28vDwoCcIBtU2mxbJfPt8BhVSICZ0V5+b9y1/9gfrC1dTUIC8vD1qtNuLGFrPZDIPBgJaWFsjl8pAruUqlwrlz59DW1pawZhOumoy6wMzNzWFkZARmsxkikQg2my0ugzH8gbvaV+SJ8IfbWtFzbgEXV8khFfEhlUoxNzfH7i7QfvV4eNFxES+dOxfcllXuOCzauRZoHFY0ZJ+cnERZWdnbWNxiIwD+mxDyBMMwDyEOM9coEkJ0kUgUdmjt749D8/GGhgbW4ODyTbl46cw0KpVSFMslmPV3MB9Q3TvXFy7Sxha6VZOens5eS7DK+tDQECwWS1hy1nhCJBKhqKgIhYWFGB0dhUajgVKpREdHB8RiMbvax6OnfUpvxd3PdcJDgP882IByhRS1eRmozVuMXKjWm44uoukFFetQlV48nGcT7enOHYdlMBiwYcMG6PX6ZeOwov27Lj3svk0IOc0wTAaAUwzDvLn07Z+RGGeuseeJx0F8EWmrKn2y03xco9FAVroJw/MEzRkEfB6DPU2FuGxTHiQCHni84MfnqtR8RTDhEp1uh83NzaG1tRWnT59GV1cXWwH3zYmpaCY9PR1NTU0rEsYRQtDX17fMkdZisWBubo4V6igUCuTm5iIrKyuq63zqgwkMqs0AgP9+bxwP76vzuoaxsTEYjUYv+2yuWEcmk6G0tJTd96bz12iIHOkMuWTmxTS3547Dmp2dxa233or5+Xn87Gc/w9VXX40NGzaEdbylhqTTALCkjOsFEHy/NwqsWFML4E10mo+LRCKICjfgsbeG4fEAB7YWY1/zokRUKgr94bvdbnR2drLjkHxvgnCITvebGYZBS0sLAGDHjh0wm83QaDRob28Hj8djV0k+n4+Ojg6UlJRENXkmHqDvm+6BcwkslUrZgh5tGFGpVOjt7UVGRgZycnKgVCrDLlBuLsyAWMgDA6Ch6NP6A23QcblcaGxs9Prbc3N7WtTztXMyGo3sDLlwJ8Ymc0qLL7j99seOHcMtt9wCmUyG48ePh010LhiGqQDQCuAjABcjDjPXKFYF0Wk+XlpaipKSEvylTwOnm0DAYzC7EH4+brVacfbsWTa/8odQRHc4HDhz5gzy8vJYIQ3Nx7k5sd1ux9zcHHp7ezE/P8+GxStx49FiY3FxccgHjW/DiNFohEajYUNpSrxgofQNbUUoV6TBQ4DtFXIAn/rbi8Vi1NfXB1UZ8ng8CASCZb323A42h8PhFSJTaauvWGclic6FyWSCUqnE3//930f1+wzDyAAcAfANQsgCwzBxmblGseKhO7UR5ubjF1YpMKQxw2hz4doW/w0fVMRCP2S6p1tfX88aMwQ6ZyCi0ypxTU0N64oSqOgmFoshEonYmW92ux2zs7Po7++HTCZDbm5uRKtktKDDMWpqaoKacfoD1/qpurqaHdlE53xnZ2cjJyfHbyi9reLTvzGNJuRyOSoqKsI+f7Bee9qvThtxqIiIil3oap8soofSfMRiDMkwjBCLJP8DIeTFpfPFZeYaxYpOUzWbzTCZTODl1+K3n2hweR2DzUWZSBPx8eVLKoL+Pl2ZqSfZ1NRUWKaUgVpONRoNO2iChorBim4TExOYm5vz2pOnjRbcVZIKNQK1qsaC+fl5VuEXbVMPF9yRTTT/pEo4iUTCrvbcwhNV3NFtx1gQTKxDnWcZhmHFOr29vbDZbBCJRDAYDDG3rQZDqOp+tMaQSw+QpwD0EkIep68zcZq5RrEiRKeFK4/Hg6Lyajz4xgR4YHBqYh5P3dIaVoMBtaKie53btm0Lq4DjG7oTQjA+Pg61Wo3W1lYIBIKQclZa8GptbV12Y/mukjabDXNzc16tqrQQFstNqdFoWG/5RKixuPknsHgjz83Nobu7Gy6Xi+0QGxkZCUtxF835Af9iHeqjl5+fD41GA4PBgJmZGdakIlpLqmAIp6ElGqK///77AHAzgE6GYc4svfw9ADcxcZi5RpH00J02k5SWlkIqlYLHAGIBDyabC9npIvAiGKrQ0dGBnJwcbNq0KaJ0gRKdOzettbWVPW6gYzmdTnR2dkKhUIQ1vRVYtL/m7slS55S+vr6oCmEAMDU1xdpOJTo1oKC6cdoTPj09ja6uLgiFQqjVahBCQk5uiQX+Vntatc/KykJeXh4YhmF99LgGlDk5OTGLdcIZ3hBN6L5z504QQvxdWNR75v6Q1BWdjhWmzSSjo6MQMh788OpN6JleQHOJHPwQW2fAYi6t1+tRW1uL8vLyiK7Bd+RyTk4OW7gLRnKLxYLOzs6YVi/aqkpD/IWFBczNzbEhPv1eoBWa9s6bzWa0trau2HAHm83GpkqZmZns+6BTWbmpSqJabunfgj5IuVbZaWlpbPqh1+tZu+lwR0n5w1ruXAOSRHQaHs/OzrJjhYFPq+7lSinKleHlr1QEQyuwkYLH48FisWBkZATV1dVeE0UC3ZQGgwG9vb1xy4UB74knNMTXaDTo6+uDw+FYttdNow+hULhi+/TAp38LrqyW+z7obsTQ0BCsVivkcjlyc3ORnZ0dt/yZ/i0kEgmqq6vBMIxXiE//Az4dF8UwDLtATE5OsqlJqB0GilANLat5eAOQBKK73W50d3eDz+d7iTiAT/PscMBtbtm6dSuGhoaimr9msVigVqvZwl0owcX09DQmJyfR2toa90kpXEgkEpSWlrJNFlqtlt3rlslkMJlMKCgoQGVlZcKuIRQogYP9LcRiMYqLi1lBCW0NpUYQVHAUrTSYNhZlZGT4/Vv4C/Ep8dPT01kfPdq2SsdEU4Vbdna2X2luqBzdYrGw9YzViITm6HRfu7i42KsHmoJaPocCLd4JhULWbCKafvbx8XHMzc2hpKQkZA85DQ1NJhPa2tqSOvGU27xis9lw+vRppKenQ61WQ6/Xs6FxMlsiZ2ZmMDExscyOOhD6Z00YVJvxdxuU2LgUNVGFXldXF9xuN5RKJXJycsL2ofd4POzwznBSNn8FPVrU47atMgyDhYUFlvgCgWCZj16icvRkIWF3r06nQ29vb9B97XDIarPZcObMmWUPi0g067RS7nK5UFtbi5GREdZ7zN/KRKMQOnhhpcJkk8nEtphyhzZQoY7T6WTJEq2cNRxMTk6yUVA4D7wJnRVf/sNZuNwEf+6cwa++0OxlBFFeXs4OlpycnITRaERmZiZbmPR3DjoMg/b1R4NwxDrl5eWsWGd4eJidH0d9BwKBDphcrUgI0QkhUKlUIc0dQhGdDiX097AId0WnwxGVSiXKyspACIFMJmO3itxuN7tCymQy1p21sLAwoLouGdDr9WybKzf3S0tLY0N8l8vlFeJnZmayFlTxiEBoukR16+Hm2HMmOzyEgIBApfcfsQmFQi/3moWFBWg0GoyPj3uttunp6XC5XDh79mxc9uopIhHrLCwsYHJyEmazGUaj0a+PXrQOsFwwDHMVgCcA8AH8mhDySEwH5CBhoXtTU1NINVEwsqpUKjZU9BeihjN/jVpBV1VVsT5i1J2Var+dTifm5ubYG9rpdKKiomLFNOvAok3V+Pg4Wlpagj4oBQKBX6snqhWnVfxoagtc3Xqkxb+W0ixc31qI9skF/MPfha4pcAuTAFjtweDgIKxWK5xOJ2u1lSgEE+tkZmayElyFQsE+hOnQS6fTGdOARQBgGIYP4BcArgAwBeAkwzCvEkJ64vH+mBBkjHoyhNPpDBlam0wm1kudPSEh6O/vh81mQ0NDQ8CVaXJyEoQQlJWV+f0+3cpraGgIqXQDwN5YpaWlMBqNMBgMrJQ1JycnaTn6+Pg4tFotmpqaYjontaCam5uDy+Viq/jh5MNc3XpNTU3InyeE4N1BLawuDy7flANBnKrrTqcT7e3tUCgUrJ+7VCplV/tkDZZcWFhAV1cX6uvrvR6a9OH6y1/+Er///e/R3NyMAwcO4Atf+EKkD1eGYZgLATxECLly6YXvLp3jJ/F4Dyva1CIQCLxWZafTyRpJbty4MegNFmz+Gh2O2NraCqFQGLToBiw+NGZnZ7Flyxa20ESlrGq1GuPj4zGvkKFAV1Cn0+nXQDJScC2oaIhP82Hq3a5UKpcVmNxuN1vwCle3/lq3Gv/y2gBAgJE5C762K7zfCwbaXFRZWcnacFHZ9NzcHDo7O9kxVLm5uQmbv242m9Hd3c16Gvjz0bv//vvx/vvv43vf+x5OnjwZrb6hGMAk5+spADvi8iawSrrXgE8bSqqrq1mP8WDwV4zzeDzo7++Hw+Fge6FDyVlpeOrb0sqVstbU1LArpL+8PtYbjBb/pFJpVKOZQsE3xDcYDKwbjVgsZqMWPp+Ps2fPorCwMKJc+Ny8DS73Uk5uiGwYpT9Qr/yamhqvARcMZ2oLnchKm6JoQS+eNQrqOtzY2OhlXAJ4V/I/+OADjIyMoK6uDrt27Yr5vIlAwogezs1KBy1SR5mmpqawK5e++T2NBuRyOWpra0OKYOjcs6ysrJDRA+C9QnLzerPZjOzsbOTl5YXld+4Let0FBQVJKf4xDOPlMmuxWKDRaNDZ2Qmj0cjKRSOxRTqwpQj9syZYHG7cfWls+/x0l2Xjxo1BuxCBxYIed3Y87XCjNQoa4kfTTERJ7lsM9cXp06fxne98Bx9++GHUBqBLUAHgbieULL0WFyQsR3e5XCGLZYQQvPPOO5DJZGhubo6oCUGr1UKtVqOurg4Wi8UrzAtFcqvVio6ODpSXl8c8w5qKQtRqdcR5Pb2OqqqqWG+SmED1DlVVVfB4PNBoNDCZTAEHSdicbrzZp0FZdhqaSwIPJYwUtOV206ZNUakeuaAFPY1Gs2zuXaiHMfVHCGXseebMGdx111148cUXUV1dHcvlMgzDCAAMYHEyiwrASQBfIIR0x3Jg9gQrRXQarmo0GnzmM5+JeCU0GAxQqVQoLCxEb29v2EU32tpZX1/PVnnjBW6L6tzcXNC8nhoNJuI6IoHJZEJnZ+cyeS8dJKHRaKDT6dgQPzc3F9/98yD+NqwDAwZP3dyMhqLYZcF0BY2nzJiCquBop1t6ejq72vsuLvRhs3nz5qAk7+rqwp133onDhw9H5SbjAwYAGIb5PICfY3F77TeEkIdjPTB7gpUgOh0oUFhYiKmpKVx00UURH5/6lTMMg8bGRohEopBFt5mZGYyPj6OpqSkpqjKa12s0GlYJlpeXB7vdzppWJsKhNVwYDAb09fWhsbEx5NYQLYLNzc3h+++aMG3xQMzn45/3bMRVm0PXVIKBPmy4uXCiQAt69GEMgBUdCQQCdHR0oL6+PujDpre3F7fddhueffZZ1NfXx+OyEq7IShjR3W43XC7Xstfp9Ja6ujooFAp88MEHEROdEIKuri7Mzc3h4osvDll0o8KP+fl5NDY2JlXOSkHzejqjvLCwEPn5+XFt9ogEVLceaq/eH06P6/Cvx/tRkEZwUy2Qo8hmG1cirTjTyCZe3vORgn4us7Oz0Gq1UCqVKCoq8rsjAQADAwO45ZZb8Ic//AGNjY3xuoyEEz2pd/y5c+cwPj6+zGs8ksIPVUlJpVLWo42aRfgD3RMWCARobm5eMX8xgUAAq9XKmlYuLCxArVZjYGAg6fv1MzMzmJycDFu37ou2cgVevOtCAPDrRBNu48r8/Dx6e3vR3Ny8YpGNUChEdnY2xsfHsWXLFhBClhX0aF/B6OgobrnlFjzzzDPxJHlSkJQVne4RWyyWZSvqhx9+GLY7DC2SlJeXIycnB8PDw9BqtUhPT/drw0zlrHl5eQGFNckA1dozDLPMJCOSvD4eoLr15ubmuD9UaOMKfS+EEHaf23cbkqrLmpubV3ReGa3y+ysA0r6C4eFh3HvvvXC5XHjggQdw++23x9tgY+2G7h6PB06nk/UUo/vRvivvyZMnw6q4cyeg0rne1IDAZDJBrVZ7EUUmk6G/vz8q08R4ggpQqHFiqMglUF4f6349V7fua8WcKNBx0BqNht2GpFLkkZERNDc3J7T1NxSCkZwLlUqFgwcP4tChQxgdHUVRURH+6Z/+KZ6XsraJbjAY2G2bQNtY7e3t2LhxY9DQTaVSYXJyMqyim9VqxdjYGKanpyGVSlFQUMDOI0s27HY76/cejU6b5o9cokRj4kAjKrfbjbq6uhXpxqPbkBMTE9DpdFAoFMjPz/db+U4G7HY7e+8F26+fmZnBDTfcgJ/97Ge49NJLE3U5azdHNxqNOHPmDBobG4NWMIM1tnBDfjpbPVRlXavVwmQysUU6Ksax2+1svhVu/3MsoJNUa2trvdRdkUAoFHrNTaf79ZHk9VzdeiJUd+GCNiE5nU5ccsklcDgc0Gg0OHv2LIDlAyITiXBJrlarceONN+Lf/u3fEknypCChKzod9hcM3d3dKC4uXhY6uVwudHR0QCaTsUKOYIMbCSEYHByEzWbD5s2bl+X8VO+tVqthMpliUrOFArVbiuckVS7Czeuj0a0nCtPT05iamkJLS8uy/JYOiNRoNKyffLztp7jnam9vR21tbVBHmLm5OVx//fX4l3/5F1x11VVxvQY/WLuhOyEEDocj5M/19fWxww4oqFNsWVkZq88OtpLTAQIymYz1EAsGXzVbRkYG8vLyAm6pRAK1Wo3R0dGk5p/+8vrs7GwMDw+jqKgobj3c0eLcuXOYnp4OqwBIPxuNRgO9Xh9U3BIpKMl9NfS+0Ov1uO666/Dggw9iz549MZ0zTJz/RB8cHGTtegFvswmu5joQeW02W0xzz6jpgVqthlarhUQiQV5eXlQ3Fq1oNzU1Jc2G2RdOpxMzMzMYHh72cpZN5n6920Pw9sCiGKUmzQqddg7Nzc0RP0RpoZUKdQCw6UqkIX64JJ+fn8f111+P++67D9ddd11E1xsD1i7RAQRsI+ViZGQEaWlpKCwsZPfZGxsbIRaLQ+bjVGyxadOmkA0Q4cJsNkOtVkOj0YRlwQx8Oi6Zpg0rOQuM6tY3bNgAuVzutToma1TUc6dUePLdMbhcblxbK8I3926Py9+E5vVzc3OwWq1siB8q/aItr9SAJBCMRiNuuOEG3H333Th48GDM1xsBzn+iUy9wm80Go9HIEiUUydVqNUZGRhIqI6UWzGq1mm1NzcvL81pN6ORViUQSlklDIkFlwf704sncr//FX0fxh48mQDwEN19Yjq/F2NHmD/706/60FNS8IhTJzWYzDhw4gNtvvx0333xz3K83BNY20R0OR0g7qYmJCUxOTiInJwfV1dUhQ3XqEa/T6dDY2Ji0EJludanValitVnZc0djY2IoLcoDIdOtAYvfrz/QM4OnTOiiys/GNy6ohT0vsZ0RDfPoQo0MksrOz0d/f72Ve4Q9WqxUHDx7ETTfdhDvuuCOh1xoA5zfRbTYbPv74Y2RkZKCxsTEkyT0eD3p7e1mF2UqFyG63GzMzMxgcHASfz2dJolAoVky3Ti25olmdY92vt7vceGdAi4JMMdIss3C5XCu2Xw+AnWw7PDzMGj0GalG12Wz4whe+gP379+MrX/nKSl3z+Ut02txSWFiIhYUFbNy4kbV98geqsMvJyUFZWdmKhsjUhnnTpk3IzMzE/Pw81Go1dDod0tPT2RsrGbp1um0VaT9/IPhWvcPJ6x862oe3+ufgcbvx3Z3ZuPrCxhX9fFwuF9rb21FeXg6lUgmdTgeNRoP5+Xn2/VDS33zzzbjiiivwD//wDyt5zWtXMAMsupn4I/r09DTGxsbQ0tICPp/PVkRFIhHy8/ORm5vrdVPRHuGqqqq4T+2MFDqdDgMDA14hMnVs4cpxqc8ctQ1OhJHh5OQkNBoNOwU2HuDxeKydMTevD+abN6G3wuF0gc9jIMkuWBUkLysrY+8V7rw7+n6++c1vsvWMz33ucyt6zclAQld0XydYOv1kfn4eDQ0Ny4puvhXvvLw8iMViDA8PJ8SQIFLQ8UzNzc1hEZc2eGg0GhBCkJubi7y8vJiLh1QrbjKZkqZbB/zn9Tk5OXi3YxjP9tlRV6zEd66sgSiMsdeJgMvlYif1BvMddLlcuPPOO1FZWYmqqiq8/vrr+OMf/xhVc43b7cbWrVtRXFyMo0ePYnR0FIcOHYJWq8WWLVvw+9//HiKRCHa7HbfccgtOnToFpVKJ5557jitiWtuhO5foVNRCq9Ph2D0NDQ1Bo9EgPT0dBQUFyMvLW5FOJ1oA1Ov1Ufez060htVoNh8PB5vWRupdSO2yPx7NiebDJ7sInYzrkCWyYU42BEMJGYivVX09JXlJSEtQezO1246677kJ1dTUeeuihmP9+jz/+OD755BMsLCzg6NGjOHDgAK677jocOnQIX/3qV9Hc3Iy77roL/+f//B90dHTgl7/8JZ599lm89NJLeO655+hh1jbRqcsMd6wSHWcbztwzs9mMhoYG1kBSrVbD5XKxK2MyGlW4xIpXATBaOe5q2MrzEIJDvz6Fc/M2iBg3fnltOWoqylZkv57C7XbjzJkzKCoqCto85Ha7cc899yA/Px8/+clPYv77TU1N4dZbb8X3v/99PP744/jzn/+M3NxczMzMQCAQ4MSJE3jooYfwP//zP7jyyivx0EMP4cILL4TL5UJBQQE0Gg29hrWdowOfFt02bdqErKyskCSnXnJpaWnshBA+n4+SkhKUlJTA6XRCo9Gwuna6t50IX2863JHq7eN1fK79Mi1+zc7Oor+/P6Acd7Xo1p1uD8b1VjBuF1w8HmTKgoB5fbhz32NBuCT3eDz49re/jezsbPz4xz+Oy2f5jW98A48++iiMRiOAxYYquVzORnwlJSVQqRaNXFUqFTszTiAQICsrC1qtNmkt1Akl+uzsLAYGBtDS0gKRSBRyRDFt6wymzxYKhSgqKkJRURG7MtLRtzQcjsfAQYfDgbNnzyZcK+5LEmrISBWDtELc09PDvu+VBB8E+6r4eHuKwdVNhSjM9K5VcP3wq6ur2by+p6eHzevjNXCBDl4MNa7J4/HggQcegEgkwmOPPRaXqOzo0aPIy8vDli1b8M4778R8vEQjoUSXSCRoa2tjJ1EG+2CNRiO6u7uxYcOGsOdMc1dGt9sNnU7HDhyUy+XIz8+PqjuNVvmTbVrBMAzkcjnkcjlqampgNpsxPT2Nvr4+pKWlweVywWazrZhZAx1YefvOKnwvTJtsXz987oM5li41SvL8/PygDz+Px4Mf/vCHcDgc+OUvfxm3+sH777+PV199FcePH4fNZsPCwgLuvfdeGAwGuFwuCAQCTE1NsYtEcXExJicnUVJSApfLhfn5+ajbl6NBQnP0Z555BlVVVew2WiBoNBoMDw+HreoKBd/utMzMTDYcDvVBUzvo1VDlpw+cDRs2QCqVeslxaTicjP5t4FO9eEVFRVy2OP11qdHPKFRezyV5sGiLEIJ//dd/xfT0NJ566qmYOxMD4Z133sFjjz2Go0eP4sYbb8T111/PFuOamprwta99Db/4xS/Q2dnJFuNefPFFPP/88/QQa7sY99JLL+GPf/wj+vv7sXv3buzbtw/btm1jyUYIYfeCqXtMvEHDYdqdlp6ejvz8fL9zuGnInCw76GAIplundQqNRgOr1cqGw4makU51DtXV1QmJcLj6A61WGzSv93g87Jz0YJNtCCF49NFHMTQ0hGeeeSah4iUu0UdGRnDo0CHodDq0trbi//2//wexWAybzYabb76ZHRr57LPPoqqqih5ibROdwmq14vXXX8fhw4dx9uxZXHrppbj66qtx9OhRHDp0aNncs0SBOzhxbm6ObUnNzc3F7OwsZmZm0NzcvGItphSR6Nbdbjfry7awsAC5XM5OV4nH35R68IcyaognqCmjRqOB0+n0cp+h6kha2PIHQgieeOIJtLe3449//OOKf55h4PwgOhd2ux0vv/wy7rvvPuTl5aG1tRXXXXcdLr744qR/IGazGbOzs5iamgIhBJWVlcjPz0/aOF5/iEW3zrVe1ul0kMlkbDgczYpGW17DmYOWKNC8nj6cMzIyUFVVFTCvJ4TgySefxN/+9jc8//zzK+JHFwXOP6IDwA9+8AM0Nzdjz549ePvtt3HkyBG8//772L59O/bv349LL700KR8QbZIRCAQoLS1lc2CGYdiVPpkhfDx169zoRavVQiQSsfqDcI5N6wN1dXUrOjIKWPycOjs7IZfLIZPJvPJ6bmsqIQRPPfUU3njjDRw5cmRFH9gR4vwkuj+4XC689957eOGFF/DXv/4Vra2t2L9/P3bv3p2QKjP1pFMqlSgvL/f6nt1uh1qt9ip8JVqgQ2sVTU1NCcknLRYLKy8GwD7I/MlxEzkHLVJwSc79nHzz+t/97new2+2YnJzEG2+8saI20lFg/RCdC7fbjQ8++ACHDx/GX/7yF9TX12P//v244oor4mIyYbfbcfbsWZSVlYWcpkoLX7Ozs3A4HKxAJx5z0YFPdetUBZiMWoXdbmeLeb7viXbmJWMOWih4PB50dXUhMzMzpEjoF7/4BZ577jnI5XKYTCa89dZbET+YbTYbdu3aBbvdDpfLhRtuuAE/+tGPotWvR4L1SXQuPB4PTp48iRdeeAFvvvkmampqsHfvXlx11VVROazSGzmavNPlcrHmExaLBQqFIiaBzmrQrXPfk9FohNPpxIYNG1BYWLiiHV2U5BkZGaisDO5Q88ILL+A3v/kNjh07xj6sonlI0QGMMpkMTqcTO3fuxBNPPIHHH388Gv16JEgRnQuPx4MzZ87g8OHDeO2111BaWoq9e/fi85//fFjztOkYoFDD7cMBFejMzs7CaDRGbB9NdetpaWlhOdcmGtSiury8HAaDAfPz86z+wHc+eqJBh2jKZLKQJH/55Zfx5JNP4ujRo3GtJVgsFuzcuRNPPvkkrr766mj065Fg7Wvd4wkej4e2tja0tbXh4YcfRldXFw4fPoy9e/ciJycH+/fvx9VXX+1XcTQ7O4vx8fGopof6A3ev11evnpmZifz8/IBbXFS3rlAoltUHVgL0Adja2gqJRIKioiIv/cHw8DDS0tJYQ41E7o4QQtDd3Y309PSQJD927Bj+67/+C8eOHYsbyd1uN7Zs2YKhoSF8/etfR3V19arVr0eCNUV0Luhc9MbGRjz00EPo7+/H4cOHccMNNyAzMxN79+7Fnj17kJubi48++ggikQhtbW0JKXT506vPzs5icHCQ3eLKyckBn8+H0+lkNfQrrVsHFhsxhoaG0Nra6lWl5spxaUirVqvR3t7OegXE21iSEIKenh6kpaVxxSR+8cYbb+Cxxx7D8ePH47r1x+fzcebMGRgMBlx77bXo6+uL27FXEmuW6FxQD7kHH3wQ3//+9zE8PIwjR47gpptuwvz8PIqKivDkk08mJfz0JQjd4hodHYVIJILFYkF1dXVUs9jiDY1Gg9HRUbS2tgbdcmMYBjKZjO3io40q3d3dcduVoCQXi8UhSf7222/j4YcfxrFjxxKmF5fL5fjMZz6DEydOrFr9eiRYUzl6JCCE4Itf/CIUCgUqKyvxyiuvwOPxYM+ePdi/fz9KSkqSmhdbLBa0t7dDLpfDbDZDIBAgLy8v7H3teGN2dhYTExN+RyRFAjpOSa1Ww2azsR2Ekcy3I4Sgt7cXQqEwZJ/9e++9h+9973s4duxYyB2TSKHRaCAUCiGXy2G1WvHZz34W999/P5555plo9OuRIFWMiwVdXV1oaGgAsHgzTU9P48iRI3jppZdgtVpx9dVXY9++fXHtNfcHf7p17r42Fejk5eUlZf93enoaKpUKLS0tcU1lqByXVvDlcjny8vKCdqcRQtDX1weBQBCS5CdOnMB9992Ho0ePJqR1uKOjA7feeivcbjc8Hg8OHDiAH/zgB9Hq1yNBiuiJglqtxksvvYQXX3wROp0On//857F///64Txyl1eympqaAoS13UITH44mbt5w/qFQqVtOfyEYPKsdVq9Ws64yvHJeSnM/no7a2Nujf/ZNPPsE999yDV199dcU99BOAFNGTAa1Wi1deeQVHjhzBzMwMrrzySlx77bWoq6uLScBCu+Ei0a37esvFU6AzOTmJubk5NDU1JX27jNtMJBaLkZubi/n5eQgEgpAP1zNnzuCuu+7CSy+9FO2KudqRInqyYTAY8Oc//xkvvvgiRkdHccUVV2D//v1obm6OiPTx0K37CnSiyX8pxsbGYDAY0NTUtKKz4YBFiW1PTw+sViukUikbwfjrK+jq6sKdd96Jw4cPY8OGDStwtUlBiugrCaPRiGPHjuHIkSPo7+/HZZddhn379mHr1q1ByTIxMcGunPEKj33zXyrQyc7ODkl6ag2dLIltMNA59h6PBxs3bvSKYLgtqTKZDP39/bjtttvw7LPPor6+fkWvO8FIEX21gNtT39HRgUsvvRT79u3DBRdcwIbBydKtcwU68/Pz7NhpX4EOddOlU15XWn1Hp866XC5s2rRp2fXQCObMmTO477774Ha78aMf/Qg333xzUlONFcDaJfrrr7+Oe++9F263G3feeSceeOCBaA+16mCz2fDmm2/i8OHDOHXqFC666CLs3bsXx44dw8GDB7Ft27akkYoQwha9dDod6yKrUCgwPDwMt9u9onPQuNc5NDQEp9MZ8npGR0fxxS9+Ebfffju6urqgUCjwyCOPRHzOyclJ3HLLLZidnQXDMPjyl7+Me++9FzqdDgcPHsTY2BgqKirw/PPPs5N27r33Xhw/fhxSqRS//e1v0dbWFsvbDhdrk+hutxsbNmzAm2++iZKSEmzbtg1/+tOfzsvwy+Fw4M0338Q3vvENSKVStLW14dprr8WuXbuSvj9OCMHCwgLUajVUKhUEAgGqq6uRm5ublDlwwa5reHgYdrsd9fX1QUk+MTGBgwcP4te//jW2bdsW03mnp6cxPT2NtrY2GI1GbNmyBS+//DJ++9vfQqFQ4IEHHsAjjzwCvV6Pn/70pzh+/Dj+8z//E8ePH8dHH32Ee++9Fx999FFM1xAmEk70hMSWH3/8MWpqalBVVQWRSIRDhw7hlVdeScSpVhwikYjVRZ86dQo333wzXnvtNezcuRNf+cpX8Nprr8FmsyXlWqjVssPhQFFREZqbm2GxWHDq1Cm0t7dDpVLB4XAk5Vq4GBkZCYvkKpUKN910E5588smYSQ4AhYWF7IqckZGBuro6qFQqvPLKK7j11lsBALfeeitefvllAMArr7yCW265BQzD4IILLoDBYMD09HTM17EakJDHPFfsDyw2AiTpybgiuOeee9gbePfu3di9ezfcbjfef/99HDlyBA899BA2b96M/fv34/LLL0/I/jjwaUecVCplRUAZGRmorq5mBTpnz54Fj8dLiFbdH0ZGRmC1WkPWCGZmZnDw4EE88cQTuOiii+J+HWNjY2hvb8eOHTswOzvLSpALCgowOzsLwP99q1KpVoVcOVacF1r3lYa/G5jP52PXrl3YtWsXPB4PPv74Yxw+fBg/+clPUFNTg/379+PKK6+Mm7kDdWLJysrya34glUpRUVGBiooK2Gw2qNVqdHd3J1SgMzo6yhYmg5FcrVbjxhtvxL/9279h165dcb0GYNGD4Prrr8fPf/7zZY45oeYNnC9ICNGp2J+C2wiwHsHj8XDBBRfgggsuYHvqX3jhBTz++OMoKytje+qjbbWkba9KpTIs1ZhEImGHKtDtrf7+fjgcDq8GlVgIMDo6CqPRGJLkc3NzuPHGG/Hwww/jsssui/p8geB0OnH99dfji1/8Iq677joAQH5+Pqanp1FYWIjp6WnWp/58vm8TUoxzuVzYsGED3nrrLRQXF2Pbtm344x//iM2bN0d3lecpqMHCCy+8gOPHjyM3Nxf79u3DNddcE7a1Mh1mkJeXF9TnPBw4nU5WoEP94qMR6IyNjWFhYSHkFqNer8d1112HBx98EHv27Inp2v2BEIJbb70VCoUCP//5z9nX//Ef/xFKpZItxul0Ojz66KNsfzstxt1zzz34+OOP435dfrA2q+4AcPz4cXzjG9+A2+3G7bffju9///vRHmpdgOq+Dx8+zLql7N27F9dccw1yc3P9Eo2OCk5Eb7uvQIfaZsnl8qCkHx8fh8FgCDm3fX5+Htdffz3uu+8+dqWNN/72t7/hkksu8bqWH//4x9ixYwcOHDiAiYkJlJeX4/nnn4dCoQAhBHfffTdef/11SKVSPP3009i6dWtCrs0Ha5foKUQPuh115MgRvPLKKxCLxdizZw/27duHgoICMAzDzkErLS2Ne7umLzweD3Q6HdRqNSvQyc/PX9aVNjExwc6QD0Zyo9GIG264AXfffTcOHjyY0GtfI0gRHVhTwoe4gxCCiYkJtr0WAC677DK88cYb+M1vfpP0Ti7frjQq0LFYLGFp6c1mMw4cOIDbb78dN998cxKvfFUjRXRgTQkfEgpCCDo6OrB3716Ul5fD6XTimmuuwb59+1BZWZn06jEV6AwNDbHuKvn5+cjJyfEr0LFarThw4ACrekuBxdoUzMQbKeHDIhiGwYkTJ/D000/jr3/9K15++WUolUp861vfwmc+8xk8+uij6O/vR4iHd1yvx2g0gsfj4dJLL0VVVRXMZrNfgY7NZsMXv/hF3HjjjbjtttuScn0pfIo1saJzMTY2hl27dqGrqwtlZWUwGAwAFleX7OxsGAwGXHPNNXjggQewc+dOAIuh7k9/+tNkFVZWBFqtFi+//DJefPFFzM7OevXUJ2qln5qaglqtRnNz87KmE2omqVKp8MMf/hAMw+Bzn/scHnzwwXWxbx0hUis6FynhQ2AolUrccccdOHbsGP73f/8XGzduxL/+679i586deOihh3DmzBl4PJ64nU+lUgUkOQDWrnnHjh3Iz89Hfn4+3n77bdx0000xnff2229HXl4eaxEGADqdDldccQVqa2txxRVXQK/XA1h8+N9zzz2oqalBU1MTTp8+HdO51zLWDNGDCR8ArBvhQziQy+W45ZZb8PLLL+Pdd99FW1sbfvazn+Hiiy/Ggw8+iJMnT8ZE+nPnzmF2djYgySlcLhfuvPNObN++HS+++CL+8pe/4Omnn476vADwpS99Ca+//rrXa4888gguu+wyDA4O4rLLLmM73V577TUMDg5icHAQ//3f/4277rorpnOvZawJohNCcMcdd6Curg7f+ta32Nf37t2LZ555BgDwzDPPYN++fezrv/vd70AIwYcffoisrKzzQq8cDTIyMnDo0CG88MILOHHiBC6++GL86le/wkUXXYT7778fH3zwAdxud9jHo4XRUCR3u9246667UF9fj+9+97tstBXrdNpdu3YtExOtt1pNVCCEBPtvVeC9994jAEhjYyNpbm4mzc3N5NixY2Rubo7s3r2b1NTUkMsuu4xotVpCCCEej4d87WtfI1VVVaShoYGcPHlyhd/B6oPVaiWvvvoqueWWW0hDQwP58pe/TF577TUyPz9PzGaz3/+GhobIu+++SxYWFgL+jNlsJgsLC+RLX/oSeeCBB4jH44n7tY+OjpLNmzezX2dlZbH/9ng87NdXX301ee+999jv7d69e7XeC6F4GPN/a6KpZefOnQEryW+99day1xiGwS9+8YtEX9aahkQiwZ49e7Bnzx44HA785S9/wZEjR3Dfffdhx44d2L9/Py655BK2p35mZoa1iA62kns8Hnz7299GdnY2Hn744aTXTdZ7rSYQ1gTRkw23242tW7eiuLgYR48eTcbY3BWFSCTCVVddhauuugoulwvvvvsuXnjhBXz3u99FW1sb8vPzYTQa8eijjwY1sPB4PHjggQcgEonw2GOPJc2fbj02qUSKNZGjJxtPPPEE6urq2K/vv/9+fPOb38TQ0BCys7Px1FNPAQCeeuopZGdnY2hoCN/85jdx//33r9Qlxw0CgQC7d+/Gk08+ibNnz2LDhg149tln8dFHH+ErX/kKXn31VVgslmW/5/F48IMf/AAOhwP/8R//kVQTylStJgyEiO3XHSYnJ8nu3bvJW2+9Ra6++mri8XiIUqkkTqeTEELIBx98QD772c8SQgj57Gc/Sz744ANCCCFOp5MolcqE5KQrBafTSW677TZiMBiI2+0mJ06cIN/61rdIU1MTuf7668nvf/97Mjs7S0wmE7n//vvJrbfeSlwuV0Kv6dChQ6SgoIAIBAJSXFxMfv3rX58PtZqE5+gpovvg+uuvJ5988gl5++23ydVXX000Gg2prq5mvz8xMcEWgjZv3kwmJyfZ71VVVRGNRpP0a0423G43+eSTT8j9999PWlpaSH19Pdm/f3/CSX4eI1WMSyaOHj2KvLw8bNmyBe+8885KX86qBY/Hw5YtW7Blyxb8+Mc/xtGjR7F79+7z3ZJ5TSNFdA7ef/99vPrqqzh+/DhsNhsWFhZw7733nhdjcxMFHo+HvXv3rvRlpBACqWIcBz/5yU8wNTWFsbExPPvss9i9ezf+8Ic/4DOf+QwOHz4MYHmxhxaBDh8+jN27d6e2dlJYlUgRPQz89Kc/xeOPP46amhpotVrccccdAIA77rgDWq0WNTU1ePzxx6MaMpBCCsnAmuteS2F94Hye9OMHqe618xkGgwE33HADNm3ahLq6Opw4cSLViYVFwdLXv/51vPbaa+jp6cGf/vQn9PT0rPRlrWmkiL6CuPfee3HVVVehr68PZ8+eRV1dXaoTC+tr0k+ykCL6CmF+fh7vvvsum++LRCLI5fJUJxYCT0xJIXqkiL5CGB0dRW5uLm677Ta0trbizjvvhNlsjnhcUAophIMU0VcILpcLp0+fxl133YX29nakp6cvq9qv106sVDNK/JEi+gqhpKQEJSUl2LFjBwDghhtuwOnTp1OuOQC2bduGwcFBjI6OwuFw4Nlnn02JcmJEiugrhIKCApSWlqK/vx/AYl99fX19qhMLix10//Vf/4Urr7wSdXV1OHDgQGqcV4xI7aOvIM6cOYM777wTDocDVVVVePrpp+HxeFbbuKAUEo/UAIcUUlgHSDjRQzW1rL9K0HkKhmG+CeBOLD68OwHcBqAQwLMAlABOAbiZEOJgGEYM4HcAtgDQAjhICBlbietOIT5I5ejrAAzDFAO4B8BWQkgDAD6AQwB+CuBnhJAaAHoAdyz9yh0A9Euv/2zp51JYw0gRff1AACCNYRgBACmAaQC7ARxe+v4zAPYv/Xvf0tdY+v5lzHrc5zuPkCL6OgAhRAXgMQATWCT4PBZDdQMhxLX0Y1MA6H5dMYDJpd91Lf38+mq0P8+QIvo6AMMw2VhcpSsBFAFIB3DVil5UCklFiujrA5cDGCWEaAghTgAvArgYgHwplAeAEgBUU6sCUAoAS9/PwmJRLoU1ihTR1wcmAFzAMIx0Kde+DEAPgLcB3LD0M7cCoC1iry59jaXv/4WE2IdNYXUj1D56CucJGIb5EYCDAFwA2rG41VaMxe01xdJr/x8hxM4wjATA7wG0AtABOEQIGVmRC08hLkgRPYUU1gFSoXsKKawDpIieQgrrACmip5DCOkCK6CmksA6QInoKKawDpIieQgrrACmip5DCOkCK6CmksA7w/wPWhJu78Lh/JAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Dimensions of grid x-components\")\n", - "print(np.shape(fi.floris.grid.x))\n", - "\n", - "print()\n", - "print(\"Turbine 3 grid x-components for first wind direction and first wind speed\")\n", - "print(fi.floris.grid.x[0, 0, 2, :, :])\n", - "\n", - "x = fi.floris.grid.x[0, 0, :, :, :]\n", - "y = fi.floris.grid.y[0, 0, :, :, :]\n", - "z = fi.floris.grid.z[0, 0, :, :, :]\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.scatter(x, y, z, marker=\".\")\n", - "ax.set_zlim([0, 150])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ebfdc746", - "metadata": {}, - "source": [ - "## Execute wake calculation\n", - "\n", - "Running the wake calculation is a one-liner. This will calculate the velocities\n", - "at each turbine given the wake of other turbines for every wind speed and wind\n", - "direction combination. Since we have not explicitly specified yaw control settings,\n", - "all turbines are aligned with the inflow." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "e3bf1698", - "metadata": {}, - "outputs": [], - "source": [ - "fi.calculate_wake()" - ] - }, - { - "cell_type": "markdown", - "id": "e11352e8", - "metadata": {}, - "source": [ - "## Get turbine power\n", - "\n", - "At this point, the simulation has completed and we can use the `FlorisInterface` to\n", - "extract useful information such as the power produced at each turbine. Remember that\n", - "we have configured the simulation with two wind directions, two wind speeds, and four\n", - "turbines." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "cc05bfe7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensions of `powers`\n", - "(2, 2, 4)\n", - "\n", - "Turbine powers for 8 m/s\n", - "Wind direction 0\n", - " Turbine 0 - 1,691.33 kW\n", - " Turbine 1 - 1,691.33 kW\n", - " Turbine 2 - 592.65 kW\n", - " Turbine 3 - 592.98 kW\n", - "\n", - "Wind direction 1\n", - " Turbine 0 - 1,691.33 kW\n", - " Turbine 1 - 1,691.33 kW\n", - " Turbine 2 - 1,631.07 kW\n", - " Turbine 3 - 1,629.76 kW\n", - "\n", - "Turbine powers for all turbines at all wind conditions\n", - "[[[1691.32664838 1691.32664838 592.6531181 592.97842923]\n", - " [2407.84167188 2407.84167188 861.30649817 861.73255027]]\n", - "\n", - " [[1691.32664838 1691.32664838 1631.06554071 1629.75543674]\n", - " [2407.84167188 2407.84167188 2321.40975418 2319.53218301]]]\n" - ] - } - ], - "source": [ - "powers = fi.get_turbine_powers() / 1000.0 # calculated in Watts, so convert to kW\n", - "\n", - "print(\"Dimensions of `powers`\")\n", - "print( np.shape(powers) )\n", - "\n", - "N_TURBINES = fi.floris.farm.n_turbines\n", - "\n", - "print()\n", - "print(\"Turbine powers for 8 m/s\")\n", - "for i in range(2):\n", - " print(f\"Wind direction {i}\")\n", - " for j in range(N_TURBINES):\n", - " print(f\" Turbine {j} - {powers[i, 0, j]:7,.2f} kW\")\n", - " print()\n", - "\n", - "print(\"Turbine powers for all turbines at all wind conditions\")\n", - "print(powers)" - ] - }, - { - "cell_type": "markdown", - "id": "8ab273db", - "metadata": {}, - "source": [ - "## Applying yaw angles\n", - "\n", - "Yaw angles are applied to turbines through the `FlorisInterface.calculate_wake` function.\n", - "In order to fit into the vectorized framework, the yaw settings must be represented as\n", - "a `Numpy.array` with dimensions equal to:\n", - "- 0: number of wind directions\n", - "- 1: number of wind speeds\n", - "- 2: number of turbines\n", - "\n", - "**Unlike the data configured in `FlorisInterface.reinitialize()`, yaw angles are not retained**\n", - "**in memory and must be provided each time `FlorisInterface.calculate_wake` is used.**\n", - "**If no yaw angles are given, all turbines will be aligned with the inflow.**\n", - "\n", - "It is typically easiest to start with an array of 0's and modify individual\n", - "turbine yaw settings, as shown below." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "be78e20d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Yaw angle array initialized with 0's\n", - "[[[0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]]\n", - "\n", - " [[0. 0. 0. 0.]\n", - " [0. 0. 0. 0.]]]\n", - "First turbine yawed 25 degrees for every atmospheric condition\n", - "[[[25. 0. 0. 0.]\n", - " [25. 0. 0. 0.]]\n", - "\n", - " [[25. 0. 0. 0.]\n", - " [25. 0. 0. 0.]]]\n" - ] - } - ], - "source": [ - "yaw_angles = np.zeros((2, 2, 4))\n", - "print(\"Yaw angle array initialized with 0's\")\n", - "print(yaw_angles)\n", - "\n", - "print(\"First turbine yawed 25 degrees for every atmospheric condition\")\n", - "yaw_angles[:, :, 0] = 25\n", - "print(yaw_angles)\n", - "\n", - "fi.calculate_wake(yaw_angles=yaw_angles)" - ] - }, - { - "cell_type": "markdown", - "id": "1ef54dc5", - "metadata": {}, - "source": [ - "## Start to finish\n", - "\n", - "Let's put it all together. The code below outlines these steps:\n", - "1. Load an input file\n", - "2. Modify the inputs with a more complex wind turbine layout and additional atmospheric conditions\n", - "3. Calculate the velocities at each turbine for all atmospheric conditions\n", - "4. Get the total farm power\n", - "5. Develop the yaw control settings\n", - "6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", - "7. Get the total farm power\n", - "8. Compare farm power with and without wake steering" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "205738aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Power % difference with yaw\n", - " 270 degrees: 7.39%\n", - " 280 degrees: 0.13%\n" - ] - } - ], - "source": [ - "# 1. Load an input file\n", - "fi = FlorisInterface(\"gch.yaml\")\n", - "\n", - "fi.floris.solver\n", - "\n", - "# 2. Modify the inputs with a more complex wind turbine layout\n", - "D = 126.0 # Design the layout based on turbine diameter\n", - "x = [0, 0, 6 * D, 6 * D]\n", - "y = [0, 3 * D, 0, 3 * D]\n", - "wind_directions = [270.0, 280.0]\n", - "wind_speeds = [8.0]\n", - "\n", - "# Pass the new data to FlorisInterface\n", - "fi.reinitialize(\n", - " layout_x=x,\n", - " layout_y=y,\n", - " wind_directions=wind_directions,\n", - " wind_speeds=wind_speeds\n", - ")\n", - "\n", - "# 3. Calculate the velocities at each turbine for all atmospheric conditions\n", - "# All turbines have 0 degrees yaw\n", - "fi.calculate_wake()\n", - "\n", - "# 4. Get the total farm power\n", - "turbine_powers = fi.get_turbine_powers() / 1000.0 # Given in W, so convert to kW\n", - "farm_power_baseline = np.sum(turbine_powers, 2) # Sum over the third dimension\n", - "\n", - "# 5. Develop the yaw control settings\n", - "yaw_angles = np.zeros( (2, 1, 4) ) # Construct the yaw array with dimensions for two wind directions, one wind speed, and four turbines\n", - "yaw_angles[0, :, 0] = 25 # At 270 degrees, yaw the first turbine 25 degrees\n", - "yaw_angles[0, :, 1] = 25 # At 270 degrees, yaw the second turbine 25 degrees\n", - "yaw_angles[1, :, 0] = 10 # At 265 degrees, yaw the first turbine -25 degrees\n", - "yaw_angles[1, :, 1] = 10 # At 265 degrees, yaw the second turbine -25 degrees\n", - "\n", - "# 6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", - "fi.calculate_wake(yaw_angles=yaw_angles)\n", - "\n", - "# 7. Get the total farm power\n", - "turbine_powers = fi.get_turbine_powers() / 1000.0\n", - "farm_power_yaw = np.sum(turbine_powers, 2)\n", - "\n", - "# 8. Compare farm power with and without wake steering\n", - "difference = 100 * (farm_power_yaw - farm_power_baseline) / farm_power_baseline\n", - "print(\"Power % difference with yaw\")\n", - "print(f\" 270 degrees: {difference[0, 0]:4.2f}%\")\n", - "print(f\" 280 degrees: {difference[1, 0]:4.2f}%\")" - ] - }, - { - "cell_type": "markdown", - "id": "99b7465c", - "metadata": {}, - "source": [ - "## Visualization\n", - "\n", - "While comparing turbine and farm powers is meaningful, a picture is worth at least\n", - "1000 Watts, and the `FlorisInterface` provides powerful routines for visualization.\n", - "\n", - "**NOTE `floris.tools` is under active design and development. The API's will change and additional functionality from FLORIS v2 will be included in upcoming releases.**\n", - "\n", - "The visualization functions require that the user select a single atmospheric condition\n", - "to plot. The internal data structures still have the same shape but the wind speed and\n", - "wind direction dimensions have a size of 1. This means that the yaw angle array used\n", - "for plotting must have the same shape as above but a single atmospheric condition must\n", - "be selected.\n", - "\n", - "Let's create a horizontal slice of each atmospheric condition from above with and without\n", - "yaw settings included. Notice that although we are plotting the conditions for two\n", - "different wind directions, the farm is rotated so that the wind is coming from the\n", - "left (West) in both cases." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8bb179ff", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from floris.tools.visualization import visualize_cut_plane\n", - "\n", - "fig, axarr = plt.subplots(2, 2, figsize=(15,8))\n", - "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], height=90.0)\n", - "visualize_cut_plane(horizontal_plane, ax=axarr[0,0], title=\"270 - Aligned\")\n", - "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], yaw_angles=yaw_angles[0:1,0:1] , height=90.0)\n", - "visualize_cut_plane(horizontal_plane, ax=axarr[0,1], title=\"270 - Yawed\")\n", - "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], height=90.0)\n", - "visualize_cut_plane(horizontal_plane, ax=axarr[1,0], title=\"280 - Aligned\")\n", - "\n", - "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], yaw_angles=yaw_angles[1:2,0:1] , height=90.0)\n", - "visualize_cut_plane(horizontal_plane, ax=axarr[1,1], title=\"280 - Yawed\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "d84cf194", - "metadata": {}, - "source": [ - "We can also plot the streamwise inflow velocities on the turbine rotor\n", - "grid points located on the rotor plane. The `plot_rotor_values` function\n", - "simply plots any data given as the first argument, so in this case\n", - "`fi.floris.flow_field.u` contains the yawed calculation from above." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "3e517614", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from floris.tools.visualization import plot_rotor_values\n", - "\n", - "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, wd_index=0, ws_index=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", - "fig.suptitle(\"Wind direction 270\")\n", - "\n", - "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, wd_index=1, ws_index=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", - "fig.suptitle(\"Wind direction 280\")\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "4dc966e1", - "metadata": {}, - "source": [ - "## On Grid Points" - ] - }, - { - "cell_type": "markdown", - "id": "e8241714", - "metadata": {}, - "source": [ - "In FLORIS, grid points are the points in space where the wind conditions are calculated.\n", - "In a typical simulation, these are all located on a regular grid on each turbine rotor.\n", - "\n", - "The parameter `turbine_grid_points` specifies the number of rows and columns which define the turbine grid.\n", - "In the example inputs, this value is 3 meaning there are 3 x 3 = 9 total grid points for each turbine.\n", - "Wake steering codes currently require greater values greater than 1 in order to compute gradients.\n", - "However, a single grid point (1 x 1) may be suitable for non wind farm control applications,\n", - "but retuning of some parameters might be required.\n", - "\n", - "We can visualize the locations of the grid points in the current example using `matplotlib.pyplot`." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "774acfea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape of xs: (2, 1, 4, 3, 3)\n", - " 2 wd x 2 ws x 4 turbines x 3 x 3 grid points\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Get the grid points\n", - "xs = fi.floris.grid.x\n", - "ys = fi.floris.grid.y\n", - "zs = fi.floris.grid.z\n", - "\n", - "# Consider the shape\n", - "print(f\"shape of xs: {xs.shape}\")\n", - "print(\" 2 wd x 2 ws x 4 turbines x 3 x 3 grid points\")\n", - "\n", - "# Lets plot just one wd/ws conditions\n", - "xs = xs[1, 0, :, :, :]\n", - "ys = ys[1, 0, :, :, :]\n", - "zs = zs[1, 0, :, :, :]\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.add_subplot(111, projection=\"3d\")\n", - "ax.scatter(xs, ys, zs, marker=\".\")\n", - "ax.set_zlim([0, 150])\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e91f7a84", - "metadata": {}, - "source": [ - "## Calculating AEP" - ] - }, - { - "cell_type": "markdown", - "id": "34bc7865", - "metadata": {}, - "source": [ - "Calculating AEP in FLORIS v3 leverages the vectorized framework to\n", - "substantially reduce the computation time with respect to v2.4.\n", - "Here, we demonstrate a simple AEP calculation for a 25-turbine farm\n", - "using several different modeling options. We make the assumption\n", - "that every wind speed and direction is equally likely. We also\n", - "report the time required for the computation using the Python\n", - "`time.perf_counter()` function." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "ee1918d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating AEP for 1440 wind direction and speed combinations...\n", - "Number of turbines = 25\n", - "Model AEP (GWh) Compute Time (s)\n", - "Jensen 843.237 3.604 \n", - "GCH 843.905 6.116 \n", - "CC 839.263 9.227 \n" - ] - } - ], - "source": [ - "import time\n", - "from typing import Tuple\n", - "\n", - "wind_directions = np.arange(0.0, 360.0, 5.0)\n", - "wind_speeds = np.arange(5.0, 25.0, 1.0)\n", - "\n", - "num_bins = len(wind_directions) * len(wind_speeds)\n", - "print(f\"Calculating AEP for {num_bins} wind direction and speed combinations...\")\n", - "\n", - "# Set up a square 25 turbine layout\n", - "N = 5 # Number of turbines per row and per column\n", - "D = 126.0\n", - "\n", - "X, Y = np.meshgrid(\n", - " 7.0 * D * np.arange(0, N, 1),\n", - " 7.0 * D * np.arange(0, N, 1),\n", - ")\n", - "X = X.flatten()\n", - "Y = Y.flatten()\n", - "print(f\"Number of turbines = {len(X)}\")\n", - "\n", - "# Define several models\n", - "fi_jensen = FlorisInterface(\"jensen.yaml\")\n", - "fi_gch = FlorisInterface(\"gch.yaml\")\n", - "fi_cc = FlorisInterface(\"cc.yaml\")\n", - "\n", - "# Assign the layouts, wind speeds and directions\n", - "fi_jensen.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "fi_cc.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", - "\n", - "def time_model_calculation(model_fi: FlorisInterface) -> Tuple[float, float]:\n", - " \"\"\"\n", - " This function performs the wake calculation for a given\n", - " FlorisInterface object and computes the AEP while\n", - " tracking the amount of wall-time required for both steps.\n", - "\n", - " Args:\n", - " model_fi (FlorisInterface): _description_\n", - " float (_type_): _description_\n", - "\n", - " Returns:\n", - " tuple(float, float):\n", - " 0: AEP\n", - " 1: Wall-time for the computation\n", - " \"\"\"\n", - " start = time.perf_counter()\n", - " model_fi.calculate_wake()\n", - " aep = model_fi.get_farm_power().sum() / num_bins / 1E9 * 365 * 24\n", - " end = time.perf_counter()\n", - " return aep, end - start\n", - "\n", - "jensen_aep, jensen_compute_time = time_model_calculation(fi_jensen)\n", - "gch_aep, gch_compute_time = time_model_calculation(fi_gch)\n", - "cc_aep, cc_compute_time = time_model_calculation(fi_cc)\n", - "\n", - "print('Model AEP (GWh) Compute Time (s)')\n", - "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"Jensen\", jensen_aep, jensen_compute_time))\n", - "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"GCH\", gch_aep, gch_compute_time))\n", - "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"CC\", cc_aep, cc_compute_time))" - ] - }, - { - "cell_type": "markdown", - "id": "c006ae1e", - "metadata": {}, - "source": [ - "## Wake Steering Design" - ] - }, - { - "cell_type": "markdown", - "id": "f5777dae", - "metadata": {}, - "source": [ - "FLORIS V3 further includes new optimization routines for the design of wake steering controllers. The SerialRefine is a new method for quickly identifying optimum yaw angles." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "32a93c6d", - "metadata": {}, - "outputs": [], - "source": [ - "# Demonstrate on 7-turbine single row farm\n", - "X = np.linspace(0, 6*7*D, 7)\n", - "Y = np.zeros_like(X)\n", - "wind_speeds = [8.]\n", - "wind_directions = np.arange(0., 360., 2.)\n", - "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7d773cdc", - "metadata": {}, - "outputs": [], - "source": [ - "from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR\n", - "\n", - "# Define the SerialRefine optimization\n", - "yaw_opt = YawOptimizationSR(\n", - " fi=fi_gch,\n", - " minimum_yaw_angle=0.0, # Allowable yaw angles lower bound\n", - " maximum_yaw_angle=25.0, # Allowable yaw angles upper bound\n", - " Ny_passes=[5, 4],\n", - " exclude_downstream_turbines=True,\n", - " exploit_layout_symmetry=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "1ccb9ab7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[Serial Refine] Processing pass=0, turbine_depth=0 (0.0%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=1 (7.1%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=2 (14.3%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=3 (21.4%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=4 (28.6%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=5 (35.7%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=0, turbine_depth=6 (42.9%)\n", - "Skipping 90/450 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=0 (50.0%)\n", - "Skipping 86/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=1 (57.1%)\n", - "Skipping 86/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=2 (64.3%)\n", - "Skipping 85/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=3 (71.4%)\n", - "Skipping 85/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=4 (78.6%)\n", - "Skipping 84/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=5 (85.7%)\n", - "Skipping 85/360 calculations: already in memory.\n", - "[Serial Refine] Processing pass=1, turbine_depth=6 (92.9%)\n", - "Skipping 90/360 calculations: already in memory.\n", - "Optimization wall time: 4.358 s\n" - ] - } - ], - "source": [ - "start = time.perf_counter()\n", - "\n", - "## Calculate the optimum yaw angles for 25 turbines and 72 wind directions\n", - "df_opt = yaw_opt.optimize()\n", - "\n", - "end = time.perf_counter()\n", - "\n", - "walltime = end - start\n", - "print(f\"Optimization wall time: {walltime:.3f} s\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "fb2e01e8", - "metadata": {}, - "source": [ - "In the results, T0 is the upstream turbine when wind direction is 270, while T6 is upstream at 90 deg" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "686548be", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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u3Mjw4cPTsq9rr72Wz33ucyxatIgjjzwyLfsMonMmWLK5Oemkkxg0aFCIEcWXjcIrIWY2D5gHUFlZSX19fWjH+uEPf8gvf/nL0PYvIiK9x4gRI9LyO2X48OGUlpZy3XXX9TwoyZjbb7+dCRMmZO342Si8NgJd/zQYGV32Ee5+B3AHwNSpU72mpia0gEaNGsWZZ57J5MmTQztGLnv55ZeVmwDKTTDlJphyE1um8nLCCScwcODAtOxr9erVbNq0KS37ikfnTLBkc/Oxj32M8vLyECOKLxuF10vAmOgNuDcSuRXRVVmI44Cjjz6aE044gdNO052QYmlvb1duAig3wZSbYMpNbLmYl9GjRzN69OjQj5OLucmUXMuNZWNQoJmdD9wKFAF3uvu/dPP+rcCGkMMaArwf8jFylXITTLkJptwEU25iU16CKTfBemNujnL3obFWZKXw6o3MrMHdp2Y7jt5IuQmm3ARTboIpN7EpL8GUm2C5lpusTKAqIiIiUohUeImIiIhkiAqvD92R7QB6MeUmmHITTLkJptzEprwEU26C5VRuNMZLREREJEPU4iUiIiKSISq8RERERDJEhZeIiIhIhqjwEhEREckQFV4iIiIiGaLCS0RERCRDVHiJiIiIZIgKLxEREZEM6ZvtABIxZMgQr6qqCvUYra2tDBgwINRj5CrlJphyE0y5CabcxKa8BFNugvXG3Cxbtux9dx8aa11OFF5VVVU0NDSEeoz6+npqampCPUauUm6CKTfBlJtgyk1syksw5SZYb8yNmW0IWqeuRhERKUjTpk3jxz/+cbbDkAKjwktERArO3r17efHFF/n6179Oc3NztsORAqLCS0RECk5raysA27Zt47//+7+zHI0UkpwY4yUiIpJOLS0tAJSUlPD973+fL3zhCwwcODDLURWe9vZ2GhsbaWtrS3kfgwYNYs2aNWmMKnGlpaWMHDmS4uLihLdR4SUiIgWns/D64he/yPe+9z3++7//m5tvvjnLURWexsZGysvLqaqqwsxS2kdzczPl5eVpjqx77k5TUxONjY2MHj064e3U1SgiIgWns/Cqqanhggsu4Pvf/z47d+7MclSFp62tjYqKipSLrmwyMyoqKpJurVPhJSIiBaez8CorK+Of//mf+eCDD3jwwQezHFVhysWiq1MqsSfU1Whmg4HhwG7gLXffn/SRREREeomuhdfkyZMZPHgwL7zwAp/97GezHJlkUlNTE2effTYAW7ZsoaioiKFDI/OeTp48mUcffZTDDz+clStXpu2YgYWXmQ0CbgBmA/2ArUApUGlmLwC3u/uStEUiIiKSIV0LLzPjpJNOYunSpVmOSjKtoqKCFStWALBw4ULKysq46aabAHj66af5whe+wDXXXJPWY8branwQeAc43d3Huft0d5/q7kcCtwAXmtl1QRub2ZFmtsTMVpvZKjO7Mbr8MDN73MzWRv8dnNZPJCIi0o2uhRdEJlNduXLlgeUiZ5xxBocddlja9xvY4uXuM+OsWwYs62bf+4Avu/vLZlYOLDOzx4Fa4Al3v8XMFgALgK8mHbmIiEiKYhVe+/fvp6GhodfdfqZQzJ8//0DrUzI6OjooKiqKua66uppbb721Z4GlWbeD681scozHMWYWd3yYu29295ejz5uBNcAI4EJgcfRti4GLevQJREREktRZeHXeXPmkk04CUHejhC6RwfW3A5OBVwADjgdWAYPM7PPu/ofudmBmVcAkYClQ6e6bo6u2AJUpxC0iIpKylpYWSktL6ds38mtwyJAhHHPMMSq8sijVlqlszeOVqkQKr03Ade6+CsDMJgLfBv4ReAiIW3iZWRnwS2C+u+/seumlu7uZecB284B5AJWVldTX1ycQaupaWlpCP0auUm6CKTfBlJtgyk1smczL66+/TklJyUeOV1VVxTPPPNMr/2/y9ZwZNGhQj++V2dHRkZb7be7Zs4fi4uKP7KulpYX9+/fH3X9bW1tS/zeJFF5jO4suAHdfbWbj3X19d/NXmFkxkaLrXnd/KLr4XTMb5u6bzWwY8F6sbd39DuAOgKlTp3rYfe719fXq1w+g3ARTboIpN8GUm9gymZdFixYxePDgjxzvlVde4YknnuDYY49l5MiRGYkjUfl6zqxZs6bHrVXpavEqKSmhpKTkwL5mz55NfX0977//PhMmTOBb3/oW113319cUlpaWMmnSpISPk0jhtcrMfgzcH319BbDazEqA9qCNLFKV/RRY4+4/6LLqEWAukSsj5wIPJxytiIhIGrS0tBwYWN9p2rRpQGScV28rvCR8Cxcu/Mjr++67L5TjJDJzfS2wDpgffayPLmsHZsTZ7jRgDnCWma2IPs4nUnDNNLO1wDnR1yIiIhkTq/Cqrq6mX79+Gucloeq2xcvdd5vZ7cCj7v76QasDJzxx92eJDMaP5ezEQxQREUmvWIVXSUkJ1dXVKrwkVIlMJ/EpYAXwu+jrajN7JOS4REREQhOr8IJId2NDQwP79u3LQlRSCBLpavwmcBKwHcDdVwCjwwtJREQkXM3NzTELr1NOOYVdu3bx61//OvNBFSj3mJMb5IRUYk+k8Gp39x0HHyvpI4mIiPQSQS1el1xyCZMnT+b666/nrbfeynxgBaa0tJSmpqacLL7cnaamJkpLS5PaLtGrGq8CisxsDPAl4LkUYhQREekVggqvkpISfvGLXzB58mSuuOIKnnnmGfr165eFCAvDyJEjaWxsZOvWrSnvo62tLeniJ11KS0uTvgI2kcLri8DNwB7gPuD3wHeSjk5ERKQX6OjoYPfu3TELL4BjjjmGO++8k8suu4yvfvWr/Od//meGIywcxcXFjB7ds9FL9fX1Sc2jlW3ddjW6+y53v9ndT3T3qdHnbZkITkREJN1aW1sBAgsvgEsvvZTPfe5z/Nd//VePWmNEDha38DKzuWb2spm1Rh8NZnZNpoITERFJt84bZMcrvADmzp2Lu/P0009nIiwpEIGFl5nNJTJh6peB4cAIIvdnvNHM5mQkOhERkTRLtPCaOnUqAwYMyMt7JEr2xBvj9XngYnd/q8uyJ83sUiK3D7o7zMBEMm3Xrl187WtfO3Az1IqKCv71X/+V4uLiLEcmIumUaOFVXFzM9OnTWbJkSSbCkgIRr/AaeFDRBYC7v2VmA8MLSSQ77r33Xm677TZGjBjB/v372bx5MyeffDKXXnpptkMTkTRKtPACqKmp4Wtf+xrvvfcehx9+eNihSQGIN8Zrd4rrRHJSXV0dEyZM4J133uGdd95h+PDh1NXVZTssEUmzZAsvQOO8JG3iFV4TzOyVGI9XgfGZClAkE9544w2ee+45amtrMTOKioqYM2cOjz32GFu2bMl2eCKSRskUXlOmTGHAgAHqbpS0iVt4AZ+M8bgAmBh+aCKZU1dXR58+fZgz58PrRmpra+no6ODee+/NYmQikm6dhVd5eXm37+0c56UB9pIu8Qqvt919Q9ADwMwsQ3GKhKajo4O77rqLWbNmMWzYsAPLx48fz8knn8yiRYty8nYWIhJbMi1eADNmzGD16tW89957YYYlBSJe4bXEzL5oZqO6LjSzfmZ2lpktBuaGG55I+P74xz+yceNGrr322r9aV1tby6pVq1i2bFkWIhORMCRbeHWO83rqqafCCkkKSLzCaxbQAdxnZpvMbLWZrQfWArOBW929LmhjM7vTzN4zs5Vdlh1mZo+b2drov4PT9DlEUlZXV8fgwYP55Cc/+VfrrrjiCkpLSzXIXiSPtLS0UFxcnPA9GCdPnkxZWZnGeUlaBBZe7t7m7re7+2nAUcDZwGR3P8rdr3f35d3su45I8dbVAuAJdx8DPBF9LZI1e/bs4Ve/+hWzZ8+mpKTkr9YfeuihXHzxxdx33310dHRkIUIRSbegG2QH6RznpSsbJR26vVcjgLu3u/tmd9+e6I7d/Wlg20GLLwQWR58vBi5KdH8iYdi4cSN79uxh6tSpge/55Cc/ybZt21ixYkXmAhOR0CRbeAF8/OMf54033mDfvn0hRSWFIqHCK40q3X1z9PkWoDLDxxf5iI0bNwIwcuTIwPeceeaZALqqSSRPpFJ4jR07lvb2dt56661wgpKCEW/m+lC5u5tZ4KViZjYPmAdQWVkZ+i+9lpYW/WINkM+5eeKJJ4BIARbvMx555JH88pe/ZMqUKR9Zns+56SnlJphyE1um8vL222/j7kkdq7W1FYAHH3yQk08+OaTIgumcCZZzuXH3uA/gOmBMd+8L2LYKWNnl9evAsOjzYcDriexnypQpHrYlS5aEfoxclc+5+Y//+A8HfMeOHXHfN2/ePB84cKC3t7d/ZHk+56anlJtgyk1smcrL6aef7jNmzEhqm/fee88B/8EPfhBSVPHpnAnWG3MDNHhATZNIV+Mo4Cdmtt7MHohOMVGdYp33CB9OQTEXeDjF/YikRWNjI2VlZQwcGP/2ozU1NezcuVPjvETyQCpdjUOGDGHw4MG88cYbIUUlhaLbwsvdv+nuZwHHAc8AXwG6ndTIzO4DngfGmVmjmV0H3ALMNLO1wDnR1yJZs3Hjxrjjuzp1zuOTU83ZIhJTKoWXmTF27Fhef/31kKKSQtFt4WVmXzezx4A/AMcCNwHd/qZy99nuPszdi919pLv/1N2b3P1sdx/j7ue4+8FXPYpkVGNjIyNGjOj2fcOGDWPcuHEqvETyQCqFF8C4cePU4iU9lkhX4yVABfBH4CHgYf/wykSRnJZoixdEWr2eeeYZXU4ukuNSLbzGjh3Lxo0bD8x8L5KKRLoaJxPpFnwRmAm8ambPhh2YSNg6OjrYtGlTQi1e8OE4r+XLu5s7WER6K3fvUYsXwNq1a9MdlhSQRLoajweuJjIY/gpgI/BkyHGJhO7dd9+lo6Mj4RYvzeclkvt27dqFu6fc4gVonJf0SCJdjbcAA4HbgAnuPsPdvxFuWCLh65w8NdEWL43zEsl9yd4gu6sxY8YAaJyX9Ei3E6i6+wWZCEQk0xobG4H4s9Yf7NRTT+XRRx/F3TGzsEITkZD0pPDq378/o0aNUouX9EgiXY1jzOxBM1sdnctrvZmtz0RwImFKtsULYNKkSWzdupXNm3V9iUgu6knhBbqyUXouka7GRcCPgX3ADOAu4J4wgxLJhMbGRoqLixk6dGjC21RXVwNoIlWRHNXTwqtzLq/I5OQiyUuk8Orv7k8A5u4b3H0h8DfhhiUSvo0bNzJ8+HD69En8XvEf+9jHABVeIrkqHS1ezc3NvPvuu+kMSwpI4G8cM/utmY0G9phZH2CtmX3BzC4GUjtjRXqRxsbGpMZ3AQwaNIijjz5aU0qI5Kh0tHiBrmyU1MX7U38R8HvgMWAA8CVgCvAZ4JrwQxMJ18aNG5Ma39WpurpaLV4iOSodLV6gKxsldYGFl7s/AEwm0rr1LHAlsBJ4DjgtI9GJhMTdU2rxgsgA+3Xr1tHc3BxCZCISpp4WXqNGjaKkpEQtXpKy7qaT2Au0AiVAObA/9IhEMmD79u3s3r075RYvgFdeeSXNUYlI2HpaePXp04fx48fT0NCQzrCkgMQb4zULWAEcAkx292+6+7c6H5kKUCQMqczh1UlXNorkrpaWFsyM/v37p7yPSy65hKeeeoq33norfYFJwYg3xutm4NPuvsDdd2UqIJFMSGUOr04jRoygoqJChZdIDuq8T2NPJkCeO3cuAHfddVe6wpICEm+M1+nuviqTwYhkSk9avMyM6upqXdkokoNaWlooLy/v0T6OOuoozjrrLOrq6ti/XyNwJDmJT2CURmY2y8xeN7N1ZrYgGzFIYets8Ro2bFhK20+aNImVK1eyb9++dIYlIiHrbPHqqWuvvZY333yTZ555Jg1RSSHJeOFlZkXAj4DzgInAbDObmOk4pLA1NjZSWVlJv379Utq+urqaPXv28M4776Q5MhEJU7oKr4svvpjy8nLq6up6HpQUlGy0eJ0ErHP39e6+F7gfuDALcUgBS3UOr06dA+zXrVuXpohEJBPSVXgNGDCAyy+/nAceeODAlZIiiehuOokwjAC6NhM0AtOyEMcB//f//l/uvPNODjvssGyG0Wtt27Yt73Lz/PPPc+aZZ6a8/bhx4ygpKWHx4sUsW7YsjZHlj3w8b9JFuYktE3lZvnw5p52Wnqkor732Wn76059y1llnhR63zplgyebmv/7rvw5MhJsN2Si8EmJm84B5AJWVldTX14d2rFdffZWdO3fqr5YA+/fvz7vcjBgxgurq6h6dV5deeinLli3j7bffTl9geSQfz5t0UW5iy0Rehg8fzgknnJCW3ynuzic+8Qneeeed0OPWORMs2dw899xzbN68OcSI4rNM32HdzE4BFrr7udHXXwNw9+8GbTN16lQPe7K6+vp6ampqQj1GrlJugik3wZSbYMpNbMpLMOUmWG/MjZktc/epsdZlY4zXS8AYMxttZv2I3IrokSzEISIiIpJRGW/xAjCz84FbgSLgTnf/l27evxXYEHJYQ4D3Qz5GrlJugik3wZSbYMpNbMpLMOUmWG/MzVHuPjTWiqwUXr2RmTUENQsWOuUmmHITTLkJptzEprwEU26C5VpusjKBqoiIiEghUuElIiIikiEqvD50R7YD6MWUm2DKTTDlJphyE5vyEky5CZZTudEYLxEREZEMUYuXiIiISIao8BIRERHJEBVeIiIiIhmiwktEREQkQ1R4iYiIiGSICi8RERGRDFHhJSIiIpIhfbMdQCKGDBniVVVVoR6jtbWVAQMGhHqMXKXcBFNugik3wZSb2JSXYMpNsN6Ym2XLlr0fdJPsnCi8qqqqaGhoCPUY9fX11NTUhHqMXKXcBFNugik3wZSb2JSXYMpNsN6YGzPbELROXY0iIiIiGaLCS0RERCRDVHiJiIiIZEhOjPESERGR/NPe3k5jYyNtbW0p72PQoEGsWbMmjVElrrS0lJEjR1JcXJzwNiq8REREJCsaGxspLy+nqqoKM0tpH83NzZSXl6c5su65O01NTTQ2NjJ69OiEt1NXo4iIiGRFW1sbFRUVKRdd2WRmVFRUJN1ap8JLREREsiYXi65OqcSeUFejmQ0GhgO7gbfcfX/SRxIRERHpRZqamjj77LMB2LJlC0VFRQwdOpTm5mZGjRrFu+++i5kxb948brzxxrQcM7DwMrNBwA3AbKAfsBUoBSrN7AXgdndfkpYoREREMuz888/noosuYt68edkORbKkoqKCFStWALBw4ULKysq46aab2Lx5M5s3b2by5Mk0NzczZcoUZs6cycSJE3t8zHhdjQ8C7wCnu/s4d5/u7lPd/UjgFuBCM7suaGMzO9LMlpjZajNbZWY3RpcfZmaPm9na6L+De/wpREREkrB3714ee+wxbrjhBl544YVshyO9zLBhw5g8eTIA5eXlTJgwgY0bN6Zl34GFl7vPdPe73X17jHXL3H2+u/80zr73AV9294nAycANZjYRWAA84e5jgCeir0VERDKmubkZgH379nHFFVewbdu2LEckvdVbb73F8uXLmTZtWlr21+0YLzObHGPxDmCDu+8L2s7dNwObo8+bzWwNMAK4EKiJvm0xUA98NamoRUREemDnzp0A/P3f/z3/8z//w9y5c3n44Yfp00fXnGXL/PnzD3T7JaOjo4OioqKY66qrq7n11ltTjqmlpYVLL72UW2+9lYEDB6a8n64SOcNuB14A7gD+B3geeAB43cw+kchBzKwKmAQsBSqjRRnAFqAyyZhFRER6pLPF6+yzz+b73/8+jz76KD//+c+zHJX0Ju3t7Vx66aVcffXVXHLJJWnbbyJXNW4CrnP3VQDR7sJvA/8IPAT8Id7GZlYG/BKY7+47u1566e5uZh6w3TxgHkBlZSX19fUJhJq6lpaW0I+Rq5SbYMpNMOUmmHITWybz8uqrrwLw5ptvMmnSJIYNG8b3vvc9hg0blpHjJytfz5lBgwYdKIK/853vpLSPeC1e8GGR3Z09e/ZQXFxMc3Mz7s7nPvc5jjnmGK6//vq4+2hra0vq/yaRwmtsZ9EF4O6rzWy8u6/vbv4KMysmUnTd6+4PRRe/a2bD3H2zmQ0D3ou1rbvfQaSVjalTp3pNTU0Coaauvr6esI+Rq5SbYMpNMOUmmHITWybzsmvXLgCmT5/OtGnT+Lu/+zsWLlzI6NGjOeqoozISQzLy9ZxZs2ZNj2edT9fM9SUlJZSUlFBeXs6zzz7L/fffzwknnMDpp58OwL/+679y/vnn/9V2paWlTJo0KeHjJFJ4rTKzHwP3R19fAaw2sxKgPWgji1RlPwXWuPsPuqx6BJhL5MrIucDDCUcrIiKSBp1jvDrH7VxzzTV885vf5K677uKf//mfsxmaZMnChQsPPJ8+fTruMTvkeiyRMV61wDpgfvSxPrqsHZgRZ7vTgDnAWWa2Ivo4n0jBNdPM1gLnRF+LiIhkTGfXUWfhVVVVxYwZM6irqwvtF64IJNDi5e67zex24FF3f/2g1S1xtnsWCOqLPDvxEEVERNLr4BYvgGuvvZZrrrmGZ5999kD3kki6ddviZWafAlYAv4u+rjazR0KOS0REJDSdhdeAAQMOLLvkkksoKyujrq4uS1FJIUhkjNc3gZOIzLeFu68ws9FhBiWSSbfccgvr168HoKysjAULFnD44YdnOSoRCdPOnTspLy//yLxdAwYM4PLLL+cXv/gFt91220eKMgmPu+fsjbJT6ZZOpPBqd/cdByVFHeCSF3bs2MHXvvY1Bg4cyIABA9i6dSsrVqzg8ccfj3t5sojktp07d8acEPOqq67izjvvZMmSJVxwwQVZiKywlJaW0tTUREVFRc4VX+5OU1MTpaWlSW2X6FWNVwFFZjYG+BLwXAoxivQ6nbcJue2225g7dy51dXVce+21fPvb3+Zb3/pWlqMTkbA0NzfHLLxOO+00SkpKqK+vV+GVASNHjqSxsZGtW7emvI+2traki590KS0tZeTIkUltk0jh9UXgZmAPcB/weyC1Wc5EepnOwmvw4Mi92mtra6mvr+c73/kO06dPZ+bMmdkMT0RC0tnVeLDS0lJOPvnkvJystDcqLi5m9OiejV6qr69Pah6tbOt2cL2773L3m939RHefGn3elongRML2wQcfAHDYYYcdWPajH/2ICRMmcPXVV7N9+/YsRSYiYQrqagSoqalh+fLl+v5LKOIWXmY218xeNrPW6KPBzK7JVHAiYTu4xQsiA2zvvPNOtm7dqnu3ieSp7gqv/fv388wzz2Q4KikEgYWXmc0lMmHql4HhwAgi92e80czmZCQ6kZDFavECOOmkkzj++ON1WblIngoa4wVw8sknHxjnJZJu8Vq8Pg9c7O5L3H2Hu2939yeBS4EbMhOeSLhitXgBmBm1tbW88MILrFmzJhuhiUiIgsZ4QWSc1ymnnKLCS0IRr/Aa6O5vHbwwuiz2nwkiOeaDDz6gf//+Ma+IufrqqykqKmLx4sVZiExEwuLucbsaQeO8JDzxCq/dKa4TyRnbtm37q9auTkcccQTnn38+d999Nx0dHRmOTETCsmvXLvbv399t4eXuPP300xmMTApBvMJrgpm9EuPxKjA+UwGKhGnbtm1/Nb6rq9raWjZt2sQf/vCHDEYlImE6+AbZsUybNk3jvCQU8ebxmpCxKESy5IMPPghs8QK44IILqKiooK6ujvPOOy+DkYlIWDrv0xg0xgsi47xOPfVUFV6SdvFavN529w1BDwDLtfn9RQ7SXYtXv379mD17Ng8//DC7d6uHXSQfdBZe8Vq8AM466yxWrFjBxo0bMxGWFIh4hdcSM/uimY3qutDM+pnZWWa2GJgbbngi4equxQtg1qxZ7NmzhxdeeCFDUYlImBItvK688krcnXvuuScTYUmBiFd4zQI6gPvMbJOZrTaz9cBaYDZwq7vXBW1sZnea2XtmtrLLssPM7HEzWxv9N/5vPJGQddfiBTB9+nT69OmjLgeRPJHIGC+AY489ltNPP526ujrcPROhSQEILLzcvc3db3f304CjgLOBye5+lLtf7+7Lu9l3HZHirasFwBPuPgZ4IvpaJCv27t1La2trty1egwYNYvLkySq8RPJEImO8OtXW1vLaa6+xdOnSsMOSAtHtvRoB3L3d3Te7+/ZEd+zuTwPbDlp8IdA5KdJi4KJE9yeSbkGz1sdSU1PDCy+8oHFeInkg0a5GgE9/+tMccsghuouFpE1ChVcaVbr75ujzLUBlho8vckBn4dVdixdECq+9e/fy/PPPhx2WiIQsmcKrvLycSy+9lPvvv19/eElaxJtOIlTu7mYW2GluZvOAeQCVlZWhd/O0tLSoKylAvuZm5crI8MN33nknoc/Xp08f6urq6NPnw79X8jU36aDcBFNuYstUXlauXEnfvn15/vnnSeTi/Orqau6++26++93vctZZZ4UeXyw6Z4LlXG7cPe4DuA4Y0937AratAlZ2ef06MCz6fBjweiL7mTJliodtyZIloR8jV+Vrbh599FEHfOnSpQm9/8QTT/TTTz/9I8vyNTfpoNwEU25iy1RebrjhBj/ssMMSfn9HR4cfddRRfu6554YYVXw6Z4L1xtwADR5Q0yTS1TgK+ImZrTezB6JTTFSnWOc9wodTUMwFHk5xPyI91nmD7ETGeEGku3Hp0qXs2rUrzLBEJGTd3afxYH369OHCCy/k2Wef1e3DpMe6Lbzc/ZvufhZwHPAM8BVgWXfbmdl9wPPAODNrNLPrgFuAmWa2Fjgn+lokK5IZ4wUfjvPSfF4iuS3Zwgtg0qRJtLa28pe//CWkqKRQdDvGy8y+DpwGlAHLgZuIFGBxufvsgFVnJxOgSFg6W7wOPfTQhN7fOZ/XkiVLsjbOQ0R6LpXCq7q6GoDly5czduzYEKKSQpFIV+MlQAXwR+Ah4GH/8MpEkZz1wQcfMGjQIIqKihJ6/8CBA5kyZUpuDeIUkb/S3Nyc0BxeXU2cOJHi4mJWrFgRTlBSMBLpapxMpFvwRWAm8KqZPRt2YCJhS2TW+oPNmDFD47xEclwqLV79+vVj4sSJKrykx7otvMzseOBqIoPhrwA2Ak+GHJdI6BK5T+PBampqaG9v13xeIjkslcILIuO8VHhJTyXS1XgLMBC4DZjg7jPc/RvhhiUSvlRavE477TSKiorU3SiSw1ItvKqrq9myZQtbtmwJISopFIl0NV7g7v/m7s+5e3smghLJhG3btiXd4qVxXiK5raOjg127diU9xgs+HGCvVi/piUS6GseY2YNmtjo6l9d6M1ufieBEwvTBBx8k3eIFms9LJJc1NzcDid0u6GAf//jHARVe0jOJdDUuAn4M7ANmAHcB94QZlEjY3D2lFi+IDLBvb2/nueeeCyEyEQlTMvdpPNihhx5KVVWVCi/pkUQKr/7u/gRg7r7B3RcCfxNuWCLham1tZd++fSm1eGmcl0ju6knhBRpgLz0XWHiZ2W/NbDSwx8z6AGvN7AtmdjGRyVRFclbn5KmptHiVl5czdepUFV4iOaizqzGVMV4QGef1xhtv0Nrams6wpIDEa/FaBPweeAwYAHwJmAJ8Brgm/NBEwtN5u6BUWrwgMs7rxRdfZPfu3ekMS0RC1tMWr+rqatydV155JZ1hSQEJLLzc/QFgMpHWrWeBK4GVwHNEbiEkkrN60uIFH87ntWrVqnSGJSIhS0fhBRpgL6nr7l6Ne4FWoAQoB/aHHpFIBvS0xWv69OkUFRWxfPnydIYlIiHraeF15JFHMnjwYH33JWWBhZeZzQJ+ADwCTHZ3XTsveaOnLV5lZWWcccYZPPXUU7g7ZpbO8EQkJD0d42VmnH766Tz66KPs27ePvn27a78Q+ah4Y7xuBj7t7gtUdEm+6WmLF8DcuXPZuHGjppUQySGdLV6pFl4Q+e5v3ryZxx9/PF1hSQGJN8brdHfXABbJS9u2baNv374MGDAg5X1ceumllJaWUldXl77ARCRUO3fu5JBDDulRS9UFF1xARUWFvvuSkkTm8RLJO52z1veki7CsrIyamhp+/vOf69JykRyR6n0au+rXrx9XXXUVv/71rw8MWxBJVFYKLzObZWavm9k6M1uQjRiksKU6a/3Bzj33XJqbm/nVr36VhqhEJGw7d+7sUTdjp2uvvZa9e/dy//33pyEqKSQZL7zMrAj4EXAeMBGYbWYTMx2HFLZU79N4sI997GMcffTR6nIQyRHNzc09bvGCyLQSH/vYx/Tdl6Rl43KMk4B17r4ewMzuBy4EVmchFgA2b97Mm2++ydChQ7MVQq+Wj7nZvHkzRx11VI/306dPH+bOncvChQt56qmnGDJkSBqiyw/5eN6ki3ITWyby8u6776al8DIzamtr+T//5//w2GOPMWrUqDREF0znTLBkczN69GgOOeSQECPqhrtn9AFcBvxvl9dzgB/G22bKlCkephtvvNEBPQrs8dnPfrbH586SJUv8zTffdDPL+ufRQw89EntcfvnlafjN4f7uu+96cXFx1j+PHsk9li5dmpb//3iABg+oaSyyPnPM7DJglrv/bfT1HGCau3/hoPfNA+YBVFZWTgmzH33dunWsW7eO0tLS0I6Ry9ra2vIyNx//+Md7PM6rpaWFsrIyVq1axdatW9MUWX7I1/MmHZSb2DKVl+OPPz5trdOvvfYaW7ZsScu+4tE5EyzZ3EyePDktrZ7xzJgxY5m7T421LhuF1ynAQnc/N/r6awDu/t2gbaZOneoNDQ2hxlVfX09NTU2ox8hVyk0w5SaYchNMuYlNeQmm3ATrjbkxs8DCKxtXNb4EjDGz0WbWj8g9IB/JQhwiIiIiGZXxFi8AMzsfuBUoAu5093/p5v1bgQ0hhzUEeD/kY+Qq5SaYchNMuQmm3MSmvARTboL1xtwc5e4xR/xnpfDqjcysIahZsNApN8GUm2DKTTDlJjblJZhyEyzXcqOZ60VEREQyRIWXiIiISIao8PrQHdkOoBdTboIpN8GUm2DKTWzKSzDlJlhO5UZjvEREREQyRC1eIiIiIhmiwktEREQkQ1R4iYiIiGSICi8RERGRDFHhJSIiIpIhKrxEREREMkSFl4iIiEiG9M12AIkYMmSIV1VVhXqM1tZWBgwYEOoxcpVyE0y5CabcBFNuYlNegik3wXpjbpYtW/Z+0E2yc6LwqqqqoqGhIdRj1NfXU1NTE+oxcpVyE0y5CabcBFNuYlNegik3wXpjbsxsQ9A6dTWKiIiIZIgKLxEREZEMUeElIiIikiE5McZLRERE8lN7ezuNjY20tbWltP2gQYNYs2ZNmqNKTGlpKSNHjqS4uDjhbVR4iYiISNY0NjZSXl5OVVUVZpb09s3NzZSXl4cQWXzuTlNTE42NjYwePTrh7dTVKCIiIlnT1tZGRUVFSkVXNpkZFRUVSbfUqfASERGRrMq1oqtTKnEn1NVoZoOB4cBu4C1335/0kUR6qZdeeonjjz+e/v37ZzsUEcmg5cuXc+yxx2alm0p6h6amJs4++2wAtmzZQlFREUOHDqWtrY1DDjmEjo4O9u3bx2WXXca3vvWttBwzsPAys0HADcBsoB+wFSgFKs3sBeB2d1+SlihEsmT37t2cdtppfPGLX+T73/9+tsMRkQxxd6ZPn87VV1/NHXfcke1wJEsqKipYsWIFAAsXLqSsrIybbroJd6e1tZWysjLa29uZPn065513HieffHKPjxmvq/FB4B3gdHcf5+7T3X2qux8J3AJcaGbXBW1sZkea2RIzW21mq8zsxujyw8zscTNbG/13cI8/hUiKtm/fTnt7O3fffTft7e3ZDkdEMmTXrl3s2rWL+++/n127dmU7HOllzIyysjIgctVle3t72rpDA1u83H1mnHXLgGXd7Hsf8GV3f9nMyoFlZvY4UAs84e63mNkCYAHw1aQjF0mDnTt3ArB161Yee+wxPvWpT2U5IhHJhM7vfnNzM7/61a+4+uqrsxyRAMyfP/9AC1SiOjo6KCoqClxfXV3NrbfemnQsHR0dTJkyhXXr1nHDDTcwbdq0pPcRS7eD681scozHMWYWd3yYu29295ejz5uBNcAI4EJgcfRti4GLevQJRHqg84cvwKJFi7IYiYhkkr770p2ioiJWrFhBY2MjL774IitXrkzLfhMZXH87MBl4BTDgeGAVMMjMPu/uf+huB2ZWBUwClgKV7r45umoLUJlC3CJp0fnDd+rUqTz66KNs3bqVoUNj3lBeRPJI1+/+k08+yYYNGzjqqKOyHJWk0jIV9jxehx56KDNmzOB3v/sdxx9/fI/3l0jhtQm4zt1XAZjZRODbwD8CDwFxCy8zKwN+Ccx3951d+0jd3c3MA7abB8wDqKyspL6+PoFQU9fS0hL6MXJVPufm+eefB2DmzJk0NDTwrW99i8suuyzh7fM5Nz2l3ARTbmLLZF5efvllAM455xwaGhr49re/zZw5czJy7FTk8zkzaNAgmpubU96+o6OjR9t32rNnD8XFxTQ3N/P+++/Tt29fDj30UHbv3s3vfvc75s+fH/M4bW1tSf3fJFJ4je0sugDcfbWZjXf39d0NNDOzYiJF173u/lB08btmNszdN5vZMOC9WNu6+x3AHQBTp071mpqaBEJNXX19PWEfI1flc27efvttAK677joef/xx/vSnP/HDH/4w4e3zOTc9pdwEU25iy2Retm/fDsAVV1zB0qVLeeqpp/jf//3fXjufVD6fM2vWrOlRi1W6WrxKSkooKSmhvLycN998k7lz59LR0cH+/fu5/PLLufzyy2NuV1payqRJkxI+TiKF1yoz+zFwf/T1FcBqMysBAi8Ds8jZ+1Ngjbv/oMuqR4C5RK6MnAs8nHC0ImnW2d1QXl5ObW0tX/jCF1ixYgXV1dXZDUxEQnXwd3/u3Lk8++yznH766VmOTLJl4cKFB55/7GMfY/ny5aEcJ5GZ62uBdcD86GN9dFk7MCPOdqcBc4CzzGxF9HE+kYJrppmtBc6JvhbJis4fvgMHDmT27Nn069ePurq67AYlIqHr+t2/9NJLKSsr03dfMqLbwsvddxMZYL/A3S929++5+y533+/uLXG2e9bdzd0/5u7V0cdv3b3J3c929zHufo67b0vnBxJJRnNzM8XFxZSUlHDYYYdx4YUXcu+997J3795shyYiIeocqzNw4EAGDBjA5Zdfzi9+8QtaW1uzHJnku0Smk/gUsAL4XfR1tZk9EnJcIhmxc+dOBg4ceGBcR21tLe+//z6/+c1vshyZiIRp586d9OvXj5KSEiDy3W9paeGXv/xlliOTfJdIV+M3gZOA7QDuvgIYHV5IIpnTWXh1+sQnPsGwYcPU5SCS53bu3PmRAdnTp0/nmGOO0Xc/S9xjTnDQ66USdyKFV7u77zj4WEkfSaQXOvhqmL59+zJnzhx+85vf8O6772YxMhEJ08F/dJkZtbW1LFmyhDfffDOLkRWe0tJSmpqacq74cneampooLS1NartEr2q8CigyszHAl4DnUohRpNc5+IcvwNy5c/n3f/93fvazn/EP//APWYpMRMLU3Nz8V9/9a665hm984xvcddddfPOb38xSZIVn5MiRNDY2snXr1pS2b2trS7r4SZfS0lJGjhyZ1DaJFF5fBG4G9gD3Ab8HvpN0dCK90M6dO6ms/OjNEyZOnMhJJ53EokWLmD9/fq+d10dEUhfrj65Ro0Zx9tlns3jxYv75n/+ZPn0S6RSSniouLmb06NRHMNXX1yc1j1a2JXJV4y53v9ndT3T3qdHnbZkITiRssX74Alx77bW8+uqroc3jIiLZdfAYr061tbW8+eabPP3001mISgpB3MLLzOaa2ctm1hp9NJjZNZkKTiRsQTMeX3HFFZSUlGigrUieCvqj6+KLL2bgwIH67ktoAgsvM5tLZMLULwPDgRFE7s94o5n13htaiSQh6Ifv4MGDueiii7j33nvZs2dPFiITkTAFffcPOeQQrrjiCh588EFaWgKnqhRJWbwWr88DF7v7Enff4e7b3f1J4FLghsyEJxKeffv2sWvXrpg/fCHS3bht2zYeffTRDEcmImGLNbi+U21tLa2trTzwwAMZjkoKQbzCa6C7v3Xwwuiy2GerSA7pOnN1LOeccw7Dhw9Xl4NInun8oyvoxsqnnHIKY8eO1XdfQhGv8Nqd4jqRnNBZeAX98C0qKuKaa67hscceY8uWLZkMTURC1N0fXZ1zej399NP85S9/yWRoUgDiFV4TzOyVGI9XgfGZClAkLF1vkhuktraWjo4O7rnnnkyFJSIhS+S7P2fOHPr06cPixYszFZYUiLiFF/DJGI8LgInhhyYSrkR++I4bN45TTjlFXQ4ieaS7Fi+ITOo5c+ZMFi9enHMzqkvvFq/wetvdNwQ9AEwzS0oOS6TwArj88stZtWoVb7/9dibCEpGQdX73g4YZdLr88st5++23Wb16dSbCkgIRr/BaYmZfNLNRXReaWT8zO8vMFgNzww1PJDyJ/NULMGPGDCAyO7KI5L5E/+jSd1/CEK/wmgV0APeZ2SYzW21m64G1wGzgVnevC9rYzO40s/fMbGWXZYeZ2eNmtjb67+A0fQ6RpCX6V+8JJ5zAYYcdph++Inki0cJr9OjRHHXUUfruS1oFFl7u3ubut7v7acBRwNnAZHc/yt2vd/fu7qVSR6R462oB8IS7jwGeiL4WyYpEf/j26dOHM888Uz98RfJEoq3dADU1NdTX12ucl6RNQncAdfd2d9/s7tsT3bG7Pw1sO2jxhUDnJSKLgYsS3Z9IuiXa4gWRH75vvvkmGzZsCDssEQlZst/9999/X+O8JG0yfev1SnffHH2+BajM8PFFDmhubmbAgAEUFRV1+96amhoAnnrqqZCjEpGwJVt4ASxZsiTMkKSA9M3Wgd3dzSyw7dbM5gHzACorK0Pv5mlpaVFXUoB8zc1rr71GSUlJQp9t//79DBw4kPvuu49Roz683iRfc5MOyk0w5Sa2TOVl1apVlJaW8swzzyT0/srKSh544AGOP/74kCMLpnMmWM7lxt3jPoDrgDHdvS9g2ypgZZfXrwPDos+HAa8nsp8pU6Z42JYsWRL6MXJVvubmiiuu8LFjxyb8/ksuucSrqqo+sixfc5MOyk0w5Sa2TOXl+uuv92HDhiX8/traWh8yZIh3dHSEGFV8OmeC9cbcAA0eUNMk0tU4CviJma03sweiU0xUp1jnPcKHU1DMBR5OcT8iPbZz586EBtd2qqmp4a233uKtt94KLygRCd3OnTsT6mbspHFekk7dFl7u/k13Pws4DngG+AqwrLvtzOw+4HlgnJk1mtl1wC3ATDNbC5wTfS2SFc3NzUkXXqBxXiK5Ltk/us4880xA47wkPbotvMzs62b2GPAH4FjgJmBkd9u5+2x3H+buxe4+0t1/6u5N7n62u49x93Pc/eCrHkUyJtm/eo877jgqKipyayyBiPyVZAuvqqoqqqqq9N2XtEikq/ESoAL4I/AQ8LB/eGWiSM5K9odv53xe+qtXJLcl29oNkRbvp556iv3794cUlRSKRLoaJxPpFnwRmAm8ambPhh2YSNiSLbwg8sN3w4YNGuclksOSbe2GyHe/qamJVatWhRSVFIpEuhqPB64mMhj+CmAj8GTIcYmEyt1T+qtX924TyX2p/tEFGuclPZdIV+MtwEDgNmCCu89w92+EG5ZIuPbs2UN7e3vSP3wnTpzIkCFDVHiJ5Ch3T6nwOuqooxg9erS++9Jj3U6g6u4XZCIQkUxKZubqrnTfRpHc1tbWxr59+5IuvCDS6vXwww+zf/9++vTJ9I1fJF8k0tU4xsweNLPV0bm81pvZ+kwEJxKWRG+QHYvGeYnkrs4bZCf7RxdEvvvbtm1j5cqV6Q5LCkgiJfsi4MfAPmAGcBdwT5hBiYSt84dvqoUXaKyHSC7q6R9doDGe0jOJFF793f0JwNx9g7svBP4m3LBEwtWTH77HHXecxnmJ5KiefPdHjRrF0UcfrT+6pEcCCy8z+62ZjQb2mFkfYK2ZfcHMLgbKMhahSAhSHeMFYGbU1NRQX1/feQ9SEckRPSm8QPN5Sc/Fa/FaBPweeAwYAHwJmAJ8Brgm/NBEwpOOH75vv/02W7ZsSWdYIhKynozxgsh3/4MPPuDVV19NZ1hSQAILL3d/AJhMpHXrWeBKYCXwHHBaRqITCUlPxnjBh2M9VqxYkaaIRCQTevpHV+d9GzXUQFLV3RivvUArUAKUEynCyqLPRXJWT3/4Tpw4kcMPP5wXX3wxnWGJSMh6+t0fNWoUxxxzDL/73e/SGZYUkHhjvGYBK4BDgMnu/k13/1bnI1MBioRh586dFBUV0b9//5S2NzM+/elP89xzz7F9+/b0Bicioelp4QXw6U9/mscff5zNm3XbYklevBavm4FPu/sCd9+VqYBEMqHzXm1mlvI+amtr2bt3L7/4xS/SGJmIhKm5uZk+ffqk/EcXwNy5c+no6ODee+9NY2RSKOKN8Trd3XU3UMlLqdyn8WBTpkyhqqqKRYsWpSkqEQlb5+2CevJH1/jx4zn55JNZtGiRrmyWpGXlngdmNsvMXjezdWa2IBsxSGFL5V5tBzMzZs2axQsvvMBrr72WpshEJEzp+O4DXHvttaxevZqGhoY0RCWFJOOFl5kVAT8CzgMmArPNbGKm45DClq4fvjNnzqSoqIjFixenISoRCVu6vvuXX345paWl1NXV9TwoKSjZaPE6CVjn7uvdfS9wP3BhFuKQAtbc3JzyPD5dHXbYYZx33nncdddddHR0pCEyEQlTur77hx56KBdffDE/+9nPaGtrS0NkUigs0/3TZnYZMMvd/zb6eg4wzd2/ELTN1KlTPczm3Pvuu4+77rqLYcOGhXaMXLZ58+a8y82vfvUrZs6c2eOB8fX19TQ1NXHZZZdx0UUXMXjw4DRFmPvy8bxJF+Umtkzk5dFHH2Xy5MlpmQ7i8ccf5xOf+AR/8zd/w+GHH56G6ILpnAmWbG6+/vWvc/TRR4cYEZjZMnefGmtd31CP3ANmNg+YB1BZWRnqZHW///3vaWhooE+frAx56/X279+fd7np168fI0eO7PF51dLSQnl5OSeccALPPfdceoLLE/l43qSLchNbJvJiZhx99NFp+Z3Sp08fpkyZwksvvdTzwLqhcyZYsrk59dRTefvtt0OMKL5stHidAix093Ojr78G4O7fDdom7BYviLRcdM5GLh+l3ARTboIpN8GUm9iUl2DKTbDemJt4LV7ZKJ9fAsaY2Wgz60fkVkSPZCEOERERkYzKeIsXgJmdD9wKFAF3uvu/dPP+rcCGkMMaArwf8jFylXITTLkJptwEU25iU16CKTfBemNujnL3obFWZKXw6o3MrCGoWbDQKTfBlJtgyk0w5SY25SWYchMs13KjkXoiIiIiGaLCS0RERCRDVHh96I5sB9CLKTfBlJtgyk0w5SY25SWYchMsp3KjMV4iIiIiGaIWLxEREZEMUeElIiIikiEqvEREREQyRIWXiIiISIao8BIRERHJEBVeIiIiIhmiwktEREQkQ/pmO4BEDBkyxKuqqkI9RmtrKwMGDAj1GLlKuQmm3ARTboIpN7EpL8GUm2C9MTfLli17P+gm2TlReFVVVdHQ0BDqMerr66mpqQn1GLlKuQmm3ARTboIpN7EpL8GUm2C9MTdmtiFonboaRURERDJEhZeIiIhIhqjwEhEREcmQnBjjJSIiIvmpvb2dxsZG2traUtp+0KBBrFmzJs1RJaa0tJSRI0dSXFyc8DYqvERERCRrGhsbKS8vp6qqCjNLevvm5mbKy8tDiCw+d6epqYnGxkZGjx6d8HbqahQREZGsaWtro6KiIqWiK5vMjIqKiqRb6lR4iYiISFblWtHVKZW41dUoIiIiBampqYmzzz4bgC1btlBUVMTQoZF5T1988UWKioqYOnUqI0aM4NFHH03LMRMqvMxsMDAc2A285e7703J0kSzbv38/11xzDZ/5zGeYNWtWtsMREZEMqqioYMWKFQAsXLiQsrIybrrppgPrf/CDHzBhwgR27tyZtmMGdjWa2SAz+yczexV4AfgJ8Atgg5k9YGYz4u3YzI40syVmttrMVpnZjdHlh5nZ42a2Nvrv4LR9GpEkbd++nXvvvZcrr7yS9evXZzscERHpJRobG/nNb37D3/7t36Z1v/HGeD0IvAOc7u7j3H26u0919yOBW4ALzey6ONvvA77s7hOBk4EbzGwisAB4wt3HAE9EX4tkRXNzMwA7duzg8ssvZ8+ePVmOSEREeoP58+fz7//+7/Tpk97h8IFdje4+M866ZcCyeDt2983A5ujzZjNbA4wALgRqom9bDNQDX00maJF06Ww+/sxnPsM999zDV77yFW677bYsRyUiUpjmz59/oOsvUR0dHRQVFQWur66u5tZbb01qn48++iiHH344U6ZMob6+Pqltu9PtGC8zmxxj8Q5gg7vvS+QgZlYFTAKWApXRogxgC1CZWKgi6de18Bo6dCj/+Z//yaWXXsqZZ56Z5chERCRb/vSnP/HII4/w29/+lra2Nnbu3HngD/SeMneP/wazF4DJwCuAAccDq4BBwOfd/Q/dbF8GPAX8i7s/ZGbb3f3QLus/cPe/GudlZvOAeQCVlZVT7r///mQ+V9JaWlooKysL9Ri5Kp9zs3TpUhYsWMAPf/hDxowZw2WXXcZJJ53E17/+9YS2z+fc9JRyE0y5iU15CZbPuRk0aBDHHntsytt31+KVqH/913+lrKyML33pSx9Z/swzz3DbbbfxwAMPxNxu3bp17Nix4yPLZsyYsczdp8Z6fyJXNW4CrnP3VQDRcVrfBv4ReAgILLzMrBj4JXCvuz8UXfyumQ1z981mNgx4L9a27n4HcAfA1KlTvaamJoFQU1dfX0/Yx8hV+Zyb996LnH41NTUcd9xxzJkzhzvvvJNJkyYxaNCgbrfP59z0lHITTLmJTXkJls+5WbNmTY9mnk/XzPUlJSWUlJT81b4OOeQQ+vbtG3iM0tJSJk2alPBxEhkxNraz6AJw99XAeHePewmYRWYV+ymwxt1/0GXVI8Dc6PO5wMMJRyuSZp1djQMHDgSgtraWtrY2fvGLX2QzLBERybCFCxd+ZCqJTjU1NWmbwwsSK7xWmdmPzezM6ON2YLWZlQDtcbY7DZgDnGVmK6KP84lcETnTzNYC50Rfi2RFZ+HV+ZfM1KlTmThxIosWLcpmWCIikqcS6WqsBf4emB99/SfgJiJFV+BcXu7+LJExYbGcnXCEIiE6uPAyM6699lq+8pWv8PrrrzNu3LhshiciInmm2xYvd98N3A4scPeL3f177r7L3fe7e0v4IYqEp7m5mQEDBnxkYObVV19NUVERixcvzmJkIiKSjxKZTuJTwH8A/YDRZlYNfNvdPxVybCKh27lz54HxXZ2GDRvGrFmzuOuuu/jOd76TlqtlRKT3uf/++3nttdeAyADq66+/nsGDdTOVbHD3nLxRdnczQ8SSSFfjN4GTiEx0iruvMLPRSR9JpBeKVXhBZF6v3/zmN7z00kucfPLJWYhMRMLU0dHBZz7zGTo6Og4se+KJJ3jsscfSPlO5xFdaWkpTUxMVFRU5VXy5O01NTZSWlia1XSKFV7u77zgoGcmXeCK90M6dO2NeInzWWWcB8NRTT6nwEslD27dvp6Ojg1tvvZUbb7yRO+64g8997nN897vf5eabb852eAVl5MiRNDY2snXr1pS2b2trS7r4SZfS0lJGjhyZ1DaJFF6rzOwqoMjMxgBfAp5LIT6RXqe5uTlmi9fhhx/Occcdx5IlS/jqV3VHK5F888EHHwBw2GGHAXD99ddTX1/PN77xDaZPn667V2RQcXExo0en3pFWX1+f1Dxa2ZZIe+oXgeOAPcB9wE4+vMJRJKcFdTVCZO6WZ599lvb2eLOmiEgu2rZtG8CBMV1mxk9+8hOOPfZYZs+ezfbt27MYneSzRK5q3OXuN7v7ie4+Nfq8LRPBiYStu8KrtbWVZcvi3g9eRHLQwS1eEJlW5u6772bz5s3ce++92QpN8lzcwsvM5prZy2bWGn00mNk1mQpOJGxBY7yAA10N6b4zvYhk38EtXp1OOukkqqurqaury0JUUggCCy8zm0ukS/HLwHBgBJH7M95oZnMyEp1IiNw9cIwXwNChQzn++ONZsmRJhiMTkbDFavHqVFtbS0NDAytXrsx0WFIA4rV4fR642N2XuPsOd9/u7k8ClwI3ZCY8kfC0tbWxb9++wMILNM5LJF8FtXhBZBLl4uJitXpJKOIVXgPd/a2DF0aXBf+mEskRB98gO5aamhp27dpFQ0NDpsISkQz44IMPGDBgAP369furdUOGDOGCCy7gnnvu0R9dknbxCq/dKa4TyQmJFF5nnHEGoHFeIvlm27ZtcWepr62t5d133+X3v/99BqOSQhCv8JpgZq/EeLwKjM9UgCJhaW5uBggcXA8fjvNS4SWSXz744IOY47s6nXfeeRx++OEsWrQog1FJIYg3geqEjEUhkgWJtHgBzJgxg//93/+lpaWFsrKyTIQmIiHrrsWruLiYq6++mh/+8Ic0NzfH/QNNJBnxWrzedvcNQQ8Ay6WbKokcJNHC64orrmD37t08+OCDmQhLRDKguxYvgPPPP5/29naee043a5H0iVd4LTGzL5rZqK4LzayfmZ1lZouBueGGJxKeRAuvU089lTFjxugKJ5E80l2LF8App5xCcXGxppSRtIpXeM0COoD7zGyTma02s/XAWmA2cKu71wVtbGZ3mtl7Zrayy7LDzOxxM1sb/Tf+WS8SokTGeEHkViK1tbU89dRTrF+/PhOhiUjIEmnxGjBgACeddJLGeEpaBRZe7t7m7re7+2nAUcDZwGR3P8rdr3f35d3su45I8dbVAuAJdx8DPBF9LZIVibZ4AcyZMwczY/HixWGHJSIha2trY/fu3d22eEFkjGdDQ8OBP9REeiqRm2Tj7u3uvtndtye6Y3d/Gth20OILgc7fXIuBixLdn0i67dy5k6KiIvr379/te4888kjOOeccFi9ezP79+zMQnYiEJd6s9Qerqamho6ODP/3pT2GHJQUiocIrjSrdfXP0+RagMsPHFzmg8wbZiV4jUltby4YNG3jqqadCjkxEwhRv1vqDaZyXpFu86SRC5e5uZh603szmAfMAKisrQ+9jb2lpUT9+gHzNzdq1a+nXr1/Cn+2www5jwIABfPe73z1QrOVrbtJBuQmm3MSWqby8+uqrALzzzjsJHW/8+PE88sgjnHfeeSFHFkznTLCcy427x30A1wFjuntfwLZVwMour18HhkWfDwNeT2Q/U6ZM8bAtWbIk9GPkqnzNzcUXX+zHH398UttcddVVPmLEiAOv8zU36aDcBFNuYstUXh5++GEH/KWXXkro/V//+te9qKjId+zYEXJkwXTOBOuNuQEaPKCmSaSrcRTwEzNbb2YPRKeYqE6xznuED6egmAs8nOJ+RHqss6sxGZMnT2bjxo1s3bo1pKhEJGzJjPGCyAB7jfOSdOm28HL3b7r7WcBxwDPAV4Bl3W1nZvcBzwPjzKzRzK4DbgFmmtla4Jzoa5GsSKXwqq6uBuDPf/5zCBGJSCYkM8YL4OSTT05qWIJIPN2O8TKzrwOnAWXAcuAmIgVYXO4+O2DV2ckEKBKW5uZmRo8endQ2H//4xwFYvnw555xzThhhiUjIPvjgA8yMQYMGJfT+Qw45hGnTpmmAvaRFIl2NlwAVwB+Bh4CH/cMrE0Vy1s6dO5O+/9qQIUMYOXIkK1asCCcoEQndtm3bOPTQQ+nTJ/EL+2tqali2bNmB+f9EUpVIV+NkIt2CLwIzgVfN7NmwAxMJWypdjQCTJk1S4SWSwxKZtf5gNTU17N+/n2ef1a8/6ZluCy8zOx64mshg+CuAjcCTIcclEqr9+/fT0tKSUuFVXV3Na6+9xu7du0OITETClsh9Gg92yimnaJyXpEUi83jdQmRM123AS+7eHm5IIuFraWkBErtd0MGqq6vZv38/K1eu7P7NItLrpNLi1b9/f04++WSN85IeS6Sr8QJ3/zd3f05Fl+SLznEayY7xgg+vbFR3o0huSqXFCyLdjS+//DI7duwIISopFIl0NY4xswfNbHV0Lq/1ZrY+E8GJhCWZG2QfrKqqioEDB6rwEslRqbR4gcZ5SXokcknHIuDHwD5gBnAXcE+YQYmErbm5GUit8OrTpw/V1dUqvERykLvzwQcfpNTipfm8JB0SKbz6u/sTgLn7BndfCPxNuGGJhKsnLV4Q6W7885//TEdHRzrDEpGQNTc309HRkVKLl8Z5SToEFl5m9lszGw3sMbM+wFoz+4KZXUxkMlWRnJWOwqu1tZVNmzalMywRCVmys9YfbMaMGSxfvpzt27enMSopJPFavBYBvwceAwYAXwKmAJ8Brgk/NJHw9GRwPXw4wH7dunXpCklEMiDZ+zQeTOO8pKcCCy93fwCYTKR161ngSmAl8ByRWwiJ5KyejPECmDhxIn379lXhJZJjetridfLJJ1NSUqLuRklZd2O89gKtQAlQTqQIK4s+F8lZPW3xKikp4cQTT+SFF17A3dMZmoiEqKctXqWlpZxxxhn86le/Yv/+/ekMTQpEvDFes4AVwCHAZHf/prt/q/ORqQBFwrBz50769+9PcXFxyvuYM2cO69ev19WNIjmkpy1eANdccw1vvvkmzzzzTLrCkgISr8XrZuDT7r7A3XdlKiCRTEjlBtkHu/LKKykuLqauri49QYlI6Hra4gVwySWXUF5ezqJFi9IVlhSQeGO8Tnf3VZkMRiRTmpubUx7f1Wnw4MGcdtpp3HvvvezduzdNkYlImLZt20a/fv3o379/yvs45JBDuOKKK3jwwQcP3H5MJFGJzOOVdmY2y8xeN7N1ZrYgGzFIYdu5c2ePCy+A8847j6amJh599NE0RCUiYeuctd7MerSf2tpaWltbefDBB9MUmRSKjBdeZlYE/Ag4D5gIzDaziZmOQwpbugqvKVOmMHz4cHU5iOSIVO/TeLBTTz2VMWPG6LsvSeubhWOeBKxz9/UAZnY/cCGwOguxALBr1y6am5sP9P3LR+Vjbj744ANGjx7d4/0UFRUxZ84cvve977F27VqGDBmShujyQz6eN+mi3MSWibxs3bq1R+O7OpkZtbW13Hzzzfz5z39m1KhRaYgumM6ZYMnmpry8nL59s1H+RLl7Rh/AZcD/dnk9B/hhvG2mTJniYbrxxhsd0KPAHnPnzu3xubNkyRJfs2ZN1j+LHnrokfjjwgsv7PF339397bffdjPL+ufRI7nH0qVL0/L/Hw/Q4AE1TRZLvvjMbB4wD6CysjLUm5JWVVVx/fXX069fv9COkcv27t2bl7k55ZRTenxetbS0sGXLFr797W/z7rvvpiewPJGv5006KDexZSovJ510Utp+p/zLv/wLGzduTMu+4tE5EyzZ3DQ2NrJrV/YmazDP8OSPZnYKsNDdz42+/hqAu383aJupU6d6Q0NDqHHV19dTU1MT6jFylXITTLkJptwEU25iU16CKTfBemNuzGyZu0+NtS4bVzW+BIwxs9Fm1o/IrYgeyUIcIiIiIhmV8RYvADM7H7gVKALudPd/6eb9W4ENIYc1BHg/5GPkKuUmmHITTLkJptzEprwEU26C9cbcHOXuQ2OtyErh1RuZWUNQs2ChU26CKTfBlJtgyk1syksw5SZYruUmKxOoioiIiBQiFV4iIiIiGaLC60N3ZDuAXky5CabcBFNugik3sSkvwZSbYDmVG43xEhEREckQtXiJiIiIZIgKLxEREZEMUeElIiIikiEqvEREREQyRIWXiIiISIao8BIRERHJEBVeIiIiIhnSN9sBJGLIkCFeVVUV6jFaW1sZMGBAqMfIVcpNMOUmmHITTLmJTXkJptwE6425WbZs2ftBN8nOicKrqqqKhoaGUI9RX19PTU1NqMfIVcpNMOUmmHITTLmJTXkJptwE6425MbMNQevU1SgFzd357Gc/yzPPPJPtUEREpADkRIuXSFh27NjBokWLWLZsGcuXL6dPH/0tIiIi4dFvGSloLS0tALzyyiv8+te/zm4wIiKS99TiJQWts/ACWLhwIRdddJFavUREMqS9vZ3Gxkba2tpS3segQYNYs2ZNGqNKXGlpKSNHjqS4uDjhbVR4SUHrLLyuvPJK7r//fn71q19x6aWXZjkqEZHC0NjYSHl5OVVVVZhZSvtobm6mvLw8zZF1z91pamqisbGR0aNHJ7yd/rSXgtbc3AzA3/7t3zJu3Di+9a1vsX///ixHJSJSGNra2qioqEi56MomM6OioiLp1joVXlLQOlu8Bg0axD//8z/z6quv8sQTT2Q5KhGRwpGLRVenVGJPqKvRzAYDw4HdwFvuriYByQudhVdZWRkXXnghffr04dlnn2XmzJlZjkxERMLW1NTE2WefDcCWLVsoKipi6NDIvKfvvfceFRUVFBUV0bdv37TNJxpYeJnZIOAGYDbQD9gKlAKVZvYCcLu7L0lLFCJZ0rXwKisr4/jjj2fp0qVZjkpERDKhoqKCFStWAJELrMrKyrjpppuAyOTtS5YsYciQIWk9ZryuxgeBd4DT3X2cu09396nufiRwC3ChmV0XtLGZHWlmS8xstZmtMrMbo8sPM7PHzWxt9N/Baf1EIknoWngBTJs2jRdffBF3z2ZYIiKSpwJbvNw9sK/F3ZcBy7rZ9z7gy+7+spmVA8vM7HGgFnjC3W8xswXAAuCrSUcukgadhVfnfb6mTZvG//zP/7B27VrGjh2bzdBERArK/PnzD7Q+JaOjo4OioqKY66qrq7n11ltTisfM+MQnPoGZ8bnPfY558+altJ+DdTvGy8wmx1i8A9jg7vuCtnP3zcDm6PNmM1sDjAAuBGqib1sM1KPCS7KkpaWFkpKSA3OwTJs2DYClS5eq8BIRKWDPPvssI0aM4L333mPmzJmMHz+eM844o8f7TWRw/e3AZOAVwIDjgVXAIDP7vLv/obsdmFkVMAlYClRGizKALUBlCnGLpEVLS8uBbkaACRMmUFZWxtKlS5kzZ04WIxMRKSyptkyFNY/XiBEjADj88MO5+OKLefHFFzNWeG0CrnP3VQBmNhH4NvCPwENA3MLLzMqAXwLz3X1n10sv3d3NLOZgGjObB8wDqKyspL6+PoFQU9fS0hL6MXJVPudm3bp19O3b9yOfb8yYMTz++OMJfeZ8zk1PKTfBlJvYlJdg+ZqbQYMGHZhPMVUdHR093gfAnj17KC4uprm5mdbWVvbv3095eTmtra089thjfPWrX415nLa2tqT+bxIpvMZ2Fl0A7r7azMa7+/ru5q8ws2IiRde97v5QdPG7ZjbM3Teb2TDgvVjbuvsdwB0AU6dO9ZqamgRCTV19fT1hHyNX5XNu/vu//5shQ4Z85POde+65fO9732PatGn0798/7vb5nJueUm6CKTexKS/B8jU3a9as6XFrVbpavEpKSigpKaG8vJytW7dy8cUXA7Bv3z6uuuoqLrnkkpjblZaWMmnSpISPk0jhtcrMfgzcH319BbDazEqA9qCNLFKV/RRY4+4/6LLqEWAukSsj5wIPJxytSJq1tLT81Rd22rRp7Nu3j+XLl3PqqadmKTIREcmkhQsXHnh+9NFH8+c//zmU4yQyc30tsA6YH32sjy5rB2bE2e40YA5wlpmtiD7OJ1JwzTSztcA50dciWXHwGC/46AB7ERGRdOq2xcvdd5vZ7cCj7v76Qatb4mz3LJHB+LGcnXiIIuFpaWnh8MMP/8iyYcOGceSRR6rwEhGRtOu2xcvMPgWsAH4XfV1tZo+EHJdIRsRq8YJIq5cKLxERSbdEuhq/CZwEbAdw9xXA6PBCEsmceIXXW2+9xZo1a7IQlYhI4cjlO4WkEnsihVe7u+84+FhJH0mkFwoqvK666ioqKiq48sor2b17dxYiExHJf6WlpTQ1NeVk8eXuNDU1UVpamtR2iV7VeBVQZGZjgC8Bz6UQo0iv0tHRwa5du2IWXsOHD+fuu+/m/PPPZ/78+fzkJz/JQoQiIvlt5MiRNDY2snXr1pT30dbWlnTxky6lpaWMHDkyqW0SKby+CNwM7AHuA34PfCfp6ER6mV27dgHELLwAzjvvPBYsWMAtt9zCmWeeyVVXXZXJ8ERE8l5xcTGjR/ds9FJ9fX1S82hlW7ddje6+y91vdvcT3X1q9HlbJoITCVPnDbKDCi+A73znO0yfPp2///u/p709cNo6ERGRhMQtvMxsrpm9bGat0UeDmV2TqeBEwpRI4dW3b1/mz5/Pjh07aGhoyFRoIiKSpwILLzObS2TC1C8Dw4ERRO7PeKOZ6e7BkvMSKbyAAzdFzcf7pImISGbFG+P1eeBid3+ry7InzexSIrcPujvMwETClmjhNXToUI4//njq6+v52te+lonQRCSD3J2f//znbNu2DYCBAwdy1VVX0adPIhf+iyQnXuE18KCiCwB3f8vMBoYXkkhmJFp4AcyYMYOf/vSntLe3U1xcHHZoIpJBTz75JLNnz/7IskMPPZQLLrggSxFJPotXzsebvEgTG0nOS6bwqqmpYdeuXbz00kthhyUiGVZXV8ehhx5KY2MjmzZt4vDDD6euri7bYUmeild4TTCzV2I8XgXGZypAkbAkU3hpnJdIftqxYwe//OUvmT17NiNGjGDYsGF85jOf4ZFHHuH999/PdniSh+IWXsAnYzwuACaGH5pIuJqbm4HECq8hQ4ZwwgknqPASyTMPPPAAu3fvpra29sCyuXPn0t7ezn333Ze9wCRvxSu83nb3DUEPADOzDMUpknbJtHhBZJzXn/70J/bu3RtmWCKSQXV1dUyYMIETTzzxwLKPfexjTJ48Wd2NEop4hdcSM/uimY3qutDM+pnZWWa2GJgbbngi4WlpaaFv377069cvofd3jvPSfF4i+eGNN97gT3/6E7W1tRzcjnDttdfy8ssv88orr2QpOslX8QqvWUAHcJ+ZbTKz1Wa2HlgLzAZudfe6oI3N7E4ze8/MVnZZdpiZPW5ma6P/Dk7T5xBJWucNshNtuNU4L5H8snjxYvr06cOcOX89NeXs2bMpLi5Wq5ekXWDh5e5t7n67u58GHAWcDUx296Pc/Xp3X97NvuuIFG9dLQCecPcxwBPR1yJZ0Vl4JaqiooKPfexjKrxE8sTdd9/NrFmzGDZs2F+tq6io4FOf+hT33HMPHR0dWYhO8lVCs8O5e7u7b3b37Ynu2N2fBrYdtPhCYHH0+WLgokT3J5JuyRZeAFOnTuXVV18NKSIRyZSdO3fyzjvvUFNTE/ieSy+9lK1bt7JixYqMxSX5L9PT8la6++bo8y1AZYaPL3JAKoXX2LFj2bJlCzt37gwpKhHJhMbGRgBGjhwZ+J7Ookyt3JJO8WauD5W7u5l50HozmwfMA6isrAz9xG9padGXK0C+5mbjxo306dMnqc/W3t4OwH333ce4cePyNjfpoNwEU25iy2ReOi+S2bp1a9xjHnnkkfzyl79kypQpGYkriM6ZYDmXG3eP+wCuA8Z0976AbauAlV1evw4Miz4fBryeyH6mTJniYVuyZEnox8hV+ZqbSZMm+QUXXJDUNqtWrXLA77nnHnfP39ykg3ITTLmJLZN5ufPOOx3wv/zlL3Hf97nPfc4HDhzo+/bty1BksemcCdYbcwM0eEBNk0hX4yjgJ2a23sweiE4xUZ1infcIH05BMRd4OMX9iPRYKl2NxxxzDH369OGNN94IKSoRyYSNGzcCMHz48Ljvq6mpYefOnSxf3t31ZCKJ6bbwcvdvuvtZwHHAM8BXgGXdbWdm9wHPA+PMrNHMrgNuAWaa2VrgnOhrkaxIpfAqKSmhqqqK119/PaSoRCQTGhsbGTJkCKWlpXHfd+aZZwIa5yXp0+0YLzP7OnAaUAYsB24iUoDF5e6zA1adnUyAImFJpfACGDdunFq8RHLcxo0b4w6s7zRs2DDGjx9PfX09N910UwYik3yXSFfjJUAF8EfgIeBh//DKRJGc5O60tLRQXl6e9LadhVekG19EclFjYyMjRoxI6L01NTU888wz7Nu3L+SopBAk0tU4mUi34IvATOBVM3s27MBEwrR7927cPaUWr7Fjx9La2sqmTZtCiExEMiHRFi/QOC9Jr24LLzM7HriayGD4K4CNwJMhxyUSqmRvkN3VuHHjADTOSyRH7dmzh61btyZceGmcl6RTIl2NtwADgduACe4+w92/EW5YIuHqSeE1duxYAI3zEslRna3ViXY1HnHEEQfGeYn0VCJdjRe4+7+5+3Pu3p6JoETC1pPCa8SIERxyyCFq8RLJUYnMWn+w008/neeff15jO6XHEulqHGNmD5rZ6uhcXuvNbH0mghMJS08KLzNj7NixavESyVGdc3gl2uIFMGnSJD744IMDRZtIqhLpalwE/BjYB8wA7gLuCTMokbD1pPCCyDgvtXiJ5KZUWryqq6sBNMBeeiyRwqu/uz8BmLtvcPeFwN+EG5ZIuHpaeI0dO5Y333zzwL0bRSR3NDY2UlZWxsCBAxPe5oQTTsDMWLFiRXiBSUEILLzM7LdmNhrYY2Z9gLVm9gUzu5jIZKoiOSsdLV779+/XlBIiOSiZqSQ6lZWVMWbMGBVe0mPxWrwWAb8HHgMGAF8CpgCfAa4JPzSR8KSjxQvgnXfeSVtMIpIZyUye2tWkSZNUeEmPBRZe7v4AMJlI69azwJXASuA5IrcQEslZKrxEClcqLV4QGef15ptvsn379vQHJQWjuzFee4FWoAQoJ1KElUWfi+SslpYWzIz+/funtP2gQYM44ogjWL9eF/iK5JKOjg42bdqUUotX5wD7V155Jc1RSSEJvEm2mc0CfgA8Akx2910Zi0okZJ03yDazlPdx3nnn8fOf/5zW1lYGDBiQxuhEJCzvvfceHR0dKbd4AaxYsYIzzjgjzZFJoYjX4nUz8Gl3X6CiS/JNc3Nzyt2MnWpra9m1axe/+tWv0hSViIStcyqJVFq8jjjiCCorKzWlhPRIvDFep7v7qkwGI5IpnS1ePXH66aczfPhwFi1alKaoRCRsnZOnptLiBRpgLz2XyDxeaWdms8zsdTNbZ2YLshGDFLZ0FF5mxrnnnsuTTz7Jhg0b0hSZiISpJy1eEOluXLVqFXv37k1nWFJAMl54mVkR8CPgPGAiMNvMJmY6Dils6Si8AD7xiU8AcNddd/V4XyISvo0bN1JcXMzQoUNT2r66upr29nbWrFmT5sikUGSjxeskYJ27r3f3vcD9wIVZiEMKWLoKryOOOIKzzjqLuro69u/fn4bIRCRMjY2NDB8+nD59Uvv113WAvUgqAq9qDNEIoOvkR43AtCzEccBvfvMb7r//fv74xz9mM4xea8OGDXmXm7feeovRo0enZV+1tbVcc8013HDDDVRUVKRln/kgH8+bdFFuYstEXp577rmUx3cBHHvssRxyyCH8z//8D2vXrk1jZPHpnAmWbG7+7u/+rkfnQE9lo/BKiJnNA+YBVFZWUl9fH9qx6urqeOihh0Lbv/ROgwcP7vF51dLSwpAhQxg+fDh33HFHegITkVCdfPLJPfrun3TSSTz99NM8//zz6QtKMmbEiBFMmDAha8c3d8/sAc1OARa6+7nR118DcPfvBm0zdepUb2hoCDWu+vp6ampqQj1GrlJugik3wZSbYMpNbMpLMOUmWG/MjZktc/epsdZlY4zXS8AYMxttZv2I3IrokSzEISIiIpJRGW/xAjCz84FbgSLgTnf/l27evxUI+3r9IcD7IR8jVyk3wZSbYMpNMOUmNuUlmHITrDfm5ih3j3npbFYKr97IzBqCmgULnXITTLkJptwEU25iU16CKTfBci03WZlAVURERKQQqfASERERyRAVXh/SXADBlJtgyk0w5SaYchOb8hJMuQmWU7nRGC8RERGRDFGLl4iIiEiGqPASERERyRAVXiIiIiIZosJLREREJENUeImIiIhkiAovERERkQxR4SUiIiKSISq8RERERDKkb7YDSMSQIUO8qqoq1GO0trYyYMCAUI+Rq5SbYMpNMOUmmHITm/ISTLkJ1htzs2zZsvfdfWisdTlReFVVVdHQ0BDqMerr66mpqQn1GLkq33Pz7rvvMnjwYPr165f0tvmem55QboIpN7EpL8GUm2C9MTdmtiFonboapaDt2bOHsWPHMmnSJF588cVshyMiInlOhZcUtB07drBz507WrFnDKaecwoIFC+jo6Mh2WCIikqdUeElBa2lpAeDWW29l7ty5/Nu//Ru//vWvsxuUiIjkrZwY4yUSls7Ca8SIEfzd3/0dP/vZz3juuee49NJLsxyZiEj+a29vp7Gxkba2tpT3MWjQINasWZPGqBJXWlrKyJEjKS4uTngbFV5S0DoLr7KyMvr168fkyZNZunRplqMSESkMjY2NlJeXU1VVhZmltI/m5mbKy8vTHFn33J2mpiYaGxsZPXp0wtupq1EKWtfCC2DatGksW7aM9vb2bIYlIlIQ2traqKioSLnoyiYzo6KiIunWOhVeUtBiFV5tbW28+uqr2QxLRKRg5GLR1SmV2BMqvMxssJkdZ2ZHm5mKNckbsQovQN2NIiIFoKmpierqaqqrqzniiCMYMWLEgdfvvfcel112GePHj2fChAk8//zzaTlm4BgvMxsE3ADMBvoBW4FSoNLMXgBud/claYlCJEsOLryqqqo4/PDDWbp0KZ///OezGZqIiISsoqKCFStWALBw4ULKysq46aabAJg7dy6zZs3iwQcfZO/evezatSstx4w3uP5B4C7gdHff3nWFmU0B5pjZ0e7+01gbm9mR0e0rAQfucPf/MrPDgJ8DVcBbwOXu/kEPP4dISg4uvMyMadOmqcVLRKSA7dixg6effpq6ujoA+vXrl9LdTWIJLLzcfWacdcuAZd3sex/wZXd/2czKgWVm9jhQCzzh7reY2QJgAfDVpCMXSYOWlhbMjP79+x9YNm3aNP7f//t/bN++nUMPPTR7wYmIFJD58+cfaH1KRkdHB0VFRTHXVVdXc+uttya9zzfffJOhQ4dy7bXX8uc//5kpU6bwX//1X2m5J2S347XMbHKMxzFmFncqCnff7O4vR583A2uAEcCFwOLo2xYDF/XoE4j0QEtLCwMGDKBPnw+/Cp3jvF566aVshSUiIlm0b98+Xn75ZT7/+c+zfPlyBgwYwC233JKWfScyj9ftwGTgFcCA44FVwCAz+7y7/6G7HZhZFTAJWApUuvvm6KotRLoiRbKipaXlQDdjpxNPPBEzY+nSpcycGdjwKyIiaZRKyxSEM4/XyJEjGTly5IE/xC+77LKMFl6bgOvcfRWAmU0Evg38I/AQELfwMrMy4JfAfHff2fXSS3d3M/OA7eYB8wAqKyupr69PINTUtbS0hH6MXJXPufnLX/5CUVHRX32+UaNG8dvf/pbp06fH3T6fc9NTyk0w5SY25SVYvuZm0KBBNDc392gfHR0dPd4HwJ49eyguLqa5uZkBAwYwfPhwXn75ZcaMGcNvf/tbjj322JjHaWtrS+r/JpHCa2xn0QXg7qvNbLy7r+9u/gozKyZSdN3r7g9FF79rZsPcfbOZDQPei7Wtu98B3AEwdepUr6mpSSDU1NXX1xP2MXJVPufmBz/4AYcffvhffb4ZM2bwm9/8hjPPPDPuPC35nJueUm6CKTexKS/B8jU3a9as6XFrVbpavEpKSigpKTmwr9tvv52//du/Ze/evRx99NEsWrQo5nFKS0uZNGlSwsdJpPBaZWY/Bu6Pvr4CWG1mJUDg9N4W+W31U2CNu/+gy6pHgLnALdF/H044WpE0i9XVCJFxXnV1daxfv55jjjkmC5GJiEgmLVy48COvq6uraWhoSPtxEpkMtRZYB8yPPtZHl7UDM+JsdxowBzjLzFZEH+cTKbhmmtla4Jzoa5GsCCq8zjjjDACeeuqpTIckIiJ5rNsWL3ffbWa3A4+6++sHrW6Js92zRAbjx3J24iGKhKelpYWqqqq/Wj5hwgSGDh1KfX09n/3sZzMfmIiI5KVEppP4FLAC+F30dbWZPRJyXCIZEdTiZWbU1NRQX1+Pe8zrP0RERJKWSFfjN4GTgO0A7r4CGB1eSCKZE1R4QWSA/TvvvMP69eszHJWISOHI5T9uU4k9kcKr3d13HHyspI8k0su4O83NzYGFV+cVRPl4CbeISG9QWlpKU1NTThZf7k5TUxOlpaVJbZfoVY1XAUVmNgb4EvBcCjGK9Cp79+5l3759gYXX+PHjOfzww6mvr+e6667LcHQiIvlv5MiRNDY2snXr1pT30dbWlnTxky6lpaWMHDkyqW0SKby+CNwM7AHuA34PfCfp6ER6mYNvkH2wg8d5dTdvnYiIJKe4uJjRo3s2eqm+vj6pebSyrduuRnff5e43u/uJ7j41+rwtE8GJhKm7wgsi3Y2NjY0a5yUiImkRt/Ays7lm9rKZtUYfDWZ2TaaCEwlTooUXwJIlSzIRkoiI5LnAwsvM5hKZMPXLwHBgBJH7M95oZnMyEp1IiBIpvMaPH5+Re4WKiEhhiNfi9XngYndf4u473H27uz8JXArckJnwRMKTSOHVOc5ryZIl7N+/P1OhiYhInopXeA1097cOXhhdNjCsgEQyJZHCC+CTn/wkmzZt4plnnslEWCIiksfiFV67U1wnkhMSLbwuvvhiysvLWbRoUSbCEhGRPBav8JpgZq/EeLwKjM9UgCJhSbTwOuSQQ7jiiit48MEHD2wjIiKSiriFF/DJGI8LgInhhyYSrkQLL4Brr72W1tZWHnzwwbDDEhGRPBav8Hrb3TcEPQBMM0pKDussvAYMGNDte0855RTGjBmj7kYREemReIXXEjP7opmN6rrQzPqZ2VlmthiYG254IuFpaWmhf//+FBUVdfteM6O2tpann36av/zlLxmITkRE8lG8wmsW0AHcZ2abzGy1ma0H1gKzgVvdvS5oYzO708zeM7OVXZYdZmaPm9na6L+D0/Q5RJLW0tKSUDdjp2uuuQYz46677goxKhERyWeBhZe7t7n77e5+GnAUcDYw2d2Pcvfr3X15N/uuI1K8dbUAeMLdxwBPRF+LZEWyhdfIkSOpqanh17/+dXhBiYhIXuv2Xo0A7t7u7pvdfXuiO3b3p4FtBy2+EFgcfb4YuCjR/YmkW7KFF8DkyZN54403NJmqiIikJKHCK40q3X1z9PkWoDLDxxc5oKWlhfLy8qS2GTt2LG1tbbzzzjshRSUiIvmsb7YO7O5uZh603szmAfOAjNwrr6WlRffjC5Cvudm0aROHHHJIUp9t165dAPziF7/gxBNPzNvcpINyE0y5iU15CabcBMu13HRbeJnZdcDT7r42Dcd718yGuftmMxsGvBf0Rne/A7gDYOrUqV5TU5OGwwerr68n7GPkqnzNTZ8+fRg1alRSn23cuHH8wz/8A/3796empiZvc5MOyk0w5SY25SWYchMs13KTSFfjKOAnZrbezB6ITjFRneLxHuHDKSjmAg+nuB+RHktljNcRRxxBeXk5r7/+ekhRiYhIPuu28HL3b7r7WcBxwDPAV4Bl3W1nZvcBzwPjzKwx2nJ2CzDTzNYC50Rfi2RFKoWXmTF27FjeeOONkKISEZF8lkhX49eB04AyYDlwE5ECLC53nx2w6uxkAhQJSyqFF0S6G//0pz+FEJGIiOS7RLoaLwEqgD8CDwEPd7kyUSQn7du3j7a2tpQKr7Fjx/L222+ze/fuECITEZF8lkhX42Qi3YIvAjOBV83s2bADEwlTa2srkNgNsg82btw43J1169alOywREclziXQ1Hg+cDpwJTAXeIYGuRpHerPMG2akWXgBvvPEGFRUVaY1LRETyWyJdjbcAA4HbgAnuPsPdvxFuWCLham5uBlIrvMaMGQOgKxtFRCRp3bZ4ufsFmQhEJJN60uJVVlbGiBEjeOONNzj11FPTHZqIiOSxRLoaxwDfBSYCpZ3L3f3oEOMSCVVPCi+IDLBXi5eIiCQrka7GRcCPgX3ADOAu4J4wgxIJW08Lr3HjxmkuLxERSVoihVd/d38CMHff4O4Lgb8JNyyRcKWjxWvbtm3s2LEjnWGJiEieCyy8zOy3ZjYa2GNmfYC1ZvYFM7uYyGSqIjkrHS1eAO+8807aYhIRkfwXr8VrEfB74DFgAPAlYArwGeCa8EMTCU86WrxAhZeIiCQnsPBy9weAyURat54FrgRWAs8RuYWQSM7qLLwGDBiQ0vZVVVUUFxfT2NiYzrBERCTPdXdV416gFSgByoH9oUckkgEtLS3069ePfv36pbR93759OeGEE1i+fHmaIxMRkXwWWHiZ2SzgB8AjwGR335WxqERCluoNsru66qqruOmmm3jttdcYP358miITEZF8Fm+M183Ap919gYouyTfpKLyuvvpq+vTpQ11dXXqCEhGRvBdvjNfp7r4qk8GIZEo6Cq8jjjiCk08+mbvvvpuOjo40RSYiIvkskXm80s7MZpnZ62a2zswWZCMGKWzpKLwAzj33XDZt2sTjjz+ehqhERCTfZbzwMrMi4EfAeURuQzTbzCZmOg4pbC0tLZSXl/d4P6eccgoVFRUsWrQoDVGJiEi+y0aL10nAOndf7+57gfuBC7MQhxSwdLV4FRcXc9VVV/HrX/+aDz74IA2RiYhIPuv2JtkhGAF0nXWyEZiWhTgO+MpXvsJtt91Gnz5Z6Xnt9fbv3593uWlra+OEE05Iy75qa2v57//+byorKykqKkrLPvNBPp436aLcxKa8BFNugiWbm6effpoTTzwxxIjiy0bhlRAzmwfMA6isrKS+vj60Yx166KF88pOfpLi4OLRj5LL29va8zM2ZZ57Z4/OqpaUFd+fGG2/k3XffTU9geSJfz5t0UG5iU16CKTfBks3Nm2++SWtra4gRxWfuntkDmp0CLHT3c6Ovvwbg7t8N2mbq1Kne0NAQalz19fXU1NSEeoxcpdwEU26CKTfBlJvYlJdgyk2w3pgbM1vm7lNjrctGu+VLwBgzG21m/YjciuiRLMQhIiIiklEZb/ECMLPzgVuBIuBOd/+Xbt6/FdgQclhDgPdDPkauUm6CKTfBlJtgyk1syksw5SZYb8zNUe4+NNaKrBRevZGZNQQ1CxY65SaYchNMuQmm3MSmvARTboLlWm50iYSIiIhIhqjwEhEREckQFV4fuiPbAfRiyk0w5SaYchNMuYlNeQmm3ATLqdxojJeIiIhIhqjFS0RERCRDVHgBZjbLzF43s3VmtiDb8WSbmb1lZq+a2Qoza4guO8zMHjeztdF/B2c7zkwwszvN7D0zW9llWcxcWMRt0fPoFTObnL3IwxWQl4VmtjF63qyIThvTue5r0by8bmbnZifqzDCzI81siZmtNrNVZnZjdLnOm+DcFPy5Y2alZvaimf05mptvRZePNrOl0Rz8PDr/JWZWEn29Lrq+KqsfICRx8lJnZm92OWeqo8t7//fJ3Qv6QWQusb8ARwP9gD8DE7MdV5Zz8hYw5KBl/w4siD5fAPxbtuPMUC7OACYDK7vLBXA+8BhgwMnA0mzHn+G8LARuivHeidHvVQkwOvp9K8r2ZwgxN8OAydHn5cAb0RzovAnOTcGfO9H//7Lo82JgafR8+AVwZXT5/wU+H33+98D/jT6/Evh5tj9DhvNSB1wW4/29/vukFi84CVjn7uvdfS9wP3BhlmPqjS4EFkefLwYuyl4omePuTwPbDloclIsLgbs84gXgUDMblpFAMywgL0EuBO539z3u/iawjsj3Li+5+2Z3fzn6vBlYA4xA50283AQpmHMn+v/fEn1ZHH04cBbwYHT5wedN5/n0IHC2mVlmos2cOHkJ0uu/Tyq8Il/6d7q8biT+D4JC4MAfzGyZRW5WDlDp7pujz7cAldkJrVcIyoXOJfhCtHn/zi7d0QWbl2j3zyQif6XrvOnioNyAzh3MrMjMVgDvAY8TaeHb7u77om/p+vkP5Ca6fgdQkdGAM+TgvLh75znzL9Fz5j/NrCS6rNefMyq8JJbp7j4ZOA+4wczO6LrSI+25uhwW5eIgPwaOAaqBzcD3sxpNlplZGfBLYL677+y6rtDPmxi50bkDuHuHu1cDI4m07I3PbkS9w8F5MbPjga8Ryc+JwGHAV7MXYXJUeMFG4Mgur0dGlxUsd98Y/fc94FdEfgC829lcG/33vexFmHVBuSjoc8nd343+gNwP/A8fdgkVXF7MrJhIYXGvuz8UXazzhti50bnzUe6+HVgCnEKkq6xvdFXXz38gN9H1g4CmzEaaWV3yMivabe3uvgdYRA6dMyq84CVgTPTKkX5EBik+kuWYssbMBphZeedz4BPASiI5mRt921zg4exE2CsE5eIR4JroVTUnAzu6dC3lvYPGUVxM5LyBSF6ujF6FNRoYA7yY6fgyJTrO5qfAGnf/QZdVBX/eBOVG5w6Y2VAzOzT6vD8wk8gYuCXAZdG3HXzedJ5PlwFPRltS80pAXl7r8keMERn31vWc6dXfp77dvyW/ufs+M/sC8HsiVzje6e6rshxWNlUCv4qO0ewL/Mzdf2dmLwG/MLPrgA3A5VmMMWPM7D6gBhhiZo3AN4FbiJ2L3xK5omYdsAu4NuMBZ0hAXmqil3Q7kStjPwfg7qvM7BfAamAfcIO7d2Qh7Ew5DZgDvBodlwLwT+i8geDczNa5wzBgsZkVEWkU+YW7P2pmq4H7zez/A5YTKVyJ/nu3ma0jcqHLldkIOgOC8vKkmQ0lcvXiCuDvou/v9d8nzVwvIiIikiHqahQRERHJEBVeIiIiIhmiwktEREQkQ1R4iYiIiGSICi8RERGRDFHhJSIJid6WY36X1783s//t8vr7ZvZ/zOxTZrYgyX3XmdllAcvfNLM/m9kbZnaXmY3ssv63nXP89ISZXWRmE7u8/raZndPT/Ub3NcnMfhp9XmtmW81suZmtjebw1B7se6iZ/S4dcYpIZqjwEpFE/Qk4FcDM+gBDgOO6rD8VeM7dH3H3W9J43K+4+8eBcUTmMXoyOtkx7n5+dDbrA6ITJyb7s+0i4EDh5e7fcPc/9ijqD/0TcFuX1z9390nuPobI3F4PmdmEVHbs7luBzWZ2WhriFJEMUOElIol6jsgtTCBScK0Ems1scPQGtROAl6OtOj+EAy1Wt5nZc2a2vrNVK1oc/dDMXjezPwKHd3fw6O1B/pPIDabPi+7nLTMbYmZV0X3dFY3rSDP7ipm9ZJGb6H6rcz9mdk102Z/N7O5oi9OngP8wsxVmdkzXFjgzOzvaQvWqRW7gXNLl2N8ys5ej6/7qvnoWuQvEx9z9zwGfaQlwBzAv+v5jzOx3FrlB/TOd+4wufyF6nP/PzFq67ObXwNXd5U9EegcVXiKSEHffBOwzs1FEWreeB5YSKcamAq+6+94Ymw4DpgMXEGnhgchtYcYRaWW6Jrq/RL1M7JsHjwFud/fjovseQ+T+bdXAFDM7w8yOA74OnBVtRbvR3Z8jcpuRr7h7tbv/pXOHZlYK1AFXuPsJRO7m8Pkux3w/ekP5HwM3xYhpKh/eyiSRz3MH8EV3nxLd3+3R5f8F/Fc0hsaDtm8ATu/mGCLSS6jwEpFkPEekSOosvJ7v8vpPAdv82t33u/tqIrekAjgDuC96Y+RNwJNJxGAByze4+wvR55+IPpbzYWEzBjgLeMDd3wdw923dHGsc8Ka7vxF9vTgae6fOG2AvA6pibD8M2NrNMQzAzMqI5PGB6O10fhLdHiLF7QPR5z87aPv3gOHdHENEeomCv1ejiCSlc5zXCURact4BvgzsBBYFbPP/t3f/rFEEYRzHvz/TBCEoCoLYCIa0GsRa7hVcEY4oYmGsIyr4EsRCSCWoaGVlIVhZKFgkRZJWjxiNhe8g5g8SLfSxmDlZjlv2FpONkd+n2puZfXa2OR5mnt39UTguS5rqmATeDmj/1nedexHxuDhA0uwuXL+od28/Gfx/ugOMVsSYJH0M+RCwERHnas5hNF/HzA4Ar3iZWR2LpC3D9bxatQ4cJa3ILNaIswBMSxqRdBJoVZ2Q68JukFaBqp7kew3M5FUkJJ2SdIK0staRdDy3H8vjt4GxAXE+AacljeffV4H5qrkWrALjZZ2SLpLqu55ExBbwRVIn90n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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Show the results\n", - "yaw_angles_opt = np.vstack(df_opt[\"yaw_angles_opt\"])\n", - "fig, axarr = plt.subplots(len(X), 1, sharex=True, sharey=True, figsize=(10, 10))\n", - "for i in range(len(X)):\n", - " axarr[i].plot(wind_directions, yaw_angles_opt[:, i], 'k-', label='T%d' % i)\n", - " axarr[i].set_ylabel('Yaw (Deg)')\n", - " axarr[i].legend()\n", - " axarr[i].grid(True)\n", - "axarr[-1].set_xlabel('Wind Direction (Deg)')\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8732cd8", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "jekyll": { - "layout": "default", - "nav_order": 1, - "permalink": "/tutorials/index", - "title": "Overview" - }, - "kernelspec": { - "display_name": "floris", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - }, - "vscode": { - "interpreter": { - "hash": "853a8652e3619d46ff0e51baac54f380b0862f9ec17aef8c5e0b66472a177ac0" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/intro.md b/docs/index.md similarity index 99% rename from docs/intro.md rename to docs/index.md index b8f9b6cd2..12ce55392 100644 --- a/docs/intro.md +++ b/docs/index.md @@ -26,7 +26,7 @@ fi.reinitialize(wind_directions=[i for i in range(10)]) fi.calculate_wake() ``` -Finally, results can be analyzed via post-processing functions avilable within +Finally, results can be analyzed via post-processing functions available within {py:class}`.FlorisInterface` such as {py:meth}`.FlorisInterface.get_turbine_layout`, {py:meth}`.FlorisInterface.get_turbine_powers` and diff --git a/docs/intro_concepts.ipynb b/docs/intro_concepts.ipynb new file mode 100644 index 000000000..f083c6054 --- /dev/null +++ b/docs/intro_concepts.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "86e53920", + "metadata": {}, + "source": [ + "(concepts_intro)=\n", + "# Introductory Concepts\n", + "\n", + "FLORIS is a command-line program written in Python. There are two primary packages that make up the software:\n", + "- `floris.simulation`: simulation framework including wake model definitions\n", + "- `floris.tools`: utilities for pre and post processing as well as driving the simulation\n", + "\n", + "\n", + "\n", + "Users of FLORIS will develop a Python script with the following sequence of steps:\n", + "\n", + "1. Load inputs and preprocess data\n", + "2. Run the wind farm wake simulation\n", + "3. Extract data and postprocess results\n", + "\n", + "Generally, users will only interact with `floris.tools` and most often through\n", + "the `FlorisInterface` class. Additionally, `floris.tools` contains functionality\n", + "for comparing results, creating visualizations, and developing optimization cases. \n", + "\n", + "This notebook steps through the basic ideas and operations of FLORIS while showing\n", + "realistic uses and expected behavior." + ] + }, + { + "cell_type": "markdown", + "id": "699c51dd", + "metadata": {}, + "source": [ + "## Initialize FlorisInterface\n", + "\n", + "The `FlorisInterface` provides functionality to build a wind farm representation and drive\n", + "the simulation. This object is created (instantiated) by passing the path to a FLORIS input\n", + "file as the only argument. After this object is created, it can immediately be used to\n", + "inspect the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "602f311c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x y\n", + " 0.0, 0.0\n", + " 630.0, 0.0\n", + "1260.0, 0.0\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from floris.tools import FlorisInterface\n", + "\n", + "fi = FlorisInterface(\"gch.yaml\")\n", + "x, y = fi.get_turbine_layout()\n", + "\n", + "print(\" x y\")\n", + "for _x, _y in zip(x, y):\n", + " print(f\"{_x:6.1f}, {_y:6.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e1eaeb53", + "metadata": {}, + "source": [ + "## Build the model\n", + "\n", + "At this point, FLORIS has been initialized with the data defined in the input file.\n", + "However, it is often simpler to define a basic configuration in the input file as\n", + "a starting point and then make modifications in the Python script. This allows for\n", + "generating data algorithmically or loading data from a data file. Modifications to\n", + "the wind farm representation are handled through the `FlorisInterface.reinitialize()`\n", + "function with keyword arguments. Another way to think of this function is that it\n", + "changes the value of inputs specified in the input file.\n", + "\n", + "Let's change the location of turbines in the wind farm. The code below changes the\n", + "initial 3x1 layout to a 2x2 rectangular layout." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d040b810", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x y\n", + " 0.0, 0.0\n", + " 0.0, 400.0\n", + " 800.0, 0.0\n", + " 800.0, 400.0\n" + ] + } + ], + "source": [ + "x_2x2 = [0, 0, 800, 800]\n", + "y_2x2 = [0, 400, 0, 400]\n", + "fi.reinitialize(layout_x=x_2x2, layout_y=y_2x2)\n", + "\n", + "x, y = fi.get_turbine_layout()\n", + "\n", + "print(\" x y\")\n", + "for _x, _y in zip(x, y):\n", + " print(f\"{_x:6.1f}, {_y:6.1f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "63f45e11", + "metadata": {}, + "source": [ + "Additionally, we can change the wind speeds and wind directions.\n", + "These are lists of wind speeds and wind directions that will be\n", + "combined so that a wake calculation will happen for every wind\n", + "direction with each speed.\n", + "\n", + "Notice that we can give `FlorisInterface.reinitialize()` multiple keyword arguments at once." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f9d834a", + "metadata": {}, + "outputs": [], + "source": [ + "# One wind direction and one speed -> one atmospheric condition\n", + "fi.reinitialize(wind_directions=[270.0], wind_speeds=[8.0])\n", + "\n", + "# Two wind directions and one speed -> two atmospheric conditions\n", + "fi.reinitialize(wind_directions=[270.0, 280.0], wind_speeds=[8.0])\n", + "\n", + "# Two wind directions and two speeds -> four atmospheric conditions\n", + "fi.reinitialize(wind_directions=[270.0, 280.0], wind_speeds=[8.0, 9.0])" + ] + }, + { + "cell_type": "markdown", + "id": "da4f3309", + "metadata": {}, + "source": [ + "`FlorisInterface.reinitialize()` creates all of the basic data structures required\n", + "for the simulation but it does not do any aerodynamic calculations. The low level\n", + "data structures have a complex shape that enables faster computations. Specifically,\n", + "most data is structured as a many-dimensional Numpy array with the following dimensions:\n", + "\n", + "```python\n", + "np.array(\n", + " (\n", + " wind directions,\n", + " wind speeds,\n", + " turbines,\n", + " grid-1,\n", + " grid-2\n", + " )\n", + ")\n", + "```\n", + "\n", + "For example, we can see the shape of the data structure for the grid point x-coordinates\n", + "for the all turbines and get the x-coordinates of grid points for the third turbine in\n", + "the first wind direction and first wind speed. We can also plot all the grid points in\n", + "space to get an idea of the overall form of our grid." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "01ea3a98", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions of grid x-components\n", + "(2, 2, 4, 3, 3)\n", + "\n", + "Turbine 3 grid x-components for first wind direction and first wind speed\n", + "[[800. 800. 800.]\n", + " [800. 800. 800.]\n", + " [800. 800. 800.]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Dimensions of grid x-components\")\n", + "print(np.shape(fi.floris.grid.x_sorted))\n", + "\n", + "print()\n", + "print(\"Turbine 3 grid x-components for first wind direction and first wind speed\")\n", + "print(fi.floris.grid.x_sorted[0, 0, 2, :, :])\n", + "\n", + "x = fi.floris.grid.x_sorted[0, 0, :, :, :]\n", + "y = fi.floris.grid.y_sorted[0, 0, :, :, :]\n", + "z = fi.floris.grid.z_sorted[0, 0, :, :, :]\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.scatter(x, y, z, marker=\".\")\n", + "ax.set_zlim([0, 150])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ebfdc746", + "metadata": {}, + "source": [ + "## Execute wake calculation\n", + "\n", + "Running the wake calculation is a one-liner. This will calculate the velocities\n", + "at each turbine given the wake of other turbines for every wind speed and wind\n", + "direction combination. Since we have not explicitly specified yaw control settings,\n", + "all turbines are aligned with the inflow." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e3bf1698", + "metadata": {}, + "outputs": [], + "source": [ + "fi.calculate_wake()" + ] + }, + { + "cell_type": "markdown", + "id": "e11352e8", + "metadata": {}, + "source": [ + "## Get turbine power\n", + "\n", + "At this point, the simulation has completed and we can use the `FlorisInterface` to\n", + "extract useful information such as the power produced at each turbine. Remember that\n", + "we have configured the simulation with two wind directions, two wind speeds, and four\n", + "turbines." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cc05bfe7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions of `powers`\n", + "(2, 2, 4)\n", + "\n", + "Turbine powers for 8 m/s\n", + "Wind direction 0\n", + " Turbine 0 - 1,691.33 kW\n", + " Turbine 1 - 1,691.33 kW\n", + " Turbine 2 - 592.65 kW\n", + " Turbine 3 - 592.98 kW\n", + "\n", + "Wind direction 1\n", + " Turbine 0 - 1,691.33 kW\n", + " Turbine 1 - 1,691.33 kW\n", + " Turbine 2 - 1,631.07 kW\n", + " Turbine 3 - 1,629.76 kW\n", + "\n", + "Turbine powers for all turbines at all wind conditions\n", + "[[[1691.32664838 1691.32664838 592.6531181 592.97842923]\n", + " [2407.84167188 2407.84167188 861.30649817 861.73255027]]\n", + "\n", + " [[1691.32664838 1691.32664838 1631.06554071 1629.75543674]\n", + " [2407.84167188 2407.84167188 2321.40975418 2319.53218301]]]\n" + ] + } + ], + "source": [ + "powers = fi.get_turbine_powers() / 1000.0 # calculated in Watts, so convert to kW\n", + "\n", + "print(\"Dimensions of `powers`\")\n", + "print( np.shape(powers) )\n", + "\n", + "N_TURBINES = fi.floris.farm.n_turbines\n", + "\n", + "print()\n", + "print(\"Turbine powers for 8 m/s\")\n", + "for i in range(2):\n", + " print(f\"Wind direction {i}\")\n", + " for j in range(N_TURBINES):\n", + " print(f\" Turbine {j} - {powers[i, 0, j]:7,.2f} kW\")\n", + " print()\n", + "\n", + "print(\"Turbine powers for all turbines at all wind conditions\")\n", + "print(powers)" + ] + }, + { + "cell_type": "markdown", + "id": "8ab273db", + "metadata": {}, + "source": [ + "## Applying yaw angles\n", + "\n", + "Yaw angles are applied to turbines through the `FlorisInterface.calculate_wake` function.\n", + "In order to fit into the vectorized framework, the yaw settings must be represented as\n", + "a `Numpy.array` with dimensions equal to:\n", + "- 0: number of wind directions\n", + "- 1: number of wind speeds\n", + "- 2: number of turbines\n", + "\n", + "**Unlike the data configured in `FlorisInterface.reinitialize()`, yaw angles are not retained**\n", + "**in memory and must be provided each time `FlorisInterface.calculate_wake` is used.**\n", + "**If no yaw angles are given, all turbines will be aligned with the inflow.**\n", + "\n", + "It is typically easiest to start with an array of 0's and modify individual\n", + "turbine yaw settings, as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "be78e20d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yaw angle array initialized with 0's\n", + "[[[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]\n", + "\n", + " [[0. 0. 0. 0.]\n", + " [0. 0. 0. 0.]]]\n", + "First turbine yawed 25 degrees for every atmospheric condition\n", + "[[[25. 0. 0. 0.]\n", + " [25. 0. 0. 0.]]\n", + "\n", + " [[25. 0. 0. 0.]\n", + " [25. 0. 0. 0.]]]\n" + ] + } + ], + "source": [ + "yaw_angles = np.zeros((2, 2, 4))\n", + "print(\"Yaw angle array initialized with 0's\")\n", + "print(yaw_angles)\n", + "\n", + "print(\"First turbine yawed 25 degrees for every atmospheric condition\")\n", + "yaw_angles[:, :, 0] = 25\n", + "print(yaw_angles)\n", + "\n", + "fi.calculate_wake(yaw_angles=yaw_angles)" + ] + }, + { + "cell_type": "markdown", + "id": "1ef54dc5", + "metadata": {}, + "source": [ + "## Start to finish\n", + "\n", + "Let's put it all together. The code below outlines these steps:\n", + "1. Load an input file\n", + "2. Modify the inputs with a more complex wind turbine layout and additional atmospheric conditions\n", + "3. Calculate the velocities at each turbine for all atmospheric conditions\n", + "4. Get the total farm power\n", + "5. Develop the yaw control settings\n", + "6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", + "7. Get the total farm power\n", + "8. Compare farm power with and without wake steering" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "205738aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Power % difference with yaw\n", + " 270 degrees: 7.39%\n", + " 280 degrees: 0.13%\n" + ] + } + ], + "source": [ + "# 1. Load an input file\n", + "fi = FlorisInterface(\"gch.yaml\")\n", + "\n", + "fi.floris.solver\n", + "\n", + "# 2. Modify the inputs with a more complex wind turbine layout\n", + "D = 126.0 # Design the layout based on turbine diameter\n", + "x = [0, 0, 6 * D, 6 * D]\n", + "y = [0, 3 * D, 0, 3 * D]\n", + "wind_directions = [270.0, 280.0]\n", + "wind_speeds = [8.0]\n", + "\n", + "# Pass the new data to FlorisInterface\n", + "fi.reinitialize(\n", + " layout_x=x,\n", + " layout_y=y,\n", + " wind_directions=wind_directions,\n", + " wind_speeds=wind_speeds\n", + ")\n", + "\n", + "# 3. Calculate the velocities at each turbine for all atmospheric conditions\n", + "# All turbines have 0 degrees yaw\n", + "fi.calculate_wake()\n", + "\n", + "# 4. Get the total farm power\n", + "turbine_powers = fi.get_turbine_powers() / 1000.0 # Given in W, so convert to kW\n", + "farm_power_baseline = np.sum(turbine_powers, 2) # Sum over the third dimension\n", + "\n", + "# 5. Develop the yaw control settings\n", + "yaw_angles = np.zeros( (2, 1, 4) ) # Construct the yaw array with dimensions for two wind directions, one wind speed, and four turbines\n", + "yaw_angles[0, :, 0] = 25 # At 270 degrees, yaw the first turbine 25 degrees\n", + "yaw_angles[0, :, 1] = 25 # At 270 degrees, yaw the second turbine 25 degrees\n", + "yaw_angles[1, :, 0] = 10 # At 265 degrees, yaw the first turbine -25 degrees\n", + "yaw_angles[1, :, 1] = 10 # At 265 degrees, yaw the second turbine -25 degrees\n", + "\n", + "# 6. Calculate the velocities at each turbine for all atmospheric conditions with the new yaw settings\n", + "fi.calculate_wake(yaw_angles=yaw_angles)\n", + "\n", + "# 7. Get the total farm power\n", + "turbine_powers = fi.get_turbine_powers() / 1000.0\n", + "farm_power_yaw = np.sum(turbine_powers, 2)\n", + "\n", + "# 8. Compare farm power with and without wake steering\n", + "difference = 100 * (farm_power_yaw - farm_power_baseline) / farm_power_baseline\n", + "print(\"Power % difference with yaw\")\n", + "print(f\" 270 degrees: {difference[0, 0]:4.2f}%\")\n", + "print(f\" 280 degrees: {difference[1, 0]:4.2f}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "99b7465c", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "While comparing turbine and farm powers is meaningful, a picture is worth at least\n", + "1000 Watts, and the `FlorisInterface` provides powerful routines for visualization.\n", + "\n", + "The visualization functions require that the user select a single atmospheric condition\n", + "to plot. The internal data structures still have the same shape but the wind speed and\n", + "wind direction dimensions have a size of 1. This means that the yaw angle array used\n", + "for plotting must have the same shape as above but a single atmospheric condition must\n", + "be selected.\n", + "\n", + "Let's create a horizontal slice of each atmospheric condition from above with and without\n", + "yaw settings included. Notice that although we are plotting the conditions for two\n", + "different wind directions, the farm is rotated so that the wind is coming from the\n", + "left (West) in both cases." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8bb179ff", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from floris.tools.visualization import visualize_cut_plane\n", + "\n", + "fig, axarr = plt.subplots(2, 2, figsize=(15,8))\n", + "\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], height=90.0)\n", + "visualize_cut_plane(horizontal_plane, ax=axarr[0,0], title=\"270 - Aligned\")\n", + "\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[0]], yaw_angles=yaw_angles[0:1,0:1] , height=90.0)\n", + "visualize_cut_plane(horizontal_plane, ax=axarr[0,1], title=\"270 - Yawed\")\n", + "\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], height=90.0)\n", + "visualize_cut_plane(horizontal_plane, ax=axarr[1,0], title=\"280 - Aligned\")\n", + "\n", + "horizontal_plane = fi.calculate_horizontal_plane(wd=[wind_directions[1]], yaw_angles=yaw_angles[1:2,0:1] , height=90.0)\n", + "visualize_cut_plane(horizontal_plane, ax=axarr[1,1], title=\"280 - Yawed\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d84cf194", + "metadata": {}, + "source": [ + "We can also plot the streamwise inflow velocities on the turbine rotor\n", + "grid points located on the rotor plane. The `plot_rotor_values` function\n", + "simply plots any data given as the first argument, so in this case\n", + "`fi.floris.flow_field.u` contains the yawed calculation from above." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3e517614", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from floris.tools.visualization import plot_rotor_values\n", + "\n", + "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, wd_index=0, ws_index=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", + "fig.suptitle(\"Wind direction 270\")\n", + "\n", + "fig, _, _ , _ = plot_rotor_values(fi.floris.flow_field.u, wd_index=1, ws_index=0, n_rows=1, n_cols=4, return_fig_objects=True)\n", + "fig.suptitle(\"Wind direction 280\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4dc966e1", + "metadata": {}, + "source": [ + "## On grid points" + ] + }, + { + "cell_type": "markdown", + "id": "e8241714", + "metadata": {}, + "source": [ + "In FLORIS, grid points are the points in space where the wind conditions are calculated.\n", + "In a typical simulation, these are all located on a regular grid on each turbine rotor.\n", + "\n", + "The parameter `turbine_grid_points` specifies the number of rows and columns which define the turbine grid.\n", + "In the example inputs, this value is 3 meaning there are 3 x 3 = 9 total grid points for each turbine.\n", + "Wake steering codes currently require greater values greater than 1 in order to compute gradients.\n", + "However, a single grid point (1 x 1) may be suitable for non wind farm control applications,\n", + "but retuning of some parameters might be required.\n", + "\n", + "We can visualize the locations of the grid points in the current example using `matplotlib.pyplot`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "774acfea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of xs: (2, 1, 4, 3, 3)\n", + " 2 wd x 2 ws x 4 turbines x 3 x 3 grid points\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the grid points\n", + "xs = fi.floris.grid.x_sorted\n", + "ys = fi.floris.grid.y_sorted\n", + "zs = fi.floris.grid.z_sorted\n", + "\n", + "# Consider the shape\n", + "print(f\"shape of xs: {xs.shape}\")\n", + "print(\" 2 wd x 2 ws x 4 turbines x 3 x 3 grid points\")\n", + "\n", + "# Lets plot just one wd/ws conditions\n", + "xs = xs[1, 0, :, :, :]\n", + "ys = ys[1, 0, :, :, :]\n", + "zs = zs[1, 0, :, :, :]\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111, projection=\"3d\")\n", + "ax.scatter(xs, ys, zs, marker=\".\")\n", + "ax.set_zlim([0, 150])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e91f7a84", + "metadata": {}, + "source": [ + "## Basic use case: calculating AEP" + ] + }, + { + "cell_type": "markdown", + "id": "34bc7865", + "metadata": {}, + "source": [ + "Calculating AEP in FLORIS v3 leverages the vectorized framework to\n", + "substantially reduce the computation time with respect to v2.4.\n", + "Here, we demonstrate a simple AEP calculation for a 25-turbine farm\n", + "using several different modeling options. We make the assumption\n", + "that every wind speed and direction is equally likely. We also\n", + "report the time required for the computation using the Python\n", + "`time.perf_counter()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee1918d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating AEP for 1440 wind direction and speed combinations...\n", + "Number of turbines = 25\n", + "Model AEP (GWh) Compute Time (s)\n", + "Jensen 843.620 0.336 \n", + "GCH 843.905 1.422 \n", + "CC 839.263 2.798 \n" + ] + } + ], + "source": [ + "import time\n", + "from typing import Tuple\n", + "\n", + "wind_directions = np.arange(0.0, 360.0, 5.0)\n", + "wind_speeds = np.arange(5.0, 25.0, 1.0)\n", + "\n", + "num_bins = len(wind_directions) * len(wind_speeds)\n", + "print(f\"Calculating AEP for {num_bins} wind direction and speed combinations...\")\n", + "\n", + "# Set up a square 25 turbine layout\n", + "N = 5 # Number of turbines per row and per column\n", + "D = 126.0\n", + "\n", + "X, Y = np.meshgrid(\n", + " 7.0 * D * np.arange(0, N, 1),\n", + " 7.0 * D * np.arange(0, N, 1),\n", + ")\n", + "X = X.flatten()\n", + "Y = Y.flatten()\n", + "print(f\"Number of turbines = {len(X)}\")\n", + "\n", + "# Define several models\n", + "fi_jensen = FlorisInterface(\"jensen.yaml\")\n", + "fi_gch = FlorisInterface(\"gch.yaml\")\n", + "fi_cc = FlorisInterface(\"cc.yaml\")\n", + "\n", + "# Assign the layouts, wind speeds and directions\n", + "fi_jensen.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "fi_cc.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)\n", + "\n", + "def time_model_calculation(model_fi: FlorisInterface) -> Tuple[float, float]:\n", + " \"\"\"\n", + " This function performs the wake calculation for a given\n", + " FlorisInterface object and computes the AEP while\n", + " tracking the amount of wall-time required for both steps.\n", + "\n", + " Args:\n", + " model_fi (FlorisInterface): _description_\n", + " float (_type_): _description_\n", + "\n", + " Returns:\n", + " tuple(float, float):\n", + " 0: AEP\n", + " 1: Wall-time for the computation\n", + " \"\"\"\n", + " start = time.perf_counter()\n", + " model_fi.calculate_wake()\n", + " aep = model_fi.get_farm_power().sum() / num_bins / 1E9 * 365 * 24\n", + " end = time.perf_counter()\n", + " return aep, end - start\n", + "\n", + "jensen_aep, jensen_compute_time = time_model_calculation(fi_jensen)\n", + "gch_aep, gch_compute_time = time_model_calculation(fi_gch)\n", + "cc_aep, cc_compute_time = time_model_calculation(fi_cc)\n", + "\n", + "print('Model AEP (GWh) Compute Time (s)')\n", + "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"Jensen\", jensen_aep, jensen_compute_time))\n", + "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"GCH\", gch_aep, gch_compute_time))\n", + "print('{:8s} {:<10.3f} {:<6.3f}'.format(\"CC\", cc_aep, cc_compute_time))" + ] + }, + { + "cell_type": "markdown", + "id": "c006ae1e", + "metadata": {}, + "source": [ + "## Basic use case: wake steering design" + ] + }, + { + "cell_type": "markdown", + "id": "f5777dae", + "metadata": {}, + "source": [ + "FLORIS V3 further includes new optimization routines for the design of wake steering controllers. The SerialRefine is a new method for quickly identifying optimum yaw angles." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "32a93c6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Demonstrate on 7-turbine single row farm\n", + "X = np.linspace(0, 6*7*D, 7)\n", + "Y = np.zeros_like(X)\n", + "wind_speeds = [8.]\n", + "wind_directions = np.arange(0., 360., 2.)\n", + "fi_gch.reinitialize(layout_x=X, layout_y=Y, wind_directions=wind_directions, wind_speeds=wind_speeds)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7d773cdc", + "metadata": {}, + "outputs": [], + "source": [ + "from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR\n", + "\n", + "# Define the SerialRefine optimization\n", + "yaw_opt = YawOptimizationSR(\n", + " fi=fi_gch,\n", + " minimum_yaw_angle=0.0, # Allowable yaw angles lower bound\n", + " maximum_yaw_angle=25.0, # Allowable yaw angles upper bound\n", + " Ny_passes=[5, 4],\n", + " exclude_downstream_turbines=True,\n", + " exploit_layout_symmetry=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1ccb9ab7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Serial Refine] Processing pass=0, turbine_depth=0 (0.0%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=1 (7.1%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=2 (14.3%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=3 (21.4%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=4 (28.6%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=5 (35.7%)\n", + "[Serial Refine] Processing pass=0, turbine_depth=6 (42.9%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=0 (50.0%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=1 (57.1%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=2 (64.3%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=3 (71.4%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=4 (78.6%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=5 (85.7%)\n", + "[Serial Refine] Processing pass=1, turbine_depth=6 (92.9%)\n", + "Optimization wall time: 1.044 s\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "\n", + "## Calculate the optimum yaw angles for 25 turbines and 72 wind directions\n", + "df_opt = yaw_opt.optimize()\n", + "\n", + "end = time.perf_counter()\n", + "\n", + "walltime = end - start\n", + "print(f\"Optimization wall time: {walltime:.3f} s\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb2e01e8", + "metadata": {}, + "source": [ + "In the results, T0 is the upstream turbine when wind direction is 270, while T6 is upstream at 90 deg" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "686548be", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the results\n", + "yaw_angles_opt = np.vstack(df_opt[\"yaw_angles_opt\"])\n", + "fig, axarr = plt.subplots(len(X), 1, sharex=True, sharey=True, figsize=(10, 10))\n", + "for i in range(len(X)):\n", + " axarr[i].plot(wind_directions, yaw_angles_opt[:, i], 'k-', label='T%d' % i)\n", + " axarr[i].set_ylabel('Yaw (Deg)')\n", + " axarr[i].legend()\n", + " axarr[i].grid(True)\n", + "axarr[-1].set_xlabel('Wind Direction (Deg)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8732cd8", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jekyll": { + "layout": "default", + "nav_order": 1, + "permalink": "/tutorials/index", + "title": "Overview" + }, + "kernelspec": { + "display_name": "floris", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.1" + }, + "vscode": { + "interpreter": { + "hash": "853a8652e3619d46ff0e51baac54f380b0862f9ec17aef8c5e0b66472a177ac0" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/reference.md b/docs/reference.md deleted file mode 100644 index 8349c4a6e..000000000 --- a/docs/reference.md +++ /dev/null @@ -1 +0,0 @@ -# Coming Soon! diff --git a/docs/references.bib b/docs/references.bib index f08ec7cd0..e5b7f41d9 100644 --- a/docs/references.bib +++ b/docs/references.bib @@ -259,7 +259,8 @@ @article{bastankhah_2021 publisher={Cambridge University Press}, author={Bastankhah, Majid and Welch, Bridget L. and Martínez-Tossas, Luis A. and King, Jennifer and Fleming, Paul}, year={2021}, - pages={A53}} + pages={A53} +} @Article{bay_2022, AUTHOR = {Bay, C. J. and Fleming, P. and Doekemeijer, B. and King, J. and Churchfield, M. and Mudafort, R.}, diff --git a/docs/turbine_interaction.ipynb b/docs/turbine_interaction.ipynb index fbeb62f5a..6df40578e 100644 --- a/docs/turbine_interaction.ipynb +++ b/docs/turbine_interaction.ipynb @@ -44,10 +44,16 @@ " `ti = TurbineInterface(turbine_obj)`\n", "- Option 2: Load from a turbine configuration dictionary:\n", " `ti = TurbineInterface.from_turbine_dict(turbine_dict)`\n", - "- Option 3: Load a file from the internal turbine library:\n", + "- Option 3a: Load a file from the internal turbine library:\n", + " `ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")`\n", + "- Option 3b: Load a file from the internal turbine library:\n", " `ti = TurbineInterface.from_internal_library(\"iea_15MW.yaml\")`\n", - "- Option 4: Load a file from anywhere:\n", - " `ti = TurbineInterface.from_yaml(\"path/to/turbine.yaml\")`" + "- Option 4: Load a file from an external turbine library:\n", + " `ti = TurbineInterface.from_library(\"path/to/user/library\", \"iea_15MW.yaml\")`\n", + "- Option 5: Load a file from anywhere:\n", + " `ti = TurbineInterface.from_yaml(\"path/to/turbine.yaml\")`\n", + "\n", + "### Single Dimensional Turbine" ] }, { @@ -59,7 +65,7 @@ }, "outputs": [], "source": [ - "ti = TurbineInterface.from_internal_library(\"iea_15MW.yaml\")" + "ti = TurbineInterface.from_library(\"internal\", \"iea_15MW.yaml\")" ] }, { @@ -68,7 +74,7 @@ "id": "f2576e8a-47ee-48b5-8707-aca0dc76929c", "metadata": {}, "source": [ - "### Plot the core attributes\n", + "#### Plot the core attributes\n", "\n", "For `TurbineInterface`, the core functionality is the power and thrust computation and plotting." ] @@ -101,14 +107,14 @@ { "cell_type": "code", "execution_count": 4, - "id": "4667fd39-4a28-4b20-87eb-d0e0fc94cdb5", + "id": "722be425-9231-451a-bd84-7824db6a5098", "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -120,20 +126,80 @@ } ], "source": [ - "ti.plot_Cp_curve()" + "ti.plot_Ct_curve()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b24cd983-a22b-4a77-87f1-1c43c6e44842", + "metadata": {}, + "source": [ + "### Interacting With A Multi-Dimensional Turbine" ] }, { "cell_type": "code", "execution_count": 5, - "id": "722be425-9231-451a-bd84-7824db6a5098", + "id": "91eee045-9019-40e0-9dc4-c95d2737bb3c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ti_md = TurbineInterface.from_library(\"internal\", \"iea_15MW_multi_dim_cp_ct.yaml\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "62b901ba-67c9-40ea-b770-453e62ae50ed", + "metadata": {}, + "source": [ + "#### Plot the core attributes\n", + "\n", + "In this example, we'll demonstrate how the usage for a multi-dimensional turbine is exactly the same, and how to produce cleaner figures." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f6e9f40c-4900-465a-882b-86ea86030f9b", "metadata": { "tags": [] }, "outputs": [ { "data": { - "image/png": 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", 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ti_md.plot_power_curve(\n", + " fig_kwargs={\"figsize\": (6, 3)}, # The legend is a bit wider, so we'll need to change the dimensions\n", + " legend_kwargs={\"fontsize\": 6}, # The labels are quite long, so let's shrink the font\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b61286ae-ec6f-46e8-a863-0384b2e87855", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", "text/plain": [ "
" ] @@ -145,7 +211,9 @@ } ], "source": [ - "ti.plot_Ct_curve()" + "ti_md.plot_Ct_curve(\n", + " legend_kwargs={\"fontsize\": 6}, # The labels are quite long, so let's shrink the font\n", + ")" ] }, { @@ -172,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "52c74e32-c93c-449e-90f2-4ed5b2bf0f72", "metadata": { "tags": [] @@ -183,8 +251,10 @@ "output_type": "stream", "text": [ "iea_15MW\n", + "iea_15MW_multi_dim_cp_ct\n", + "nrel_5MW\n", "iea_10MW\n", - "nrel_5MW\n" + "iea_15MW_floating\n" ] } ], @@ -215,10 +285,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "47026e36-ee3a-4b40-8c8f-e787463be596", + "execution_count": 9, + "id": "273da4ef-0243-4296-9838-3f8e98615deb", "metadata": { - "tags": [] + "scrolled": true }, "outputs": [ { @@ -226,8 +296,10 @@ "output_type": "stream", "text": [ "iea_15MW\n", - "iea_10MW\n", + "iea_15MW_multi_dim_cp_ct\n", "nrel_5MW\n", + "iea_10MW\n", + "iea_15MW_floating\n", "x_20MW\n" ] } @@ -249,12 +321,16 @@ "### Comparing turbines\n", "\n", "There are a number of methods that will plot the varying properties for each turbine against each\n", - "other, but here the primary output will be displayed." + "other, but here the primary output will be displayed.\n", + "\n", + "It should be noted that the 15MW turbines are all variations of each other, and so the\n", + "multi-dimensional example is removed in favor the of the floating 15MW turbine to highlight\n", + "a multi-dimensional turbine in relation to the standard 15 MW example." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "14008ddc-35be-4ac7-8371-f17cbf2f9ac3", "metadata": { "tags": [] @@ -262,9 +338,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -275,10 +351,11 @@ ], "source": [ "tl.plot_comparison(\n", - " exclude=[\"nrel_5MW\"], # Remove a turbine just for demonstration\n", + " exclude=[\"iea_15MW_multi_dim_cp_ct\"], # Remove a turbine just for demonstration\n", " wind_speeds=np.linspace(0, 30, 61), # 0 -> 30 m/s, every 0.5 m/s\n", - " fig_kwargs={\"figsize\": (7, 6)}, # Size the figure appropriately for the docs page\n", + " fig_kwargs={\"figsize\": (8, 8)}, # Size the figure appropriately for the docs page\n", " plot_kwargs={\"linewidth\": 1}, # Ensure the line plots look nice\n", + " legend_kwargs={\"fontsize\": 5}, # Ensure all the legend items fit\n", ")" ] }, @@ -293,7 +370,6 @@ "Alternatively, these can all be ploted individually with:\n", "\n", "- `plot_power_curves()`\n", - "- `plot_Cp_curves()`\n", "- `plot_Ct_curves()`\n", "- `plot_rotor_diameters()`\n", "- `plot_hub_heights()`\n", @@ -303,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "5168be89-64be-482a-8a8a-c6889e64de88", "metadata": { "tags": [] @@ -313,21 +389,23 @@ "name": "stdout", "output_type": "stream", "text": [ - " Turbine | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\n", - "-----------------------------------------------------------------------\n", - " iea_15MW | 242.24 | 150.0 | 1.225\n", - " iea_10MW | 198.00 | 119.0 | 1.225\n", - " nrel_5MW | 126.00 | 90.0 | 1.225\n", - " x_20MW | 252.00 | 165.0 | 1.225\n" + " Turbine | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\n", + "---------------------------------------------------------------------------------\n", + " iea_15MW | 242.24 | 150.0 | 1.225\n", + " iea_15MW_multi_dim_cp_ct | 242.24 | 150.0 | 1.225\n", + " nrel_5MW | 126.00 | 90.0 | 1.225\n", + " iea_10MW | 198.00 | 119.0 | 1.225\n", + " iea_15MW_floating | 242.24 | 150.0 | 1.225\n", + " x_20MW | 252.00 | 165.0 | 1.225\n" ] } ], "source": [ - "header = f\"{'Turbine':>15} | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\"\n", + "header = f\"{'Turbine':>25} | Rotor Diameter (m) | Hub Height (m) | Air Density (ρ)\"\n", "print(header)\n", "print(\"-\" * len(header))\n", "for name, t in tl.turbine_map.items():\n", - " print(f\"{name:>15}\", end=\" | \")\n", + " print(f\"{name:>25}\", end=\" | \")\n", " print(f\"{t.turbine.rotor_diameter:>18,.2f}\", end=\" | \")\n", " print(f\"{t.turbine.hub_height:>14,.1f}\", end=\" | \")\n", " print(f\"{t.turbine.ref_density_cp_ct:>15,.3f}\")" @@ -350,7 +428,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.4" } }, "nbformat": 4, diff --git a/docs/wake_models.ipynb b/docs/wake_models.ipynb index c3ad37473..ddaced065 100644 --- a/docs/wake_models.ipynb +++ b/docs/wake_models.ipynb @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": { "tags": [] }, @@ -73,7 +73,7 @@ " height=90.0,\n", " yaw_angles=yaw_angles\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", + " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes, clevels=100)\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes, yaw_angles=yaw_angles)" ] }, @@ -94,19 +94,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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Zsw0OKk6KZTS2qz3Pts2NpXo8oPVY5XKnpX1dNuv7qR6P4nl5bGfk9VbPlcbz5jGbMlqsq0ls/fjN81QH6NCOF8Pm8zYxHH2ey2q5vCFozCaszXMrRXU0opk1qAlrtqguuTASoW22r57TqMTn5zH+tefR3ubxZxXVVRwz5s6aKyScOkSSD89IcakZf6QSURIEYaEkwTpOGHHbuRi/u1ZbZ1JbAtsL5vvqUcnkTou7gF/2Fi1vh2Wc/B2E0etB2YmcyIIEElZXIpdFTaZaha8iVnE6un5EBkdlNV82uo3TcvzRc2DkGC3nnH96itfg1rbRLfvIo6WmKsRjpLUerR09fjN62jzXqqg29z0OndZL8c8cTW1ET902eWuR0WrbppTalN/xMlpNAW4ev5o2XJxvvr9GOnCbeLqFvNaFtymbzXTjVY6ErqLgLgKR5FGOSh5FlARBWCjX4nNEvWsMHEOv28GpXFH1FOFpohMrC75/+NLZJp08T9G88jaNaXI3jUXIXxurJoSHYZEyuWja+5GmcZ8RNVuPaTeGsyahs9AmacbYNFugSKstYlMj0dF2Qc23bbZtymk+RrLYdy1CORr1rEcms3GWuKgxKbtVcRyXIjwtbdeO8xwVzllTdgsRa4yZNGlarmtI2LzjPd3quEqnuX60bVMW82XVfZdjRvXIuVUF86gKAC0bkccx208Yx1xFREkQhIURng/4M+c2Hv6N98FvwG//vR9lq+cReim+p/GJCTyF72oCT+O64y9AJklwDyEvI1IWBAfe1+i+p3fQFyF3TabJ3iwsWghzDiuGB2HRMnmaJHJWVlk2D8M4UQ1DryGW80npaab5WWiTuXxMZVMwq+M0yZbrYttcDN3GPieP9ywUxzi22M+IOJaRy3zbQlDzgj+V86ZxTqVguoU4jqMUKV2Tv6q4uY6G1BbhcRuy5VQqsVZFr6jyWtlHIX01WSsl0K1uV6n46jYrx2bneZoqah6H9J2ed08QhJVAe1HxeNdskiYRcd8jTj0SZSvgqcR2+B0HfFfjuVaa8sf2ZnBVgueabL398vccU7RxHVOsz3/581yN5xgcPy2eu/kvkYdIFzEVOTqMwE1iasRtgbI3euzDdZiXIYY5ixDEWViGRB6HQM7DaUxbPQinVRhh9oijF87z/XLyBHOWv7ExFIJViFYhVm69cE8jOqeNU2tbyJex0UFtGoV/jIsyAaqQNgetR49TO5+K7GnjoHFpRiyrVAWvjKiVz6tTbeSCZdKkkDq3EMNyGo68XVXmSnFrFzm32H95PT4Ii5Q+b8ZzEFESBGFhGAPKlB3me16xw1onsr+yVdAaEuWRJppUuyjtkioXpR2UdtDazZYHtny4dkljRWqy9amLMvaiooybbeMUbZWuXMQqx86/oF23cd8Qqupy1zG4Oim/9EfaZFJXyFijXaO9Q/kYpWpRNW9JImRmkKBlyV/OvGmXNZb0vhxWDmdh0QJ5VNI4D4cRzFURyaYwnlT5a5MBxzsd41sOm2I6nwiOHB04nv+9WSU+l7yaSGVjCtum1LAy5hbrctHTUV3qtLaVW9OiTbm+mLYDJ5v6o7neGxutq4tTJlhuVbSqglURuSSx12usqOVzMubyVzwm+xGVbN5Gtx5xmxURJUEQFsaO2Saozrfa7UGnjDCR2i98F4iyW41ZO67pbJ0Ye+GwcqUSbSUquxioTKysbNn7/OKhtFtcdJR20SZEJSmpzi8EYJSTrXPKm85+XdTO6PJm1bUWgXMciuiYTaWoC9gk6apuQ5rW2ua/Ita2y9dVjl2mfYw5BoykSxo1W+fhOCVwHMcWGTwMC34fj0IWJzGLSB6HHDblb1WEbhy52B1OBo6eeSJ5J0H4ljFe8HB/0+WMX5w3ApunRzZlKp9X0ZjKOiasMy468FAmIMFF67q06WKuxvJ59XGVjncV+LWp575UUfqZn/kZfuZnfoY//dM/BeAVr3gF7373u3njG98IwGAw4Id+6If4+Z//eYbDIffffz/ve9/7uHDhQrGPJ554goceeojf+I3fYH19nQcffJBHH30U3xfHE4RVIzYRvSgunpvuOqYb4eRSEHbqG6SNL9vmej1jB6m5nwwHWyK8tSuWJBw4bWRGUWtiTClv2lh5K37Fa4hVLmTalPJlmutx61KWplYA/ajcRo3uv3oOVu5GRa8tlSOXO7chck5D1Nrkrv6Y2nbNZUVqRiZ8k7bN5a0qec19NpfN/PeaUQKbLEMKDyOEk1ikLC5NEKe8n8cVGVyV6J5K0qUVfzks0yJzqyh2h0nDXCWZW2aRl4P+3cqt8sqrBzvHg/yNmrK2O+M+lvqf9YIXvIAf//Ef5yUveQnGGH7u536Ov/yX/zJ/8Ad/wCte8Qp+8Ad/kF/6pV/igx/8IFtbW7z97W/nzW9+M7/9278NgFKKN73pTVy8eJGPf/zjPPXUU7z1rW8lCAJ+7Md+bJmnLgjCAeibHr5bfq18/PN3cq7rEQSaKNAEvib0Nb6bEmbPA08T+AbPy8reqslfXk6bpDQFC8bK09htZpWyeY9TwclutUvpSCdvxtLdreey2I5qLnYqbQhdLlRV0dKjEma3bwqZW7bJlqkkLSJ9VtLKSmFVUSz32xTJ+vEnVe0ylWjbiHS1ilU9mlaNshWVtRpRu6aw5cdyVFIZI8DIMSZKXui3RvQO/Lc9oARO4jCCuKzI4FFF944zMre80YHz0RZ1WxWBmyeV8rjlbVHj5Y5L2o6iCudh/kb5lmrGiZQcY8xivnVn5Pz58/zET/wE3/3d381tt93GBz7wAb77u78bgM985jO8/OUv57HHHuPee+/ll3/5l/mO7/gOnnzyySLK9LM/+7P88A//ME8//TRhGE46VMHOzg5bW1v8gvcies6qfKUIwunj8uZLcb/+Zfz1X/sJAH7vF38V3+mQpC7D2CVJHdJYkyQuceKSKgel7JeVZ1J8z+D5Gt8z+J4h8O34H5/ErnONrZ6Xrfc8jecZPBS+l7XN2nneaPRgmoQ1aZWyWZhDnkY4qLAt6vhtLKsTuGCxa6ISVUhUESmrSlVjWVXIapG1lkhcNUpnDOg0ra0rpA9a24+L8lWPNw6bX59JmUoLkXMqwlaIljsaaZskiVUJbEvBLPZfaUOa1tc1xuc1o4UHKaqyDLFbVpQuZ6mSVjvO8UnaKkXVVo2TMs7tpBYyOayQ3YwT7v7n/4EbN26wubk5tt2Rqb5Sig9+8IPs7e1x6dIlHn/8cZIk4b777ivavOxlL+Ouu+4qROmxxx7jVa96VS0V7/777+ehhx7iU5/6FF/7tV/beqzhcMhwOCye7+zsLO+FCYJQYIBEl7/03PKCiI2OX2vhqtHCDjpOrUSlDkpZgUpSB5U6pMpDqQClYKhclAYVu6hEF6KVKjvmKFVOLRjjmRQvkyrXzUTLz4s3mGKdZ/LqevmyrECDa3Bdaus80mKdl60fIarP1DqvoBXbHVQkqtGyRUhT2FmMwOXk57ToFLVGh9GbOUV7xijeksUuJ5eCUp4astUiYM0ona7ImGmKYiWqV+wzT9s0DkbbtrRIZTWCV99nfRmMl728bLHrajydFFUu82qWvqvxPTvg2v4vZssqFTG9rI3najwV2/9td/KUAyPn4bWk0y1QyKoRtmVKmRv4RyZlUBczZ4nVLptMkrJlTXvQZB4hO6po2mGF7CgiaMuQscNGzJzWi/coS/8r/tEf/RGXLl1iMBiwvr7OL/7iL3L33Xfzh3/4h4RhyPb2dq39hQsXuHz5MgCXL1+uSVK+Pl83jkcffZT3vOc9i30hgiBMpR9u4jtliYZhsEYY1CO/nlf/wnSNgsiGw6tf165OaUsqcadJR5KgMnFSGvs4f64cVKqzx3Z8j30cMlQuWoOKbTuTliKmso5jXpWv1qd2ICAppMmtSFQhV67BdcCrCFtVztzqvWfbVvdRPEZlBRhm+3sAhbQdVNaqHFjcmrgL7lzlEucv+ILflMxFit2EjrOTCd5ImuZYJpRqPiK5K86kIRpa0x6xM7aISrPipSqqYPqkWbXLWDuoxB2pkJk/V7p8l/LIl+9lla5MUpMv39U2Eu2Uz4u2eVS60d51DeaQIlKVsmWlPR5VhMxd4jQF5bFaqvcdgZQd9cTkTQ4SGVumkC0qKrYsGTuKaNjSRemlL30pf/iHf8iNGzf4t//23/Lggw/y0Y9+dKnHfOc738kjjzxSPN/Z2eHOO+9c6jEFQQBlfDy/jGZ8/A/Pc267SzdK6HQMYWjoBClBYIgiQxDYqE8TT6etifeeTsAbqZUHZMIFEFgxcLNb/vVsxWs8UwWs0k4X4kRFyuwv8bmQaQM6pej8mULKINEO2vRQqYPO2ya6tq29z6St0f91MpEKSHAzsfIKySplq0xBrCzL2vgmKSQsX18TukrUzaZggZO/mYeNLkXdhYgb5PK2wItwVY7CBXXM2t6vRUndtIjhIjq1c3TAnUYUb4aadpAOp7Zq3bIiHDaiXBeoqoClyrPipQLSRBErl35SETXTEDDlFuPc8hTGmmyZMorluXXpshNqq5HluZxVx1QdVsCqOJ63FAmDw08APo1mRGyZMjYpVXFZMjZriuKqTQi+DAlbZEriYQTMmzH4vHRRCsOQF7/4xQDcc889fPKTn+Sf/JN/wl/9q3+VOI65fv16Lap05coVLl68CMDFixf5xCc+UdvflStXinXjiKKIKGrvTAmCsDwSE2CcMoK0fauHHwX0hx7XrznEsUOaGOLYsX1HBzqBIggMQQi+bwgCK1P2sV3m+4YoUHZsUra8GlVpk61iXS5I48SreDJFwHKCMq3OwX6JhlMkrLa/OQQhb2uM/WU+ny8qFzRtStFSykErXUS+dNHWQWuI8+cxmXzlkTNV2291+3GCVkbF0kKwiohaRcrcMcLluQbfxLYcek3uyv1MTW8E0ArjHb5DVZO2BVRUHY28LaDTN06IDitzs0jvYaXuMDI3Q+QN7P+hD1kETTHvnDdtgpHLlxWp8nEuUla8PFIdkMSKQeqiY7s8qbRNVb00sevYQjZN8Srkqilcni16kz/P17dFlp3AX6h85e+Ns+RKjsuQsHHpiIuWsFnGiC1awOYdG7ZoATvMmLBFytdRjAM78nIkWmuGwyH33HMPQRDwkY98hAceeACAz372szzxxBNcunQJgEuXLvGjP/qjXL16ldtvvx2AD3/4w2xubnL33Xcf9akLgjAFA6SV2ZHOPy+g27MXJdcpB156riJNbR8oTXx0qkhihzRxSBLYT33SgUOaQJLaZTo1JGmZ9ub5tpMd+Qo/FyoP/MBky1MbsfIMvl+/D70U3wc3aKmWx2Txgop8FQva2oy5eDaEbETEqgT1sU6uTnGZ/Yt7Hilr26ZN0HRL9MxoXbRTKpu3QlFExGLtoBNGxS6LpOXtqpG0ZhZZLlVei6g1o2hucasLV1PCXNdkY2Q6ramOrsvsaY4V4TisvLVG2w4pcOPTJg9xrrOOf5tF5sYJW5ukTTruuE7wlM5sMxIG4Kdpdvj8u2u2zmmbdBlTileSRbeSbOLsNM2jXT6JcoljTT8po2B16Srfy1y4cnkKvEywPIWn4+J54Cn8rE3oK1tp1FN4M4znMknaOpbroFTfm0XKV1va4VGVvl+keM1amGNR4nXQYhyLEK9FFOA4jHS5ZrZiEEsVpXe+85288Y1v5K677uLmzZt84AMf4Dd/8zf51V/9Vba2tnjb297GI488wvnz59nc3OQd73gHly5d4t577wXgDW94A3fffTdvectbeO9738vly5d517vexcMPPywRI0FYQQwuSpdf4DtxB52NUfLc8kvJzx8Hth/kO/UvLNcZ/QLzXPuFrpT90ThNHFDadiASxy5LHVQKSaLZG4SkCXacUWq306ouW46LjVIFEHqqkC/fA88v7z0PIj+LnvhkFffqAuY7jQuONyph0wSsaDcuQuU12025qFakbKKQVclTFyvnkKcxTrtgWMmav7x5m9BV0xurkTFdkTajdEXiyihaLm2pdhhqB5VQW1fIXhZNmyXN0W2Ilh0zlhX2qKQuOrmIVaNkHvU2jVRIX8d1UfO9A1WGs29c+9/5oPI2MUXygPLWLm1jzq9NisaJ16ToWFO4ZpG8agf4oOmH2WvNx5sFQHeGiNekFLpUjQpXomy6YaI8uy4O2FMeaVxZlm2Tj+lyMIVI5cIVZAUyAk/hm5jQV/iuIqgIVn4/15xkWad4EdLV9t4sQromjfFahHTNUnBjEdI1TxXERQjXSZetaSxVlK5evcpb3/pWnnrqKba2tnj1q1/Nr/7qr/IX/sJfAOAnf/IncV2XBx54oDbhbI7neXzoQx/ioYce4tKlS6ytrfHggw/yIz/yI8s8bUEQDoAxcFNt0nPKFJOP/xef7fMBUQc6Xeh1NWEEUQd6XduBBPAaYlSVKsjESle+rjwIfQ34NCcJsJI1+uVvRcte2ZXKbimgDWkCSnmZbGUd6RRipUn3rYBpFWbFHaykKW3FqyldXkWeAtfKVx7tchsSFuby5dUjXp4X1dLNxkpOQ8YOLWK1RtX2M154MzGbWcpygu7YMWT5hMFt3YdRwZpd0sZF2/IoWlm8g1HBKgStPYqW5tGxZDQ9sipoZTGR8YJWRrgqzzPhCkiKwh95IZCmzLUVCqlG4TzX4KmktZw+gDlI4Y0p6XzjpG3WSNrM0bFJMtSUrWkpiFXRmqeSZN7xnbPQQiFbLa818KYL1yTRMgYrTWkZsUqUS5p6hUzFQ5eB6pEObZs4W5cqr6hm6Ht1ebLRKitVYSZa+fPAs8tCfz7BgtFxXIeVrXHvzWFla1oxjcPK1qyVDQ8jWwcpNX8Y2TpsefmDytY8gnXk8ygdBzKPkiAsH2PgsYv/E1vf8CJ++N//dQD+yQdv4nprqMQw7NviBsMhxAMAKxVhCJ2uIQwhyG7dyApVENp+RhSOfk015QpGBQsq0avm8pbtoT2aVe6/ZRxDFuHSCtA6i17ZCJeVMYdUYaNfmYQ116nUCpjR5eusiZdbRrZCXxVS5bq5XJVtfc8QeGkZ6cq2GzfOZ1apmVXCivZzjNuy7Q+QJjivkDG9qMfEbQ9RgGLatuMELU9z1EoXolWMU9NOrUjISApjszhIqkeic1WqclaVqlqqomsISOtj1Rrj1Koy52Zzm1XFzJbrB2cW4ZhhDNWshUFmrto4jwjNW9jkIOX6D1LN7hDVDqcVhFDaKcQpadzHqcdwaMrlqlyej9PyshTAMBcqTxEFpUgFviIwA6JcsA4gV8VrWcJ4rUVzGubzOuq5vBYxf9fNYcwLf/xfrc48SoIgnBXKK5obOURdBz+bAbs67YFRhiSBJAaTGuIYdvchuQ46dUli26fQJuv852l6IXQ7Bj/wCAIrU1Gos8ceflBKge8a4mZGXCZCzTpbbZJl91FZrutfmblsub69QfmlOipc9R9p6tJlBcmYLEVQA0pl5cyxneJMplQakCpIUjCJRg2cSoSsLl5K2fQaaI94uW4pUrmA2ehWXbzcyvPQS4ptJ5G40Vwi47nzXY48naLmHF/j6QR9gF+j89eh5zzHqpTpGVPfigIhI53/tvMeH0GbVep0rXJjJc2xKA6SV24sI2ha+ZmU2QqOKqmmPpZj15R2atUcaz/LOvYzFQaaKNCEob3veEOiQBOF+U0RBbr4vLXJVS3qNUFcqpGsWVMKp8tVts95xmstck6yRZNOH5OUjwVtFs2YJhFakwmWR5x6xJlgWbmC3WFIsucRZ+uHSSlXvqeIfEXkp6Vk1W4pIYORqJVzwIjDOME6SDRr2vty0EjWrIJ1kEjWvHJ1kCjWYeTqIFGsg8qViJIgCAtB4ZNq384zlLHXN6SmTLHLhQnA9x3wwO/a5XnZgpE55IwhjZ2s8IONuqSxw2DfShYaksS145HSfN+GwC/FKgig08kiLIGVqSCETiZYng9UBkn7lUHOVdFqRrGqsjVOtOz+WtZl0jUS2XLA8QAvsB3mCRGuvPM8Gukqzz9NwdEqEyenFvHSGlTqZuLlEcdZ1KJNvLRNVTTGjrfJO7pWpsqIVy5enp8V2mhZV9wX4gZ6jmi/axTKm/3ylUfD1ByiU42IzSNk1cjYPFLWlMpZpWxchGxWMXNVgheMK+PdZsN1OZsnylYtDpKmNkUxiQ3D2GWYuMSxy26yxrN91y4bGJIk6yj7hm6krFiFmm6k6PoxnVARhbq4dyopcpMiVoVczZAmOFPEKp/7aqZITjBfZGnenlqazF8WPu+0zjvurPJ6p0mE50E3KFMGZ4nOKO2QpC7D1LfylHokmUQNhg43+xHDXLZSvxh/FXiKTpASBVasIj8lCuyyfF0nSPFNe2n6eQVrUuRqGXIF8wvWPJGreeXqIFGreeXqsFGrEblKZ/u7iCgJgrAQNC4KHx2sFcv+8Hc027coeusOnR6sbzjk88/6fhZlqnxXVUXKtgFwwIWwRzYeqWzTlCrPNZkw2Yp5pVjBXj+ropeAUWTt3KzjD0Fosup5FPLkB9CJbOlyz4cgkyw/gChoCEwmWlXJysllqy1dML9MTxItu98J67U/NpUQx3b2PF9PndNm/ETn5WtSChylSBXofMxWPm5LgVZWvNIU9gZuIWfVohr5Y52dcpGe5UHgt0uXn43jChvyFQSldFWjiQAe9uI9q4RVZWVWEWumJM4iY21pibPIWFt64iwyNimyN4uQTUpXnEXIqjKVR8xGj2pw1WB0/xrixCWODcOhFarh0GUQB9y42WEwdBnELunQynsUZNIUZRGqSNGJNF1vaOUqUtTfMnv+i5CqufD9OVIB5933nO1XVKwgj15BR80211aeFjhMrDjFiZWrft/l+l7AMPWtZCU+2ri4jiEKUitSviLMBCqXrPxx6OuJojGPWM2SDjiPXM2aDjiPWM2bDjiPWB00FXAesVpkKqCIkiAIC6UaUVrbSlFGcf05l+FXQKcOrg+dni3uEHagu+YQdaDbdXCcskPu+w7D2D5uXjPahQpyifIDCIO6WLVJgOearDhDFj1JbEpgXtRhvw87O04Wtcqr7dltXdclDCspgblkhaVg5ZEr37ei5TjeeOFRXqtk5cSqXbRy0sOIVt7GTMmnw6YUGt+zkSDaiyyUEa5x+7Ov0zUqk6gyXbCWOphFu1QKg6HOxnf5tWhXXukwly5btdC+981xWnZOrmx5YOUqClQluhUUbcaNifAavdZpEtYmKdMkbNx4sGkSNm5c2DQJmzY+bJKMzZJeOU7GmgJWla5Crrzsf7kH68W60U6z1tgo1NBGqAZDj2Hssjv0efamS3+4yWDfRrXy6FS3Y8WpE9kIVc+3MhWF7XMUQTB1XJVxF59aN9fwnDSBWQtw5Oc56zxZ1dc+a6e12WGdVawaEjmrOPgeeG5KN0wZTbBuHEI5DBKfYeIzSDziTKL2BiHP7XUZJD6DxCdVHq5j6ARJIVDdMKUbJvZxYB/bAkPTJeO4pGqe8VWzStVBxlfNKlWHGVs1i1S5arYSDSJKgiAsBIODxs5qn+OFQ3obWXlwz0VrMKnLcGjT5faedbj6FYNWVpKCyApUEFmJ6q05hJG9jgd+2V0YYoqIFMAwni5T0HaNdvA9cHy7zu/YZW1S5XvlOKK0kCYHtB1rpVKIY9gfZJEsVUa1tLEdb5v65xIEmURlgpWPtep0TCFazc6672rSCTGhaZKj9GRR8lxNOlZsKqI14Th5VEuryeeSj99SeODZfp0bjkpXPaVwvHT5jkLr7O+SpRjaSoaVsvFZFGuYpRfmpePTlFrEKx9HYwVLV+bmKkXL90vRsn/TbFJk306enBfYAFDO+MtsU7pyJsnXJDEZJ2DTCnGME7BZCnKMk7BZinPkAtb2mnK5aotmaS8YTfnzIAogWsslq/19ihOHwQAGA5f9gcdg6PHcXshT11wGw23iga3+1+0oOpGilwlVr6PodRVr3ZDIiSe+LmeW/t2MkyU7KsHMIBdFdGoW6Sl+7ZlzfqtVE6oxEblZxMEoO4H5upew3pn8Wc2FapD4DGJ7v9932d1Zox8HDBKfpCJT3TClEyZ0g5RemNCNEnqhlazqd/oskjGLVM1asGLW92UejluojqJQhYiSIAgLQeHjeB4JZcnq4TBBmyF+kKWlBbazG/XsfQ8rUEaD0baAQzJ0GA5cbt6Ap4YGlIPrQdgxBJGh03UIIitTvZ5Tu3ZPkiloFyqYTaqGiZUqwGYDZlErzy3HV9X3WUlXS8toVJoCeac+sVGrdCcTKuWQxPbX8Vys8sp/YeRknfJymV8RrHHp1rngjJOsPIqlJsiN5+gi9791fXGMGUQLxspWM31wnHA1C2Wo/LVlkTsPG01sq1I4Trj8rHebVyTMUwlL0bIRriQFlWqGu7YDlSQV2Uqdou/muhSl4cNAZymdlQiXD4EfltKVSVjg2yjXuGIZ48RrnHTBePGaFg06qHhBu3xNi3iNSy1ciEhl+B5sBgmbG+2ltbWGwQD6A5f+0KM/8Lg58Ll6w2Ov75EkLoGbWnHqpKx1M5HqKtY6Nho1S9bmomQqHz+1MjI1y/7bonLTOtxtHe0jkClgJqFS2qEf+1acYp9+4rPf97m222U/DhgmPo4DnSChl4lTL0rohglrUUw3SAn80R+zToNMwXShOmjlv6OQKRElQRAWiEN1JEz/5hCzFqJV1pFOyt5fU54A/MjFj6w85WgNRrnEQytRe3suybWskIOy1/Eggqhj6PQcwo6NRgURhMGoAAV+M23PQTVC8J5nIxO1dp4zcs31/dF2tm09BdHuNPvFO3uf6u3rx9eKsniFMtnYKlvQYn8AaZyVJM8EzBgH18WWVM8rAWZjq4LQK5dHWaSkcry0JdDUTNNrSlYzRbBNspppgm2i1RyX1SZarSmDLaLVNkarTbTGlX8vZCsbROP7ELWKFrTJVj7hcB5xTCoTIOcClSYuaQpJqukPymhXkqd+ZpIGVrSsDJusqqMpZKuMSNrHQWX8XC5Y3pQJTaEuXZNEq0lVvOapbFgVrzbZUq4/dvzW2AjVGKHUrn+wUvAe9NyEXk8DGqgfVyWa/Swatdf32e+HXHvGY39g0/08UtZ7irVuynrPPl7vWaFynDKl0EyQCWcWScnkZFaRsscc3+UbGS81SXaaojNNpo5LpGC6TE0YJzZNGqrC4LmG9c54mdIa+nHAfhzQj3129xyu73V48rkN9uOAVHkEnmK9E7MWZbdOzHoU0wuT1h9O5pGLSTI1bwn1ed6Xmfe5JJGC8TLlznieIkqCICwMQ70E8DNPGrZujQkig599WeWC1JQnP/BIE509rsiT74KrCbsQdusS5bouyZBCom7uuCRP23majCnHQYWRIexA1HXo9Ezxi6/vOSRpvdMf+G1CNLtMwei1t5g7cqS9vW9KlediK5EFME2qjMmiGYmdADeJyW4O+zdsOmAaU4zjcR0IIocwBD+081iFoSnmrQojCCOvUWSjfsymXLXJzDS5glHBahuD1RSscUUvmpI1dkxWQ7LGFsGgLlqT5teCimgBXqAqY+TaqHy+nUa1uzzamImTFWSHNHFtOf1Us9/Pq0Bm95VIVh6xCjKRCjPJCoLK8tBk6+z8Za7bOP8ZJKt43ZlszSNaYGVr3PituebsmvRnmT7kbnQTlYwtUGErBLpsBLDREpEySrHXt5GnvX2fm/sRl5/z2d237+1aGFt5WlNs9FI21xN6HV3bPyxOomCySB2JRMHM53rg/cNkkZrUyZ4kUlOKbUwShqYsuC6sdRLWcpG6tXGKqcveMGB3GLI3CLmx1+HJ5zbZG9pvkW6YsB7FrHeHrEcxG90h69H4aRqOS6Jgvvdl5n1O+Psuex4qESVBEBaCwSHFw3XKb+6bzyn2b3p01xzWthO665AmKX7gkybKSlOSzVGjyg5DmzzZ5y5p1kv3fRelNLjQXXfprkPecfE8Wy7cSpQtL7v/LMQDO56o07FV+KKuJupC1IGo5+A4kFaEKBefqkzlEanqNTRP8avKVPVaURWkappffR/j2lOjKlWFM7rgRwAOYS/frkVM0mzeKl0VKti56ZA+a6VKJbaz7gdWmqIIwo4dKxZ1bJpfHqEqz7dSWr3luFDK1aSiErlcTSpqkcvVpMIWUArWtGqCuWBNLXaRz+cy5bhQCtY0uYJSUIo0QceO1wrDcaKVn0e7YCUVeUoSSBKPNIH+QJfSlUIc2zRPoIhOhSGFQHUibYuVhKYoWpLfj3sd8wgWWMmaWbDmlB5PJ60ph65RE4tLTJKkSevAdhDX12F9vSFRWtkI1L7HzX2fm3shl6/12Ot7eC5s9QZsrVtx2t5I6HZ06/5hvETVikyME5OGlIyTqGY59GORqHEFMcbtf1KRjUVL1AzVCsfJwjhRCHzNtj9ke61egMIYbARqELI7CLm55/PszR43BxHGOKx3hmx289uAze4Q3zMLE4ujkqjDTOQ77rUuSqBElARBWAg2muRgKhGQjXM3CULFcNjh2lNdcGBj27C2FeOHLmn2RVsTJ4DscZzJk5sZQbVNmugi8lSVJ8AKFBB2XcIu5J0W33ftgP4+xAPHlox91kakHAy9dYeoY+htQHcddGhwG+OXUmVGxjQlqRlJ6St/4a8vV8q0jpNKVftYqWqkoH2b0eVghWqkKIVTClV3fULFH20YDuz7Eg9hOID9vfy5g9EUqX5RVnij04NuDzpdh253/L5T5Y2VqaKNni4uKZOrBOYo5U6VqrmYo8M+1wV2jlP0XEVqGn/4TLCiME/vrJxHIVWj0cgkttIUx5lkxQ5x7LDfhxs7+XPbRusyJTDMBSo0RJEtfhB1XDqRsRHc8eG08nWgUI13ySOduZy7Z9KxY6GUO9p5miRP0F6dL0/fa5OkXGAmrbNPPHo96PUUt2XfRY6xBUhu7vvcuOlz42bAF57scnPPx/c1270h5zZjzm8lbG+0Rw6mCdRIhb4FC5Q97uh7NrbseZvkjBOcoxAoaJeoaR3sIxQox4FelNKLUm7f2i/bG9gfBuwMQm7c9HjmZo8vXD3PMPFZi2I2uwPOrQ04v77PRieuFZFoE4uDSEWbRB1EnmC1BUpESRCExVLpiyXDGD8M6fb6dLp9VBpx83rIznMh69sxm+fthSDNflXOo0328ag4AcRKt4qTfa5raXtpqgt5AitQnucWaXyg8H1bTCKOIe7baNbTTzkM+zYVrbth2Nyy4pTPARWr2QQKxktUU6Ds+bVLlKW9SLDdV/sWc4tU5VhRzxD1xmybgk5NKVJDuH4NLn/ZPvY9x0pTVga+u2bvo47t8MWpYyetncgsRjJDyXPXoGYoez6PqMyC72rSWY6LjVLpGdsCh5eqCm4InZCiBEs9UtVIucwqOyaxjUxZgXJJhprdmz7DocNgaCsKep5Nee10DFFobNQ2MnS6hm7X0Ouqkc5wmzjZ5aPy5BrVWtiiTZ7ycU+rJE/G8XA82NwwbG4k3EmCY+zE0Dt7Vpye2+nwxcsbGO1wfivhls19bt2O2VhTrcdoFrCYSZ6gXUpahKRNoI5UnmDmcz3w/uFg8gRjfs06eBGFSZLgOGUq3/O2y+XDxOPGfsSN/Q5Xd9b47FO34jiG82t9zq/3Ob++z1Z3OFL+fhXlCeZ/X2baZ5DPnTbbF6mIkiAIC8LB4KIrnReVJqg4LXKI/GDI1i1D0jRi97mIvRse5y8O6FQ65Lk05VEm+9irPa6l6VXOYFKqHlTS9TI8r1zv+dDdsNe6c7dnc/fsQzzwePopG4WKurB9u+H8rQ66MWbJ9Zxa2l5xzLHjoFra+g5t1wA7HqpdLNqKTNh92ftxxSYAVMt1IpenVLWLmZ1vCDzfIejAWmO96xgGffveDfqwuwvPXLXPDbCxAZvbsLHtsL45WoUwF6hxx8/PAaiVoh9pk1fiaxbVGGmXVf2bICrViNSk6n9QqQA4pV01YjZNqEaqAU5p79bOd3p0plodcJJU4dkfGHo9RVOiqs/T1EYeBwMbiRoMcply6Q8cBn0bneqtGS4+T/G859m5jBSj4gTU5KltEuHaRMHVAhUmF6R2cbLrgsrysmPYJmbFusr+ZpoLqmVd23rjeLg+bG8Ztrdi/gfTxxgrTs9eD3jmeo8/eWIL3zfccfuA59+yx+Z6OvP+YVSexs4L1RSSMTLSlKc2cbLHrf8NJk6225SbaWIz47keeP8wKk+zSkRTnmadZJhRSZhFEKJAcfvWvo0+Pc9Gnm7sR1zb7fLsjS6fv3IeYxxu39zl4vYut2/u4Y2JzDfl6aCpbMuWp8OK0zRElARBWBi2kEP5pes4hjT7cvVCH6UUnufh+0NuuUNx87mQa5e7nL/Yt3PRBH5Z5GGKMOXPdSU9r7luXmkCam3WNl3WNlUhTrvXHZ59yuXZJ2H7NsP2reC4VnzaxAmmj3mC6rin+vJy7FNtcWP8U2ObMQUl7P7ybVrWzSBPdtuWUuqVVDptysqDm5X1xtjo0+4O3LwBT/+Jfb62DtvnYescbGzZ7duoRqAmSVzRZoyojFT0GyNS1bS+WSUKxovUSIW/GSSvaDvD3FVVJolU27ipSTLVVmJ9okwBvq/wfUNvDepCVc5FNhzCzR2Xy19x+bMv+lx8nuLOuxTdrqnPs5Wfx4KkCeriNIs0TTpGM+JUFadp4jJJqsCKE46NOm2t9/nqF/TRGp69HvKVqxGP/dGt9LqK518YcOdtuwR+o3rmssRpgohUxWmcNNnjHpE4zTL57ykWJ8eB7TU79umrL5TidPlaxGefvJX/+mcXubC1x8Xtm1zY3B1bHAIWJ052X+X7cBhpguWLk4iSIAgLpDEeJ0mLa5CK05osqSRl4xxcf9rl5jWHcxdMLfVOZyl2zXFM9nE+TqlMvdMt45nqbesV9caNa6pW1au28XzYutWweYti7wY897SH4xi2byvFp5pil4tTNUWvTZqgvViEPX6lfWV59TrQjMiMO4bdX/l45Ho9sYBE/XkzZa8pLiNV8rL1XgBbt8Att9v1w4GVpt0bcPkrNtJ014ug02GEpkC1pe5NOw9oF5TWqn0tAtVaua9FSmap3gcTKvjNeI4wXqLGFZ0YJ1Fjy6aPkaj2OarycxozZ1eW1uc49m/c6Whuux12bxq+8oTD730y5GUvT7jtttFzKopeVIok5NJULQhxlNJ0kPmdoF1YZpImwPHgtvMxt52PSdNdLj8T8eXLHb7wpR4ve+EeL7g4wFFjJjKecPziOBVpOCppssct/w4TpQnqYrNoaZp3/zlViZhHIGpVfA5epntWOaiK08vu3GGnH/LkMx0+8+RtfObJ23jxhWd5/rmdicJU7KvymldFmuBg78skRJQEQVgg0wfX11obbGGAcL7tlkk+jqlKdayT48D6NvT3NPu7Ltu3lefeNh5Jt4xngvnGNNlzGJ1A156vvW8vENF+DLs/ez+uSARMGt+UHXPMxTQXlrEV8LL1UceOXbn1gi0q8Gefh0/9PrzkFdBbH398KMVp0linqjhNKiCRi8nUAhKZPE0rIlGVp0mFJGYtfV49x5yp5zpHGXQYFaip5dAbAjVJnMpzapSMz8RpfcPw0lcYrl6Bz3w6YPjVKS+4U6GNO3Zy4WnCBBNKkDvtJcjHzuPktk+IuwxhmmV9Lk2+r3jBxQEvuDjg8jMhn/78Bl++3OHrXnGDMDCHEiYopWmsMEEpIlMkJJemacJkj+tnx52h05xLzSxCM+O5Hnj/OblAzCsPRcj/4NXk5hWDzW7M5p0xL33BDk8+t86ffPk8n79yC694wRVu39yfvoP8+CsuTYcRpgPMMiAIgtCO4wCVX/69wMfPvkC9MOvMePkvoz7Xn+6ACVjbsu3zaJLruUVkyA/8Yrl97rU+rm6Tr6u3dWuFHoBaoQd7bu6IJPm+W2s33IcrT3js33RZ36xHgtoEp02SoL3CXb6f1vZjltvzHrtqIpOmEJkkKTBeksrt55Nfzy/7JPlA47YUwSbjUvUOwrRxRWU7Z+rYp5yZCknkbbU7dQxUeQ7uzOcLVpxmLS4BVpzy2ywo7RW32c+p3vb2C5qvuVvzpSfK5ePOoTmeyS4b/UBrx2st1qAcv7UYhHL91kp6dl17Fa1xx4D2YhDFOi8YW3I8Xz8JUznmxVtjvuXrr+F5hs/9mR05aLzJv4VPO35xnGkTxIKVkGkTz2KFadokueVx5/gt3w8mlyavMuO5Hnj/OXYys/m2AbLJ0ObfDisG0yaEbd3Ogeef3+VbX/UVXnzHDr//xTu4ujOmms+0fR3kNbfux59YnnyufR3wfQGJKAmCsFAMlWmU8HxbxcEL/Zog9XcD9ndCOj3YurVP2KkMjq4IUk5VeJrP3abYjLSdLEdAqxzlaA2DPYiHLv2bthre1q2Gi19lO/erKEfjjmP3OX47u+3k9YsQpLxNdczSznX7ml7+Wlt2fNp5wORo0rznBNOjNGW72SVwntLk0+Z8Gj2POdsfoEz6LHNBLZJOxzAYuCh18B8AVg3t+q2RpZm29YLJqXKOh2Py6Q8MX/PCPT7+++f4mv9hj8A3GM8fG1ma9RhgZWliZGlOjBfMHF2aKbJ0EFxvvugSWFma930IgvmjS2C/rA/42h3PO1AUxXHgrlt38D3N7//35/Ntr/gCoT//d4ATBAubx8gJ/IVEl+Bg74uIkiAIS8MN/FKSnID+bsBwv4MfaM7dnrC25eA4fiMS5Gf3B5OjphhBe+RoXBuVwv5NSGIrRv19K0S9ddi6xbB5ziEIG+NlRIymttEKBgPo75XjkoZDO/fS5ja88MWweW78TPNwMsQIzqYczZJ616Q5aW4Sw2c+5XLnXar2eW87n3kmt21LjQNa0++A1vS7gx4D2tPvZt7vFJlwGsd95rmQrY1kpLDDYY6xDGaRJOH4eN72Ln9gbssi3Af7sWSRsrRI5pUlESVBEBaCg8Z1NE4aV5Z5aN1jbydAJSFRV3Hr81OirsbNekKzptW1LbfPp6fTta3XKptAtQ9KecQDW1wgTewPgOtbcO42uGPdzv+Tv6I2KRIhKmVomL2Pg769DQe2tHoS2x9w19dhfRNufbF9j9vO+7iEyLY9mdEiWG0pglExylHKzsH1Z3/mc/685kUvTieeW5skNccn5SxKkprjk2Y5BkyWpGmCMo8kJanDn/zpGl+53OHrX3nDrp8SSZrlGMWxZomizBidmUeS5oomzRvpmTeadJBjwMGiSXDgaBIcvojBn3x5k8BThP7B97OKkgTzvzciSoIgLAQ7i5Kpzcmwe32bIOqycc6wvp2Sp8z72cytbWI0T7RomhR5rkuSQBpDMnTQ2iEeOsR9e+3yPOitOURdw/YttqR11IEgWm6E6DhkaJoI2e3HS4LWgDYksZWeOLbvazy0kaJ4YJf7fja5bNfeb5+z72mnW07YC6dThuD0RIhg8UJUxRjo7zs8/bTL5S87hKHhFa9IOH9LVr1yxijSsgXJrmvv8B0mijRJUOYRpDhxePKqrXq3uZby5+95jrWuWpgkzZxuJ5LUzgmTpEHs8Zkvb/HMzTXuffGXxs6xNPUcFihJi0q7g4O9NyJKgiAsjdvv1PQ2FUHoA24hPW1SNE6IpsmQ67okQ0iGsB87aOUWnfk0BhzodBzCCILIsLUNnec7RL2qeDgrLUHLjAZpDUZZAerH9rqeDLP7XIqGtjohZOOTQ3sLQ5s6d+68lcxOF6JI0uPgaAQIjjYiBLNJENjPTzx0GA5hOHSIY4dkoBkOnGLSWYBz5zQveWnKrbfq1vf5JMkRHE0ESSm48mzEU1c7PHM9YGs95e4X7XLH+T3bcMIpnio5guUL0kHHZZ0wQdodBHzhyQ2efG6T2zb2+HMveYJuOP+5nCZByhFREgRhYbhonMpVevOWgCCM8AOvVYrGCVFVhowGrV3SGNLYRoXSxLFRotheV8LAIYggjGy5aTuOCILIdugdJ5eUuqjMI0EHEaCjlh9j7BirJAG0QaX2Op/m943HRtvnjmMFyM/kJwjsPDebm1Z8wux99IPxY4gk8tOyzQmI/sB0+Umyz02S2P+9JHGsSCcOJtX2eUyxPI4dtLb/M1HHEEWGKNSEEazfqul0Dd2uoddVI5+ncWOPTmJqHSwmcmQM3Nj1uXajy7XrIdeuB3Q6muffPuDuFz5HrzP5MyNyJNGjNvaHPldurHPlWsRze12ed26Hb3rpn7Lemf91LEqQFilHIPMoCYKwQrhoUAmBU37RhR2f7pod4JNLUVOIjAGHMgqUDG1UKM2iGygHxy2lJ+pAb8122oMQuj2nUrXVCkubAM0TATpI5Ocw4tMmGSq1vxynKTjGjIiOStulB6zM5NVsAz+rzhfC2pqxlWfzdUEpQHlJbhGelm1OgfAolX1mFKSpk32WnEyAPPuZSjRpakUoTSGJ7b3W2Y8NAQSByaoeG3xfEwSwtm7vo1AV66PIzFTheB4xmleK4GSOOVIKdvZ8ntvp8tyNgOdu2JzVc1sxt27u89I7EzbXD34esHgxgjOYVgcnSo60hhv9DleuhVy9sc7eMOT8+j4XtnZ5zVc9RSeYb7+nWY6qiCgJgrBQnMrYk95aRNT18TzXduaVy3A3K56QpcsZ5WC07YSFXVsaOlyzYtTtOvhhObalKUCzRn7mjfgcdKxPVTK0ttfrNLUd1GEKGFMITvU+78RiymXGlOfoZdNqBIEpJKcTZVGg/JYJkOdDNMMEvqsgOqsqObA643pU8RlySFX+ebECozLpUSnoVKOUWyxXmQSlykpR/nly3fwzlQmPb/ADQxgqom4uQqaQovxzN8vUKPNUoqtvd/KkCA4XLQIrRnHisLPrs7Pn2/vdgP2+h+9rzq0NOL814CUv2GFzLS1+zJj3OCARo0MfAw4uRXAsYhSnLtd2uzy343N9r8P1/Q6ua7htY48XXbjGbRt7BHOU/j4rYtREREkQhAVhe2I+5ZfpzjMRQRiRxg4qsaldUddGhTa38nFDWaSoUnK7KUCzyk+b+IyTnmnC0xQdXYnuKFUXnLxzii6jPDq7/riOlRfPLzueufiE3fJxfh+FprasrXN02uf7gZMZxVEKdGKjNlo5KF2KjVL2M6HScjlaZ58dB5W6pFk7lQlOVZg9L7v5Bj+7D3yN59nPVRSC52Wfr0o7PzB4Hvi+XdeWOnlQwanv4xAlsI85fQ6WHymKE4fdfZ+9vsfevsfuvhWjYezS7Si2ekM214Y8//wem+spnWj0s3zqIkWwulIExxItgoN1/OPUZacfcbMfcf2mx/W9DvtxyFoUs73W545zN3nFC66y3olbrykj57CiY41g+WLURERJEISF4JMSkBCYYbGst+6wfc4l6sH6hpN12Mpv6aYANeWnKT5t0jNOeDy3Hr3JxcYxxspMI5rjmHqkpyo6rp/98u7bEte5xASejezkaUZVwQmy6E7eMZ1FFmYVm2VJzVEIzXHKjNaUwpLLjMqXlWlmueRoBUbpYr3dhkJotAKl/GJ/pvLny6XECosunvu+tss9KzZhz4q555f3npfLUCk/wSFef5XDCE2TafIxC4sstDDtnJYtQ0nqsNf32M9ue32P/YHH3r5PkjpEXsL6WspaR3H71j4vuiNlYy2tzXlUHGfKW3ucESJ7/CWmzoEI0RiUdtgdhNzsh+zsutwcROz0I+LUpxsmbHaGbHSH3HFuh3O9wUwRo7MmRfnrNTN+XkSUBEFYCA4Gx9EElXkXvuZuj6iTjU3yqhGjcruq/LRJTyE2iZ2PRykwlUIFudg4lGN4cgGCrFCBb0YiN34A3V5ludcW3dETJ0CdRWxmlZplCc2qz63jaGXlpCIvxXPdeK7A6FJi0KMSo01ddJRy0NofERnXBd/TuF4pLUXUxmvITACua9e7Xik0rguhl9ba5vuY+/1bMYGpMind7cD7PGIRgsPLUJpo9gceg4FLf+jRz+4HQ5f9gUeSuIRewlpH0esq1roxt2+lrPUUax2FnwnRyHHGvCSRoQUdB05U2pzW0I8D9oYB+8OA3X2HvWHI3jCkHwf4nmKjE7PRGXJha5cXX3iWze5w6vjSsyZEsJjXLKIkCMJCcSi/rNc6Dp2eFaRcgrQCnZWjThMYalMUbkhiQNvHuQAZY+fcKQQmG5MThYZOCH6vLjlRqGsRnWqndZrYzCI1swjNzKlxS47GzCsvStmOqFZlZMWKR/Zclylk1fW5pDimLi4mlx3tWGlp7NNeG8vLkOfZebhc1wqJ54LrZc9du775OJcY14XQVzYC6I229TwbZczFaFL6iUjLIvc9X0fluCJCaeowiGEwdLObR3/oMszu+wMPlWh839CNFJ1I0esozq0l9G6xz9e6isA3449VeWkzSxCshgjBaqfJwYmKDCWpSz/x2a/IkJWjkEHs4zjQDRPWopi1KObi1i69KGa9E08suiAytHhElARBWAguCt9RtS/D61c1nqdRiZ2LJ4ltNMhxsrl4Auh07MDx9R4EW9Dp6LIyWyY/nQlz84yTm2lCM1Mq3IxiMqvAzCIu1TQupcAzuhSTliiL1vUoi9Z1YaltmxXOUDqTmIYI2UlgM7N0MslwDU4WMSmiKm5+KyMvrmsIXI0bGDyXbJv646bkNPfpOEcoKXPOo7gMQYHlScq8gjIvixTAZUaC0tQhjg3Docsgtrfh0GWYuMSxyyD2GA5dVKJxHOhEim6kiUJFN0rZ3BrSiTTdSLEeDIqo0FjUCRwnBEcjQQc5Ts4xRYRg/g5+HhHqJz6D2GeQ+Oz3YZAt68cBSrv4nqIbpKxFMb0oYWtrl26YZDLUXrzDJAlmQf/aixYhOLkyNAkRJUEQFoLjgOekqLj88vWMZmNNs9bTxVw8vW69glZTdMYJziSxmSQ0s0hMm8AYU1auy8XC0XqmtLBcRlC6iMSU+3IqkZZ6hMUYM9KBd1y3iIRYcalHWfJoiu9oXM8Q5CJTERbXNdl2o49dB4IsfSzf17SIy9j3+rCd58prX7SYiJBMOc4So0ojx5qh0z+ujTEQJw5x4pLGhjh1GMYBcWzlZxi7xPn9wKC1UwhQJ9SEoaYTKjaihGhD0wk1XX9IJ1SEwWQJctKEmTx+SQJkz2HJUSA4OZEgOFIJMgYS5TJIfIaJzzDxGCQ+g4FhEAcMUp9+7BOnPg6GTpjSDRKiIKUbpmxt7dIJE7pBSidIR8YP1WQgnfu3nNHzFREq0C3vhU5mey0iSoIgLAx/0MdUJnV95SsS1tfL4g5N2WkTnHFiU5UZY8qCCyqFgXZwtcoG5lfSw1R9PEua5hLjFONZ8ipk+XgWjC5kporjguc6NWFxXEPg6XrUxbFSE7ngZBXHaulguZhkbfOoSlN88ihLG8scRG/fYOa6Si9DQpYlIGdVPFb1mEpBnLgkqUMSm0J0krRDnDgkqUuSOHb50JAk9p/C82zVvyiw8hMFmjBIWd9QdL2YMLBCFIW6ViyhykjkZ9yf7CyKz0GPlXNC5Afs9SCXnmHqEWf3g4EhTnwGqZWiYeJjcPA9ReQrIj8lyqTn/Po+ndA+7oYJka+KH5taJcBw4MjQSRIgOB4JWiQiSoIgLIzIHeCqfvF8PRjQy6o1eK6ygpNPmpo6JKkqJr3M54lBaTtGKSuPXC2ZbLQhVXWJcYpB+Q6hp8rB+XkUxrGVxzwXwsiKSeCmWdSkrGRXjI2ppZdlpZUddaAIy2GiIp5O4WAF4lr2tfgL1TKF46gk4yQKhjF5tNMhD0AqA8bYqKWhXKeNYztkWXuMam1njI1q1h/b/zN7jGybynqtg+L41f1Uz03Xzsveo5SNsBrHpoIW84UZwsAKTxBoQl8TBoaNaEi4bggDG/GJQkUUjBZZmZjuVv04nXTpgTMnPjBfRz5VDnHqMUx94tQjSV3i1LPLhqZ8nAmQNi4O9jMW+VZ2Ql8RBbZoQi5EUaDoBCmea6Z3/g8QERIBqrNsAZoVESVBEBZG1+nj6JvF8y9+JiUIUuLYIY0NSWo7bo4LkZ/auV2Csiqd5xlCL2WtV50TJlvn5mWS01qFsTaBmUdQpkZDxkRXFiEfRznO41D7PkKhmOdYbdJgGtKQd96b4tCUA7RCV9oa42QSUNkHNr2yaNMQiLqk1I9NcbzItqF57g4o1fJ6srTN7Nwn4tjqj65rxwG6jr13HPsjgEP1cdbGBRwb6XSK9lm0U6fFvpyiTbZfd/Q4xX1jOY11rpOngNqoUOBnx5i1I5/O93+zcuN7ck6a8MCRSk+e6hanHokKsnuXpJAeXTyOlUeiPJLUQxsHB0Pg28hPmN0CT9EJFFu9AaGnivVRkOLr4Ww/himY5Wt7GdIDy59D6CjGAh2lAOkJr2fSuipLFaVHH32Uf/fv/h2f+cxn6Ha7/Lk/9+f4R//oH/HSl760aDMYDPihH/ohfv7nf57hcMj999/P+973Pi5cuFC0eeKJJ3jooYf4jd/4DdbX13nwwQd59NFH8dtmlxQE4VhxK5XlbtkcsL2Fzf8PDWFgCLJ0tIPMn1K2SWyncYbv24PIyCKkYxly0bbPsmOfjYVqdNDrv/jbDrlpdP6r6zG67KxT33fecScbv2XIIwj2XErRKGWByv5pyEn1+LkkGBO1SERFfjIhmeXnWqel819dVpeDoOzMm7QiEqbxuLLfTDiqAgF2DFn9ePXHTkNMmiIB9XOrSUaaNM6lut8DfrCWwawSYIAJ/ypLj+rA2RIdOFLZUTpLn1QeqXazxy6pyqRHecSZ8KTaLZelHkrbsKHrWOkJPEXoWenxPRt1XI/iynNFYKwE+Z6e/f/BABPaivSM56ijPrPKzSJZqml89KMf5eGHH+YbvuEbSNOU/+V/+V94wxvewKc//WnW1tYA+MEf/EF+6Zd+iQ9+8INsbW3x9re/nTe/+c389m//NgBKKd70pjdx8eJFPv7xj/PUU0/x1re+lSAI+LEf+7Flnr4gCHPiOAa3IkAve/51er1ePfqi7e0w86EU7Q4gI06aFB1unctFJRWpGkHI5aDaxihdCEEzxamaYmSMN9KuLU2pSIvKownGqd3XxaNbO695IwyOU3b0PZOWHfGaALjtYoEp90PZoXcq0Qmvuo3bLgkjspL9DYtIR+UcmzLQlI3aa2NJ0nDQzvCyCMYsHxP5nFc0jotTLziwMpIDs3XSU+WQKpdUu6Qqlx0HlT/O1sVDXTy2UR17r7RrvxuxY08DXxN4isDT+K7O5CelE2g2OkO73Ff4ephFgGx7b8aJuCe+3iV26E+D8MDZkB4Ak0ekZ/y7OcaYw38CZ+Tpp5/m9ttv56Mf/Sjf8i3fwo0bN7jtttv4wAc+wHd/93cD8JnPfIaXv/zlPPbYY9x777388i//Mt/xHd/Bk08+WUSZfvZnf5Yf/uEf5umnnyYMw6nH3dnZYWtri1/wXkTPOcBMgIIgzMTlzZeSvvpu/h+/+Y8A+LPf/c+s9bojQmMMmDi14490XoDBsfPv5KWuU2PXaSerJpcVaTBOWUUuT0vSDibVtWVKO7UIi1IT5MIB36RFx7wmFo10obGpRmO2cwDPJCNpT1UpqG4HFOvaogqjAjMqC3mp7ZXr5Fc4KR34cRyoY39SOawgHIbDfIZPkdjA9A65yr4nc2lRmeCk2iFNPbsuW57Eyj5WZbSnKkX2x5hScvxCbuy972ZS42orOJ7G10OCLJpT3tu27iFk5yRLDhyd6MDZkR2oCM8BuTmMeeGP/ytu3LjB5ubm2HZHmrt248YNAM6fPw/A448/TpIk3HfffUWbl73sZdx1112FKD322GO86lWvqqXi3X///Tz00EN86lOf4mu/9mtHjjMcDhkOy0pbOzs7y3pJgiBU8EhxvfJ/74//0KXbcUiHgS3nq5ysOIO9ADtaZZXf8olGy8deMQ+PxtVpsTxwTCkunim2L0XGFPP/uK7BNaqQDrudqYlILhnzctwdfSetjBZeUNGHI+E4O93HxQoL67GzCjIDRyY09kccN5OaXGicYlmqraSkyi3Wp7FCGRvJyUUnFyJlRsXGweBl1Th9t5Qc39XZco2nE7qexvdzmVH1bdxSdub9fqyJjWHmcT0z7/8I5AaOVnDgeIoXHKfowOFl5yg4MlHSWvMDP/AD/Pk//+d55StfCcDly5cJw5Dt7e1a2wsXLnD58uWiTVWS8vX5ujYeffRR3vOe9yz4FQiCMAvVeZHOedfZikLCdY3v2nkjfM9kN/s4vwgfRDwO/Kv+JLFYhY78ae5Yr8L7e9I55s7NkXIEApNHn3Nh0abtsYPOBMUuy2QlUcVjrV0rNA35yfdTlRnICma4mbi49rGfPfY9javjYl3oWqnJozP2vtK+IkSzRG9mitJk40APk3Z0VFIDZ0NsymOL4OSoA/4dZt3uyETp4Ycf5o//+I/5rd/6raUf653vfCePPPJI8XxnZ4c777xz6ccVhLNOx9lnt/L9/TW3XWGtE9Ub5ZM2LnjixplYlU76WerozsJZSmM75RykY5xHWbRxytTaTFC0CQox0ca2y1NxDU5dXJK0bFtZXu4rEyJTik0+hiYnH4vnuToTGVPIihWXLIrtpll02hA6Gs+3cuM5pfi4+X0uRI6pSVFbpOZQaWZzRG+OUmCKYx7T995xl5k+bqnJWSW5gYMLzlFzJKL09re/nQ996EN87GMf4wUveEGx/OLFi8RxzPXr12tRpStXrnDx4sWizSc+8Yna/q5cuVKsayOKIqIoal0nCMLyCJyEOK58rezfBB1P3mjFxgHMQj6ysyjrnD/OOl1aqaIAQ7ENTq2NXTb6HKhtW90/lfbVtiPn1dxmQttqe0xLu9rrru+3tu2My9tew0zn1rIf3fi7jzvv5rpZj10y5tzMaE937H5b9jrpuNXXPW39gc+p5aQm/t1mOOZsr7N83hQVyMfgZbKiUhzX4Dm6JjDlLZvwOWsfOIaOZ3D9iuBU2rgNWcllyHUMroqPpnJgVtDGFoc5vs7rcYlLznELTM6qiAysnszkrLrU6Hj+89PxbO/1UkXJGMM73vEOfvEXf5Hf/M3f5IUvfGFt/T333EMQBHzkIx/hgQceAOCzn/0sTzzxBJcuXQLg0qVL/OiP/ihXr17l9ttvB+DDH/4wm5ub3H333cs8fUEQ5mTd2eFL/fKiY/YHYHSZT68d0sTUfuFV2i+qz5nGL8q28ptTVIBTqaqVqdaFUDi15cV9JjK1stWNdVW5aGtjqcx1MwYzg6wVaYaY2uPqOvu4PFJe4KFY7bS0x4w8HzlmYz/585HHjefjt5l8vOwdHXsezX23rWs9v8afoHoMd8w5tZ5z4zWPO94s5zj9vWg/n2qb0WOa1nbT1tkFBhJVWT85eWrcezHptY6eQ+NzOmH/1eeF8LgGL7tfFiNSklcK1PVFy+S45aTKqohKlVWSliqrKjA5qy4yOQcRmuNmqaL08MMP84EPfID/8B/+AxsbG8WYoq2tLbrdLltbW7ztbW/jkUce4fz582xubvKOd7yDS5cuce+99wLwhje8gbvvvpu3vOUtvPe97+Xy5cu8613v4uGHH5aokSCsGK5jiNwygvSrjz+fTtBBZxcZW0JaF50ip5KWUvuVOFtOmhTP7Xw1hmrJ6GLeGsf+gmwrv5myTDTlfbVSXE51H7VtoXZfXZ8/hoag6LTevnKMlZrfRjgbTC8Iu3qY+Qb9r5J0tLGKItJkVcWkyaqLSpOTIi5VTqLEVFHxfJ/lWdsvVZR+5md+BoBv/dZvrS3/l//yX/LX/tpfA+Anf/IncV2XBx54oDbhbI7neXzoQx/ioYce4tKlS6ytrfHggw/yIz/yI8s8dUEQDojrlBe0b3zeZ9js+kUVpVl/LV6pDpBp3B9iF+M4CR0qQRBGOSmiMY2TJiJtnEQ5aeOkC0uVeeVlFTnSeZSOC5lHSRCOhvB8wM5XvQJ1yy18wytvsH3LNs4BwymnpQMiCMLJ5TQIxLycFuGYxmkSknGcBlGZBaPmnyPjZpxw9z//D6s1j5IgCKefdXZ4cvDV+MNr6MFw+gYzclYu3sLZwQvkEnwUyHfHYjgLYjEvZ0VEJnEQSTlJyLe0IAgLI76WsP2CZ9DDmKs3N7hzI5GLqyCM4az+b7hhvetxVt+H04wIxOE57QKyKA76WVuJMUqCIJw9HAdu6zzLlZ1zXNx8cmJbuRAIk3A897hPQVgC0okW5kGuE0fPIv5Hq1VntXGzarL2Xhs7h5mtNluu18bNtnPLqrdk6/NKt0Ul22zuNco52Kgcr2zrludQ2V/kPjPT6xBREgRh4WzwLJ9/5k62ow0AO7Fj5css/3I02ZdcUaq7KNedz9eTlequPIcx8wJNKt3dMlfLMphWhhkmlYmep1z1LOWc28uIt+2nreRzvcx14911Rtu1lhAfs//aeYyUSW/sr7G8bb809tv6eqol11v2WT/26P78ShTEwbSe97j9j5Z6l0qIgrDqLFIWqtey6hQU+beFMeX1r7wWVq+DTjHnWH4Npdo+EwC73rUVJKlKReVaWr0OU70mQ1U06ufVbFtfVt1fLj7jKKvQ6rKarWMqFWsNLnb+M8epVLx1dFalttzGJAmukxA4pmybV9HFHiPftrmfQdqf6W8ooiQIwkJJBymbG9e4PbzKV65v2/LfjrZfWDS+ENG4ToKbl+ZudEDzL02Vpea0dThz2uYjotF+mbSJmhd6o8unTAja3Nd8k6g6o8fT9fWj+69u23bMlvM1o8cpL/wtr2Fkv6OvzzS2G7fcPnFGXkP9eE6xvPY8b9NcbpxGu+a5tZ3vwU3H9etFhfLPun2cLzQjn+/RubdG5YxJclZZNlYWWyRvVCCr+54sw7Wy+mMEkpb/z+YnrHVOp6b4j5vPqvmfM+ZPN+49Gce4T8C4H0OOgtb/10ntW75Dxuy4df0sExTnnfSxx6n8r7f+z1X+n5v/y9W57qhtXwrExH1XvkvafpgztfOrS031nHIBGXecSVSnpcj/76tTWxRTVWCy9RQy4dSup/beJKr438y3taJQ3V8pIb4DLhryqTRcXduGyrGKZflz6gLjZPvKJ412Ku2q03GsAg6SeicIwjGhE8WLNz9H0LOTuej0kKkTnQWc1Kowb/96jvauL6lqR82IpDU6hdXOWrMLbRodsOayfBsvi2aVv/qO7rtd8iaLX9u50iKobZHcEfnU1J5XzzVfpsd0IMd11if9MNAm6u1tJx9r3PZt5zOyfMyPHk7l8TwYnNonpPm8SdtxxsrbhA7qaKR52rbjIrHt+511vV1WXiuaUeR8QuniR7XKCY8cqzYfXnU/VH5MKNsUz2vR8/oPGFUhscco4kGl1DR+0CiEpXIutccH/71FyEiHBxvjqJLZKlqKKAmCsFDSXUXSUQf+8hIOjirn+i0618LRUurJIXaQUYjvpB73+LCGcIrxI/n/PirGXsvMmMdTKOOwwklA/tMEQVgK6fDszT9y3FQ7T0oqiZ0YxkntoSOxwlJYhcit/BAlCNOZ9B066/eriJIgCEtBJwq5lM+PHx18UmzpPB0/B/mlX6R2eSwjsioCKwhnBxElQRAWiupr0oHt+EWB7fTrGXOBVwk3OLiwHAaJxB0dh5HScYisLo5FpJeJhB4tkvIrnDbkEy0IwsJJdxV+xz+RgpRzks/9JHOUgipSuhiWIZxwtqXzpI5BEjEVTgoqme2zejL/EwVBWHnyqJKwOgSd44mSzYMI6nSOK9o5jtMqnMsSwFk4y5K4LE6qfArHi3xqBEFYGidRlvzO6f1aTAans0O7SEQmV5+jEsXTKoBwvBJ4XIh8ClXSVCJKgiAcA/G1hPB8AIC/fvIuxidR7o6D0yqUIpMlqyqNJ0UUVy3yV+U0S+BhOIsCKUzmdF7pBEFYCdLd1b8Yn0SZWwXOslCeVklscpKlcRUk76QIXRurLHnLRATy7JCmMuGsIAjCVE6CzB0HIpDjEUlcfVZR8lZB3mblJEveNM6qBAoH42R84wmCcOJQfY3XPf6JGYWDcdoFUkTwYKyyJK66xK2ivOWcJIk7LKdZAudFpHE6q/2tIgjCiUb1z97EjCKHJ4PTLIJnVQKPW+JWXdQmscoS1+QsSd2yOcvSqCX1ThCE46Ja0OGscZrlUCTwZHDSJfCkip6I2tFwkqSuiUjeyeNs/FcJgiAIh+Y0SaBI3+pylKJ3UqWsjeMWtZyzImwH4SRL3jROqwTKp1kQhKVwlqNKwupzWqRPhO9wiJQtnlURtiYicMvlpElgqma7BsinRhCEpRFfS477FKYiMiecZE6q8J1FwTvqlMizImazIgInHAT56wiCcKY5CTI3CyJ8wkli2YJ3FkWsyXGMVRM5m59VFbgmZ1XozuarFgRBOGWcNOETsROWyTJFTCRsPMdVSEQEbfmcFKFr4zCSJ6IkCIIgHDknSexE6oQqImGrhwiaMIk2yUuVlAcXBEEQhEMjUiccFZKSeLJYhVL8ImvLRURJEARBEE4Jqy51InLHi0TDTh+rIGtweoVNREkQBEEQhCNh0SIn4rU6SDTsbLMqwpazKHETURIEQRAE4USySPES6VptjqIUvsjY6WGauKVaxigJgiAIgiDMxKKkS4Tr5CIyJjQRURIEQRAEQVgQEuUSJnGUk0SLlB0eESVBEARBEIQVZBHSJbJ1dpEI2eERURJODOH5YOUrOgmCIAjCKiERLmGZnPYI2VKP+LGPfYzv/M7v5I477sBxHP79v//3tfXGGN797nfzvOc9j263y3333cfnPve5Wptr167xfd/3fWxubrK9vc3b3vY2dnd3l3nawgoTng9mvgmCIAiCsDjia8lCboJwEFRfL/Q2C0uNKO3t7fGa17yGv/7X/zpvfvObR9a/973v5ad/+qf5uZ/7OV74whfyv/6v/yv3338/n/70p+l0OgB83/d9H0899RQf/vCHSZKE7//+7+dv/s2/yQc+8IFlnrqwYhxEfA6yjXyBC4IgCMJykZRC4aTgGGPMkRzIcfjFX/xFvuu7vguw0aQ77riDH/qhH+Jv/+2/DcCNGze4cOEC73//+/me7/ke/tt/+2/cfffdfPKTn+Trv/7rAfiVX/kV/tJf+kt8+ctf5o477pjp2Ds7O2xtbfEL3ovoOadzQqzTzqp+IYpYCYIgCMLJZVX7F8Jy2dOKv/LMZ7hx4wabm5tj2x3bGKUvfvGLXL58mfvuu69YtrW1xete9zoee+wxvud7vofHHnuM7e3tQpIA7rvvPlzX5Xd/93f5K3/lr7TuezgcMhwOi+c7OzvLeyHC0lnlL7F5z03EShAEQRBWh8Ncl1e5fyIshmMTpcuXLwNw4cKF2vILFy4U6y5fvsztt99eW+/7PufPny/atPHoo4/ynve8Z8FnLBwn8w7gO8rBhfMg6YCCIAiCcDoQyTr9nMqqd+985zt55JFHiuc7Ozvceeedx3hGwlFzWsQKRK4EQRAE4bRxkOu0yNXRc2yidPHiRQCuXLnC8573vGL5lStXeO1rX1u0uXr1am27NE25du1asX0bURQRRdHiT1o4Vvz1+viydFctbN/ziNUqS1WOpAQKgiAIwulinmu1SNViODZReuELX8jFixf5yEc+UojRzs4Ov/u7v8tDDz0EwKVLl7h+/TqPP/4499xzDwC//uu/jtaa173udcd16sIRkv+jNyVp3LJxHJdUwekUKxC5EgRBEIRVZdZrtAjVZJYqSru7u3z+858vnn/xi1/kD//wDzl//jx33XUXP/ADP8A//If/kJe85CVFefA77rijqIz38pe/nL/4F/8if+Nv/A1+9md/liRJePvb3873fM/3zFzxTjj55GLid+of13SQzryP45IqOJ1iBRK1EgRBEISTzrhrswiUZami9Hu/93u8/vWvL57n44YefPBB3v/+9/N3/+7fZW9vj7/5N/8m169f55u+6Zv4lV/5lWIOJYB/82/+DW9/+9v59m//dlzX5YEHHuCnf/qnl3naworhr3sjkgSj4jSNWcVqHqmC4xcrOBlyJVErQRAEQTgZNK+/Z1WcjmwepeNE5lE6uYTnA6LbAvyOT9CZ/rdLBouRlnmiVXPve8FiNS8nQaoOg8iVIAiCICyO0yhJKz+PkiDMQ9DxcIN2UdKJqrWblUlSNU+0al6pkojVcpHIlSAIgiAshtMoSfMgoiSsNF7Xxe/4uIGHHzWq3g2tQIwTqDaOW6rg5IkVnN5xVjkiV4IgCMJZ5azL0CRElIQTgR95+FH5cU2H6Yg4jSMXKphdqqpCBYuTKlhutAqOt3BFzmkXK5BiFoIgCMLqIvKzGESUhJXGX/cIOlaSvLD8uHqhj4oXP47osFEqmE+qQKJVIGI1DpErQRCE040IzWojoiScCIYqIHBCAjfFdWz9EdcP0eniOszzRKls+8NLFZzeaBWIWB0WkStBEISjQYRFaENESVh5tBfy+M69eH3bSQ/clMiLCb2E0E0IvQRf7RN5CZEXE7l2XVWogKlSNU+U6iikClYnBRDOlliByJUgCMJhEfkQTjoiSsJK43d8rgV3cUu4x+te+ASx8olVwH4fYh0Qq4ChCtlz1ng2DRnGIcOhg8EphCqXqshL8NJ+JlilaPlu2SFedJQKDi5VsDopgHC2xArOTtQKDteZEckShNkRcRCEk4WIkrCy5BeUq4NbefFtN/B8h66v6KLY6gEMADCq3kE1BmLts993GKZWpIYqYKg67HsbDFVAHAcMYwdtXFxHE3kxnUaUykv7WZRqSOTFSxcqOD6pgsVGq2D5aYAgYrUKSMdPEARBOK2IKAkrjQ679N0tnn/LVYL1brl8TIqcihMcoIOmEwIoxgkVQKI8hiqwUpUJVawC9lWX2DnPMA7ZH3o1oSrT/lKb9pfuFxGr0E0I3ATHKY9x1qUKViNaBSdbrOD0yJUgCIIgnARElIQjZ99ofsvc5IpJeIt368S2182tnF/r0+m6+N0IAJMqvKD+0VWJ7Ti7YX35JKECCD1DSMxGF2Bo9z9FqGy6nxWqgV5n6JxjmAQMdl1S7eNgishU5NnoVJiNn7JRKitb1cIUObNKlYrTWrn0aRynVMHxRqvgdIgVnN2olSAIgiAcByJKwpHzDAk/ra/gAf83s82WM/5jGOuQ9V5CsNbB73XRSQpBPdVHJwm+X+/QmtR2WI9aqLRxGKqAfp9KhMq3aX/pBgMVMhwGDGPb4fXdtIhEtaX9hVnqX+DWO+DzRKlWVargaKJVcDRpgHDyxQpErgRBEAQhR0RJOHLuciJeRMQXGPIxc5PvdM6NbdtXXba8mJ1kHTc9T+QN6AQpnltGYtxk9GOsk9HO7lEIlQf0/JReBGDbjhOqWPn0B9QiVInyy7S/JKB/00MZD9fRVprcMjqVP3dV3wpWtu6gUSr7Oo5OquBsR6tg9cQKDiZXOSJZgiAIwmlCREk4Fr7N3eQL+ml+Xe/wnW67KGnj8Jy5jVdvP8vvXXkN3bhDnPqoRBH6ishP6QQpHXdAJ0joBglR/rxTlymTJLgNGRoXnQKORKi6vsJmE9pxVG1CBZBqN6v05xRCFauAgVpnJ/HL6n9ZtT/fTQtpimol1PuElfLpzSgVrJZU2W2OLwUQRKzm5TCStQxE3ARBEITDIKIkHAv/o7PBv+BpPseQL5uYFzjhSBuFh9fpoKJNNnzDt37dUxgDwyEMEp9B4jNMPPpxl739Ltf2ffpJwDBplyn7OLHPg4QoGhJ49Y7U2OjUMQkVQOBBECSsdQBie5wWqTLGTsw7GJiKTIVFlGpobmE4DOkP3aI4RRGdckupirwEJx2UqYBugueOHm/ZUgUcawognD2xgqOXq2WyauK2aohICmcd+Y44u3jaTG+EiJJwTGw7Pl/nrPF7Zo/f0DutRR0M9gvs+qDH9vMS6HZtRbsedACSBJveNoS07ExWZaof+wwTn37cpT+A6wOfwU27XCUa39U2ChWkdMOEkCHdTKaiIKHjDgl79Y7j2OgUHLlQQSlVDtUo1RAYjo1SJcqzUaos9a8UqzV2ksBGqeIySuU5KitCUab+WcFKcVV/YpTKMvtXzVFLFSwmWgWLTwOEoxUrELk6S0gnURAEYTIiSsKx8Xpnw4qS2eH7zC241ZrawI7eZi3Y47p6Pq99HtDbgLQyuWXYAd3ooKVJTaa2C5miJlMASerSHzg2OhXbCNV+32c39hns+fQTn2RocB1TyFQnSIkYFM8j3wpW1BniuvVfJ45CqKAuVbMIFZTFKWzF9XisUBkDifbtBL8qqJVQv6l6xIldVotSuQmhV03/sxLlpoNiot9oTJTKcnqkCo42WgVHL1ZwMLk6KCJlgiAIwlEhoiQcG/c663RxuUrKp+nzSnq19WnUpddziN111rYVu2adTlfhuuCostPvVAUo7MwkUwABEGzAZpJgxwkNs/Xl/pR26Pcd+kWqn0+/3+Fm6vP0Xhmx0qki8hVRFplqylQnSIjcIUFvdHLOVRYqB4jQRCFMi1Kl2mWYhlmBCt+KlQ6IVZe9dJOhCkgSn0EmVZ6jKtGpPO0vzqr+lal/zXmp6qyWVNntTk60CuYXK1iMXB2Uo5SyWRBxEwRBOL2IKAnHRuS4fJOzzofNDr+ud3il1xtpk2oX3/N47HMvYLB+G45SdEJFt6PoRoqeP6QbKTqRohfEdCOFF9nIzoFlqrLOA9bDhHU0dnxQnLWpp/oNBlmqX+IzjH0GSY+9Plzb94vxVCrReK6NTuXpfaEzoOOnZSGKICV0hngNQTKZIM0iVLlMQV2ocpmCxQsV5GOppkepwIpUHqXKq/4NVcie6vJc7Nvng5AkBgdDkE3uW072m5dTT/HUfjHOyh8bpYKjkCo4eSmAMJ9YwcHkahzHKV2LYNXE7SCI7AmCILQjoiQcK693Nvmw2eG3zC7/d6OJnDJnPjYRWkVEPpgw4i98yw3ixKU/cBkMoD/w2R9EXNtzGTyNTf/SDpGb0IkykQoykQoVvY4VkjDQEHVrIgUVmQo79j4Xpvz5mMiUA3R70E0SzpEyNtUv1tm4qSArQhHQH/S4lvgM+nZ5ojxQaVmEIhOoiGHxuJOt80hahaopU9BekMIuX0yECuaTqsibLUqltEus/WxeqqBSSj1iR6+TJP5I6l9VpmpzVBXpf1a4mmXU65wuqYLFihXML1fjWKR0TeKkC9kyOQ2yt0qIeArC6UFESThWXul0uQ2fp0n5hNnjm52NYl3f9NjqdfE6Idu3eaS9Hi6wsZGSt3IrsuOqhDhx2B94DAauvQ3XuDrwGF6H/YFHmjr4pIVINaNS3SCmE9r0vokyBaORKX9URKrrgy4EScIGCpvqF4/IlNYU46b6sU+cZuOmkg7P7JeV/qqFKLphFp1iaOXKT+iECR0/JXBiHIelRKhgfJRKNeaxOmiUyvfAp5yXyqi9sdsmyqM/gEEa2jmpdJ7+lxWoGNp5qgZDtyij3jbZb+TFeNncVJEXTyhQUZzllPXV13c0UmW3XZ5YwcHkahyLkq5JHJWQtSGSdrYQ8RRWARH2xSCiJBwrruPwrc4mHzTX+HWzwzdTipI2Lqn2SN0ua+dDBsE6nk5tPhzg6QS8yO7HKGsiHVhfS1nP96/y8UfZPocpg9hlMMyjUiHXBx2uXLMRqUFsd9714iK9r+tVRCqTqcDPohHzytSEMVMALrAWJqxhgCS7MSJU8dDUqvo1C1EMEr8WnSqLUSSlUAVJLToF7UI1Vqbg2IQKRqUq9AxhCFsMMao/djtjINY++32ndbLf54Y2DTBWAUlicB1N0JSqypgqV/WL55NT/2CZUgWLi1bB/GKVM02w2likdFU5CgGbheOUtFkQkROE04cI+2T8GS8Pq/3tLZwJvs3d4IPqGr9v9rhhUrac7GPZDdFuROJ1uB6vceOJNXo9Q7erWeuk+IFNifNMeZGfKFIAAXR0SqdyfLc2limxEpWL1NCnPwi50fcYXDP0hzYqFThJIVKdSNPzBjWZ6viVAgQNmXIa0jOvTAGEfkIIbBbRqeHE6FRZiMJjL9msjJ0KUIki8PIy6UlRgCJyhnT8pFaUAq1GZAqWL1SwuCiVA3TQdEIAhVH7Y7dNtUs/H0ulc6nyiVXEdbVBHAfEerRARTX1r5oK6Ol+VkY9nZL6B/NKFXBk0Sq7fftV5qCC1cZBpKvKsgSsjVWRsoOw6iI3DRE9QRCWxcn+dhROBXc6ES8h4nMM+Zi5yXc652rrk9Tl80+sse70UFcchgNjZSUwrPUUva6m0zWsdxO6XStS3VCBN4NIAXhRRaS6BFFKAI30vrR4nCSOjT4NPQYDh/4g5NqwS/ysYX/gMUzsOKueH4+k+BXP/RjfLzvKjl//VxyRKbfR4ZtBplqjU839YqNT/axEejU6dTNenxidsjI1KAtRZNEp39MYNSpUU9P9YKxQwfjCFLCctD/IC1QAJBg1HLst2AIV/b5hqEIS7WdRKZ++2uB6nM9V5TOM7efDd9NyHFVl4t9ctDzTnyn1T6caL1yuVEFdrOz2B5eQcZLVZJHSlXNY+RrHUUpZzkmWs0Vy0kVvHkQKBeFoOTvfLsJK83p3k8/pp/l1vcN3ulaUtHEZmA4qjYjWPV76Gh/PVYBDEkMyMPT7Hv2+z25f8/S1iEHfIYkNrgfdjmGtl9LtGLo9XYhUp2PwnYOLFB3oraVFMfN6el8CScIwdukPPIZDQ3/g0x+GPLe3zvAZG5VSysH3TVlsYs6oFMwQmYLRcVPN9ZTRqa0J0SmVqCIyVUan3EZ0yrdRFR3X553K5p7qNCJWjsNChMqum17pD5aX9gf1AhWTUv+0cYiVz2BgihS/PP1vV/WIh0ExZ1We+lcVqrwCYDGmyo9xksGUCX+rzPe1f1CxymkKlt3X4qRiVunKWYZ8VVmWiLVxHHI2CRG35XOWpHBeRCKFZSD/ccJK8C3OBv8fnuZzDPmSibnTCUmNh0OA5wb0tn1i7YO2H1nf0wRrEKzBtqMBL5Mog1KQDrSN9uwHDPuanacdvtjvEA/tGJVOx7DeTel0Dd2OYb2XS5QmyhNXx4kUgDdGpMBGpTopwaZ9akVK0xaV6meFJ4axz3PDDpefZWxUqhMp1vwhUVhGqQK3fmGYGpmaRaZgpI3nJ6x1mSk61Rw71R+47CU9Bn37PE59TJrW553KolFhQ6iCLDoF4wtSQF2qli1UcLgolQd0fUU3YmLaH9RT/4qqfzogVh36ep1hHBJnRSvSXKoq0akiWlUpVOE6/RlT/+aPVpWvu5rWd/jLTJtslftfrCzMK15Nli1iOUcpZLOyauKWIwJ3NhCJnIyI5MGQT5WwEmw7Pvc4a3zS7PEbeoe3ereyr9fYcH38ToC/FrKfhHiOHSw/BLzKwHnf1aVEORq3A70OrJ+zEgXguQpjQMWK/r7DYODT33d47qbhqasR+30HnRqCwNDtGdY6im5P10QqDA0epUhVJQrGixRQi0q5YUpvjdaolKv20ZoiKjUYGAZDG5W6sb/OoBKV8jxTzB9VytSAKNTFc5d6J2GqTMFoqt8MMkWSEPrUx06lo1XqVKIYpnkUysvmnQrZG4RcSzYZ3LSipY2Lq5Ny7FSW9tccO9XxU1zXHIlQwXipOoxQweFT//Kqf2UZdZv6N1C26t9wEI6k/tkiFXFRrCKPVgXZOs/sZ8tmv8AeVK6qqLiZ5reYS9Uk4SqPtZzO/mEFrMlRCVmVVZSzWVhVgRuHiJ2wDEQkG+zN9n8m75qwMrze2bSiZHb4Pn0LqQnwPQ8nCNjZDbj+JY9uz2NzQ+P5gPLwXfureKyYLFEA2sd3NPgBnU3obZVt8mhUktiUvkHfod8P2OtrnnnOob/fJYkNjltJ6cvS+DZ6iR0X1TW4fiW6NKtIwUh6n6tTgg4Em7DJ5KiUHSsFg2HIjaHL1RtrIxX8OpGi18km5vWGtbFTQdeMjHdamkxho1M9oIfJXk86Njo1TL0y5S/26fdDric9BoMsOpV4GBwChkR+WSq9mu5XRKuySOGhhAoOVJjCHm+2KNVBhArKqn92st/B2O2rqX/5hL9lKfUee6lfPM8r/zmYmkA1J/0NKutct4/vqDJtdEZ0Wvl/XLBo5SxKuGA26aofe7md9UWLWBvHIWc5J1XSDsJJE7scETzhJDGrOIooCSvD65w1erg8TcqnsGM8bKKXzxf/e4S75YKBJHHpdIyd5LUH6xuG7hp0ex5RkHW2DipRLgQ9iNY0W0A1pU9rm9LXH0B/3ycZGJ55zuFLT0b09x0whqhj6HYNa900KyyRF5nQBAEk7gJECkaiUmVEKo9KJRjDSAW/wTBkp79eq+DXFpWqylQnVLje6AXwQDKlVfu4qWYlvUp0agONnXNqNE1Np4o49ejH5dipQRKy3w95LtlkuGtFK1UejkpGx075KR1nkMmUlSwv+9wsS6jgaNL+YJbUvz4wfjwV2PS/XKqGFalKlE9fd7iR+CTDUq7STKx8V2UFK1J7n0es3ITAK5cHboqTDAi8dG65qrJI0coZJ1ywWOnKmVe+qixbxOBoZGwcxylpOWdJ1g6CCJ5wGhFRElaGyHH5JmedXzM7/Lq5yTfiguuTJA5e1+GFr3AJQ0OaQH/PIRka+vtw8ysO/X0blOh0HLpr0FuDjUygOl0I/bIH5hu3eKx02blK2wQKSokCnAjWO5r1bbvKywbPG2PQiU3p6/cdBv2QG7uGy1cd+oMuKjH4vrHV+XqqiEB1uzYiFYYGxwHlVP4lJ4kU4DlTREqnBBFFBb9JUalhXM4rdWPocuXmJoO+sVEpA1Goa4Un7PxSA3pZifTAN5hFyRTMLFOu79OBrNz7+OhUmuiKTHkMkoDBwOdm0uHpZLuY3FcbB9/EhTTlQtXJIlRRlgIY+mXE5LiEyh5zfNofzB6lgvGRKpv+52An/I2n7ycXK+2TKJ9Ue8TailWsQ/ZUjzi2YpVqj0QFKOOiU5VFpaxIBY4i8BJ8J1vuWamyclW2JbHn5PrulDObTFW0chYlXDBZunKWIV9wOAGrchQylnOcUjaOVZC1KiJui+GkCh6I5B0FIkrCSvF6d5NfUzt83Nzkta6LGzgMdYjXcUk0mMT2TsM16G44ZPUS8D1DEsNg3wrU/h5cv2YFCgPdNaeIQOUCFXXAcbxSisZEoWC8RFWlCy+gu6npZiflFRXIbEqfGhr6WUrf/r7m2g2H/r5DPOziuNDrGjpdzXpX2SIT3bJSn5sdZpJIVSUKGiIFuG793931ApyQMVGppIhK2bFS1aiUy81dj0F/o4hKuW49KjUyWW+ocF3mkKmGFM0iUzBWqHwfNrp5dEpDOhpFyaNTeTGK4nHfZycrk97PolOotBgz1QmShkyldIOETuiPRKfgeIUKlitVAL4H64GDnaNqfLva/hpylVTuU+0x0BE3U59U+yTaywTMR6UaB1MKVBapyp/7rsomAq5IWPbYc3VNIA4rWtAuWzlHLV1VliVgsDgJa3KUUgarKWbTWCVxE2k7Hk6a5J1EsRNRElaKV9DlNnyeJuVz/c/yVbySgQrwfJe0Eizwfcj7g74HSjvgQHcjE6hbrTwZA8MBpEPo78POTXj2aYfBPuDA2rqht+bRW4NuFoXK+9++Oz0KNTaVr7GNTenTBD075sirrCpS+voO/b5LMnB4+loemephdJnSt95L6XVNUWRirWslABppfbAQkQLwI9jYgK0iImWjUvlEvWmaRaUGZGOmQm4MXK7c9BgObIrf2KhUpOj5gyIqBYeQqTSB8OCpftXo1DYppO3jfKql0vNI1H7f52YS8XSyXaQAauO0lkrvBCkd+oSBsnNQRd5IdAqWI1RQSlWbUNnjTo5SweKkqjinTK6sWM0mV8bAcKhtxCpLCSwfe6Q6YKA73Ex9EhVkkS0rWNkLxHcUnqtqaYC5VPmVx0HjcV4xsCkJi5AtmCxcsFjpyplXvnKWKWGwPBFrctRiBidTzsYh0ibMwiqJnZ9Or/wKIkrCiuE6Dq93NvkFc40/3vuvfJv+blTqEIQOw9h+qD2vKknOZHkC/BD80KGzAbd5WWfcwKAPyQD2d+HqVfs4jh3CyApUt2cFqrcO6+um6MzWBMo1KDU9lc+2HSNRgNfRrHdg/Vw1EgVgiip9/b7DcN/n2o5D/7J9rpWtxNfJCkuUIpVPvFt+KdWiUYDXEKODihShjdStFdGDOaJST5taVKoblTJVj0qFRVQKRmXKUQkcgUwBeL5fL5XeEp0yShGnblGEYpj4DFOP/X7Is/0eg+R8USrdwRA6w9pEvrlYhZQT+lYvMEcpVDBdqmB6pAoWJ1YADtD1XYzKxrAxPS0QSsGqRqzy9MD8caK79LVHklZSBLWP0tl7opKKQNlUwBHRclqWuWqkLHubCCxKuGC6dOWsknxVWbaIwdHJWBORs+Ug0iYsEhElYeV4vbvJL6hr/Pf+59kZ7qJ9Hz9wSDP7T1PwszFHShm87DtxHnkC7PidyKG3ZaNPYPvH8cAw2Ldpe9eehbgPOA5r61kEat2jt27HPkVh2enxXU1K+QVdlSHP0ShdPh9J5au0bUqU63t0N6G7OSpRJlFZJMphMHDZ3Xd4+pprU/piCAOdSZMtMFGVqF7jIr1okYLDR6Wu7nr0901RwS8KNGvhsDIxr86KT9SjUnBEMgWtQuX4PlEEEdlEvml7ae9mqfRBHNDvu+wmPZ7t1yfybSuV3gmyYhRBSse346rcGYTKNMRo4uS+jdfYJlUwPVJlz2Gx0aqcWeUKrGB1HOjkRULmIBmmWbQqTw30SGqCZaNYu2kpV/m9zv+vK5LluYogE6q2SFYuW6GXZtGvdumZ1NE/DunKWVX5anJaomKzIHJ2tKyCtImsHY4TI0r/7J/9M37iJ36Cy5cv85rXvIb//X//3/nGb/zG4z4tYQnc6YS8mC6fp8/jTzzGq7/6PlzPIR4TJq2Kk6VslyorTmU7GuvsY6Udmw7nQthzCHtwPpMnrUHFdtzT/q7D5aesPCkFUccK1PqmYX3DClQYZNKV9SlGBKoShWoKlOdqUirSVJEo39HoSvTKdTR4HtE6ROu5ROXrDahMovZtSt9+3+HZ6w79fZfhEHzfTrbb7Wl6PRuN6nYNvZ4mikA7hxQpnaC9ch+uUeg5o1JuNiFrWwW//iDkxk2P4VXD/qCcV6obqWK8VLUs+ppn55cqKqtphfHqknNomYK5o1O1Uukt6X5GKZLUZZh69OMgK0ZhS6U/N+jRr0Sn7Fs5HJnIN/JToqK6X0rYSH+oChWcHKkqzin0lyZWOUZpgsjPPgllhclZaZOsNI9omdFIVpqtjyuRLNfRuCqupQv6rrIFL7wUzykjV/njakRrUlXBWTrzxyldOcuQL1iOgOWc5qjYLIicHS/HLWsnXdROhCj9n//n/8kjjzzCz/7sz/K6172On/qpn+L+++/ns5/9LLfffvtxn56wBP5HZ5vPmz6P/+lv8mreBQ6karZ80lnFqWxbXVduVcgT4AQOa9uwdUu5r3ho51za24Vnn3H4yhOgUuj2nCJdr7sGvXUP369ErWYQKKhL1CSBgjES5XiEPaz0NSTK0YpB32EwcOjvewz2NV+5HGUT8TpWorpWoPKxUZ2OodczRJHBd9KaSLlGobwlihQ2AhipJCvbPhqVShKHQewy6FeiUkOXyzc9Bvs2/Q+HUqRCVZZAjxQ9f0g39PErUalDyxTUhWqSTEFdqDLJcHyfMMpLpc8enapN5Dsoo1NKu7iOKYQqL0YR+apI98uFKvBULUIFB5eqEaGqvN5xQgWzSZU9D38mqYL5xQoOJldAlhrIoSQrHSa16FQZwSqFKtERsfG4kfgo49VELI9mea7CVXFRun2WaFbg2nFcs86NNWuHfZHSBQcXr5yTKGBVzrqMNZFiIKvDcYnaogTNMcbM1vs8Rl73utfxDd/wDfzTf/pPAdBac+edd/KOd7yDv/f3/t7U7Xd2dtja2uIXvBfRc44/DCpM54r2+Rv6M2g0D/31/8yLXv96dvM5grz61dpvPA/8xvrGc6/xEWhu3+gD05hHtFaIwa4v/4V0atjfhb2bsL9nI09xbCvLrW3A5pZhY8tW3GtuC42y5FBU4SuO7dTXt6XijOyjsY07so/6l4lryjFR/X2HYV9nE/BaifK8UqJ6vSylr2fodTVhlO2TdGSftWOa0Qtum0hN2geA29jGbamyli/TOo9KefT7Vpz2B55dtm/lSmsrib1OJk4dVZlfStPLolI19Oh5OS3n0VoavWVC3nH7HNu2TTLajoWVnFQ52XxTVqji1KffN0URimFqx1OlysN1jJ3MN49K+aqcfypICRjS8RMiX+E2PqtNoaqtmyBGrUJVWz9dWKZJVc6sYgXzpQIW+z+gXEEpWIdFxQlKu5k45VLVFs2qj9mqtgNwMFa0dEzgZJGsTLisVKmxomWXz/d6jrNTfljpOgqOSsCOkpMkYseFCNni2E1TvvW3PsGNGzfY3Nwc227lI0pxHPP444/zzne+s1jmui733Xcfjz32WOs2w+GQ4bD81XVnZ2fp5yksli3H54Wdl/CFwWf5vz71i7z49a8v1uksspQLUx5pyoUnyVL0cmHKxzaV45rK49jCEJVxRpXxTXabcoyTXQ+j/ZeKaDkO69ummGcJrDzt3YTdHXjyyw7DPwHPt+LUW3fY3DKsb+bn4mXHGY0+AUUEKheoagpfTjUKZbdxa/uopvIBRSQqFyiFh9+F7TWFfRmVSJVJGfQd9vdtCt9gX/Pc9ahI5wuCeiSq2zVsrKX0eroIlniMRqOAWkTKMymqEl3ydIpqRGxmikjpFF2JCHWChM462euyUamqXMWJUxadGHgMhj5P73sMnvMY9A1J4hZFJzqRYi0YFmOlup3U3ruD0V/Q5opMQS061RaZyvYJ1KNTE8ZNgY1OBdne19GQto/RMUqhtGMLUaSevcU+w9TOP7Wzny/3iRMPg0PgKSJnQCdL++uGSVYm3Vb56wRl6peT/d3aZCqPUI2TqTxKNVGogmAmofJ9b2apmididRhyuXKav8jMSFOwvDDAw0Yk541mqdgWY0mNh9JllKomWnkxDBOxrz3StCx80RStXJxcHWfyVEa1PFcTugmeo2tFMPLUwnHjs6ZxkM73qkW72jgp47/m4SyNFTsoRxUpEyErWXlReuaZZ1BKceHChdryCxcu8JnPfKZ1m0cffZT3vOc9R3F6whK5e+21fGHwWf740/+e79I/Ve2vt5IqU4sOjRMmGJWmsiDEeGlqY6o4YfvCG+fsDWz/Nh5YedrfhStPOhgF61uwsQWb21agqtGmVHn157oeOWoKlH1t7kIECuoS5XVgu9eUKDsmKo889fu5RDns74XEMbYkeCZQvZ6mt2ZY76Z0u37x/ueRqElpfXk0ahki5Yaw0UnYyF8TCleV44aUgv6wLHveH4TcHLg8vVcvOtEJVb2CX6X4xJrfx/PAuKMpeVWZKqJSy5IpaE31AytUPrDehXXM2FLpRilbRS7xyrS/2Ge/73IzXufqnk8/CRgmtrpf5A7ohgm9bPxUN7Spf90woev0i3mnYLJMwYKEKnsPZpEqgHm6KCpJaxX/ZkHHKV7YMj/YLMdbsGABxbnYV5GNo2O2zmZ+PrloJcovoldV2bL3EQPtcTPNhaxMKVSm/CEnj1p5Km6kDCr8RpQryJf5/tTxWdM4bAd7FcZ2zcJplK8qJ3E+seNi2UJ2kkRs5UXpILzzne/kkUceKZ7v7Oxw5513HuMZCQfhRd2X0QnWuHHzK3zhsx/j4su/GRhNvctpptBNIk1NLSWvKUwwGqmy22XHqoxrKo+f7atyHfNcSFUzVdDQWXPorMEt2fNBH3auw83r8OSXrGqtbzqcuwXO3QpBWD+WPbY3krpnoySVNngj6XuNJijt1n6tHREoAOPWUviaUSgAHI/OuqKzDtYJc/FKSRLo75eRqJ2bLpcvO/T7EUpBp2OjUGvrmrUspW9jLSGKyiIS4yQKRqNRUIpUntJXFak8ra8pUkBNppoiBdALE3ob+Wusi1RRdGKAjUwNffrDkOd2vNb0vjwq1a0Uneh1FCFxXaTsm26PcRQyVW0PE4XKAbod6MLYYhRawyDx2Y8DBrFPPw7Yzyby7ccB/dhWhQs8RSeTqU6Q0M2iU53AylVghiMd3sMKFVipmipU2fswq1TNE62Cg4kVlCmBBxGsamrgQQULJksW2I5GpxhXOP39q56XNk5tTFbSiGTl47OGeo0k8YrxWYmyY7Wq47OKEu1qWKYEVgpg1FMFs4mJHbWwDvYiOtMnRbpyTrt85SxLwk6bgOWcJBFbeVG69dZb8TyPK1eu1JZfuXKFixcvtm4TRRFRFB3F6QlLJHQDXnPXn+N3v/Bhfv+3/jXf8cpvGWkzTo6a45Rq20xY10YzUgWjwmTb5edULmtKk21XmYfJM6TKwQ/h/O325rmGwT7cvAFXr8AXPw+bm7B13opTd81u6zpmRMLaw27T5akZfbLnPipQI+MMZhAohQcu9DYVvSINOJvjykkZDinGRA32XJ5+2mFvz2EwiPA8WFtTdgzUmh0H1e0ZNnopvl+vxNcUqVkkChYvUpGfEK1RKzrhqnKupTy9bzggGyMV8fS+R/+ax6BvS6Xn1fs6kbYi1akUnYgUHdcKw9JkCg4nVA2Z8sDOPZWOzneUC06culagYp9BJlI34g6X94JiYl/XMbain9vPBCrN0vusSHWDtHV8GFihmjRuahahgjmkipHfJCYyb7chTwM8SNSqOOYh5SpnkVEsqJ+XR/4JnW2+rOb55eOz8nTAfNLhqmwNTcReJW2wOW9WNZqVi5Sr4lo0K3AVgZdMHJu1iM70IjvOJ026chYtX6soXjnLELDTKl9VZhGx0R+ax7Q77MksmzAMueeee/jIRz7Cd33XdwG2mMNHPvIR3v72tx/vyQlLw8kq1X39Xd/M737hw/zXT/xb3vQ3fpog7I60nSRFMF2MmsUdRrafI1IF7cIEpTRV+xS56FT/YZV2CDpwvgO332FIYrhxDa5fgy//KWyfh+f/D7C2Xj+vNnHyPUOqp8uT3UFLuynRJ5hNoMBKVLOQhMLDCxXrIdm4rlygbBQij0Lt77vs72meecZnf98hTaDTMaytaXtbN/R6Pmtrhk6QCdIMEgWlSFULTDSjUfZcg2xZ5ddub3R/uUhVC01UZcr3EjY6jE3vy+eUqlbve27g8dSOW1TvcxzoZhGo9WycVDUi1QnLsVdtKX4wr0zBTEI1a6pfQ6QAO+9UlLJNClSidJnc5GOm+nl6XyZT13c36Mc+gyRAG4fQT+m4A3p5VCq0MpWLVegrzATRmSZUMDULeG50kuA0vzAmYFJVm+x3Fg4qVnB4uYKjESwYPb9yfFaeNtheNRJGz9EYRqoNNkUq0REDvUaS+KTD9iIYuTgFXjoiWYGbzFUAY9VkK2dR0nUchTQWKV6rLF05i5av0y5eKy9KAI888ggPPvggX//1X883fuM38lM/9VPs7e3x/d///cd9asKScDC4aF58+0vZ2nw+N3a+wp/8/v+P137T94y0nTVCNE2Iiv3NIEbNynij+5jtWDltwpQvdzy49aLh1ou2z3n5y/DpP4CNbfjqr6GoNKdNed6uY2r7Lc8rLxLRKDUOpLrR1jXF+IAas17H2q6b8wiUq4jWIForU/l8x3Zgkxj29qxEDfZsGt/ermsn2Q3LKFS3q1nfMKyve/Siihw10vlg/pQ+mD8aBZNFylUJrgdrEawV0be6SGlNMTnv/qAcJ3X1GVtwojlOqptX8IuUfdxRdEKFb8pO4VwyBbV/gKkylYtUtZJfLlLNSn4TRMpPU9YDzXpLVCEfKxWnHv24TPHb2w95tt+jfyOgn/hZJT9NL0roOINCnnphTCdMi7FTbuAfWqZg8UIFB5Mq4MBiBYeLWuUsKnoFBxOsWaoItp1jNtKNeSNZ+disImVQNcu7+8S6YyUr9UfGboG9DuZyFVbSAz01rM2PVS1+kc+dNWlM1mE7ysvsGC9CuI6zauGipOskCFfOaRevEyFKf/Wv/lWefvpp3v3ud3P58mVe+9rX8iu/8isjBR6E04bBd11ec/df5mO/8z7+4L/8G1737d974L3NExmaJkL1/U5vc4j0f6AiUoHhBS+EC8+H/+sTsLNjI0zN82iTpup+bPtRaYLJ4gSMyFNz0txJ+K4mbZGv1re75VqXC5QT2AhUHoWyAqVHxkLd2HH5ypO2zLkfwNqaYWNdsbaWlTdf8+h0wGtUAlOOP1LifFnRqOo+m2l9ZdtSpPCgG0C3qN43KlKDTKT6Q5f+wKM/7PHcjsv+VTtWyhgIA13KUzZBb57e1wm1nZw3PbxMjaT4tUWjmuXQ20SqRaKK4/o+TprS8aHTSTnX+NvlUpMqpxgTZe9DdvshT+9v1YpOFNLklMUn8nFSnSDFdQ3OFJmCxQuVTlLcZkXDie0PJlUwn1g1qwEuQqxgfrmaVI59GdGrKuMly2DHZbWfW9s5VyUrVsFoNMv1i3TBJPFr6/MxWdXIVFB57OlhEcUKa5KVVSR0pqcjrWpUK+ekyxacTeHKWaUxgXBC5lE6LDKP0skjMQG/+4L/mef/uZfyZ91N/t//37+E63r82L/+CpvnJgvyPJIzjXkjQzCfFM2cI1tpNxzAznPw5S/CS14J65t5m8n7GHcBnHQO4+Y+GSkQUaE51qlYPnZfY5a37KcZeSr33d4ZzSNQSsH+vsPersNgz5TRqL6D60KvZ9jcVGxsaFtQYs0QBKMCVRyvpfJX2zxPMNucUeXylnmgxuy3OYcUtM8j1VxuTDZOqo+dU2roMhh4hVQNB7YMejW9rxvpIiqVT9LbjRSeR02mCmaYW2rmeaXa5pQa1xba55WCsXNLQSlSbUUn9vrQHwb0E7vM4BAFVqQ6Tr8QqbIkelqbU2qaTFWPP7XdjIUkZh1DVbaff76neQpVNDlsmfWDzGk1jsPMdTWORc2BNYlx510dk5VHqGqPlV8UxSgjXWV1Qa8SlQoaKYOeGhTLQy+1Y7O8dObJiBfFqkUcxnHcsrUoTqJszcJuknLvL/+Xkz+PknA2cVG4aYxONHd81Z3ccefX8+SXfo//+ts/z1944G8t/HiHjfg0mVWAxm1nbLVt4iHsZ5PX7u9Bfw+Mht4G3PFVsLnVPrSoyaRfCcel/UEZbWrKzLhoE9QjTm3lyYt1ReRqTERr1sgTjE8HdMv77rq9lREoinFQe3sO/V2Hp5/2+e//3abwRRGsrWk2NhTr64bemhUoz8siWy1RKBiVKO14I7LTFokCG41qSlRbNApmHx9l21YiUkDgQ9AFe2kwtWITYD97g9iOkxoM7aS8e4Mez153bXn0vkFrhyDQVqI6Kb2OZi0YsLGWZsU2zGhEqiUSNTkKBa2RKLDRqDaJ8oN2iQqCsRLl+D6kqS0eEhjWemV6X1VijLEiZSNSmUjtd7ixu8F+bCNS2lREKkjoun37uFJwoilSzgx5wUYpnBmiSSZJitLo08iFatYola6lRB4sUgWHi1bB4iJWsNioVc6yUgOrjDvv0TFZJZPOXRuHuEgPDArBykUqVgGp7pCkfi3KpY1bjsPybIpg4KU12fLUkMBLauOyZo1etXGYiMNRStZhIlurJFmLiG6dZNkSURJWEgeDg8EoTcdPefVrv5cnv/R7PPbhf8Vf+p/+X3kje+fYDkz+eJm/bBmTHSu7N1hx0dree67BAHFslxU3BSq122hdipBK7S1NG4+VPUYQQG/d3i7eYSvedXvT5WieC9AsUpfqloINtfWZbDWr6Y2RJqiLU6263hhxglF5KuZ8aiwv5n3So505z1WkJlvuUIyD8i9o7F/UpvDt7zns7toxUF/+isvenoNKbfre+oZmc0OxvmHY2NBTo5htqXxweIkCK1KzSBTMLlKOB90wF0tw1egA+DhxijLodqyUz5M7W+x82SFJXNa6iq2NhO1un831hM21lCC0n49JAgVWokaq12XpdXXGSNG8EjWBXKLAfuX0fEOvm6dS9UdEaph47FfS+3KRygtOGEMhUr0oF6n6GKnmd9gyZAo4EqGC2aWqLUp1WKmC+cRqWrRqGWIFxytXUK0uODop8aTXkGqXpBCpUq5i7ZPogGESEqtNkmEewQqK60I+9qoQKy8ldJPicTWCNcvYq1kQyToeDiNbxy1ZIkrCSuI6xnYsHVtC+mu/7i/za7/0w/zpnzzORz/0KW69ePfM+3KcyhSw+YNMckaWV9bNk5SaC1ohatkxPd9KjeeB62WC44Dr2HVRBF7P9t983y7zvPrz5vtyGA4S6ZokSPNQpHa0pM+NkyZoLzxRrJtRnGC8POVpe4U8AY4Pa1uwtZ13GOz9YAA7N1z6u/DsNZ8/+zOX4RC6PcP6umF9XbO15bK5qZl5KMkcF37l+a2pfPNIFFiRakvp067fmtLXFCmwqZ7rHVgHXFUf6D4Yuty86XDjps/Text84arPsA+9rmJjLWVzLeFcr8/2RkLgGyAYSeE7nEDBUUgU1P98DtD1oTtBpAaJz/4wKIpO7O33eObGVhGRch1TS+frefuZVMX0wqQ2KW+x30wQpslUfi7zyBTMJlTVdL9lCxWshlTB8YkVzC9XB00JnCZYkf15sFg27TXk0auqYMU6yCJaPjfjNZJsWT5Oqxm5irIIlU0DTArhctWA0E0IM/k67DWzykEk6zjSBEWyShYhWSJKwopjcF2493Ub3PPn/yKf/C8f4trVf8WbvvtHbQFxQy2alD+uSk7b45FfbLPlaerguOOjUo5Dbb2DfT5L+ttBOWga31zHOIQMTRqv1GTc+KUquTS1jWmaJE1QF6eR6npzRp3s/urr/Ahuv6DgAuTRp3gIN3cd9ndd9nddLl/2GPQd1tYMW9u26t7WZsramjff52TMZ7AtCgWLlShoHxs1SaSqEhX24JYe3HbrkLwc8zC2EbobuwE7ux2+9PQ6g30rT1vrCdubCee6fTbXkkqFykMIFBMcdNy1c9FXxLQeCSojUja1rzk2SWts2fNhkEWj4LnBJl/ZCdiPA5R2Cf2UtciK01qY0HX36UVWrEJ//LiheaJSsHyZstscTKjgcFEqmF2qpo2nmlWsZh1XNY9YzTu+ahXECqrRqzI9cNprUdol1mXaX1IRraEK2U16dl0WzcorB/oNkQrdxIqWm9jlFblaRMSqyUEjWMc1DusgkrWKcpUzSbJmre8loiSsLJ6TglY4wz5xX/HGv/w9fPK/fIjf+rUP8P985N24Wa+ztYT1AWiOuznrzCNAk5hFjka2mUHcpqYDTpAmqIvTSHnyMVEnu9/6ujBS3BIZbrmlbDMcwo3rLns7cOWyyxc+30Erm7Z37rzm3DnN9nY2aW7LVKMeCjXm69kjrZUyn4kx/yLjJAoYO+5r3mhUTtCFW8KEW86X8hQPNTu7Add3fJ6+0eNzX94kje2EwtsbCec2E86v7bPWrR/PaTlM26d1ZQUKRiQKbDR5I9BsrA0xan9kk2HisTcMCpm6uRdyNV5jPw6IUx/fU/TCpBCp9Sim6+6zFiVM63/PG5WCo5Epu93044wrSDGLUE0qTHHUQgWrIVUwn1gdtoDFLHIVZpErFe9N3V8etYp1Jk+ZYOWytZd0iVVQ3FQWscrTAEMvIfKSLDplhSrMx1mpPqGXLDRa1WRewTrOAhcHjWCtsmBVEVESVpY1dw83GRLvpwxvxHzrt7+Bf7yxxdXLX+bTf/Cb3PO6b5m4fftEq+PJKxXPWur6ICxK6o6Lg0jP1H0eMJo1TxQsbZm/qUouTeOq6lXFqVlhbyTq5CiiCG6/oIvIkzGafh9u7rjcuAZ/8lmfwSBgY9Nk0qTZ3LJjnVxHt8oTLEGg4EASNW9K3zj8HtwaJdx6iyKXp0HfcGPX57kbAX92dZ0/2t0mcFK2N5NCns5190fmRWuTJ2gXqHHMLVBa1SfXrW2TMDb/Mkkml+dskSiAjgedTsL5Sjn4YhPl2FS+YcD+MGR3z+fabo//f3tvHi1ZVR5uP2esqntv39vzRDfQDNIiqIiKjfOyf7aKMSb5MBqCEI0KwaUoS8E4LVd+yqCJMSZBzZegK1EJrk8xCg4dwCm2EhCEZlJkkm6abujhjlV1hv39cYY6NZ+aq+59n7Vq1a0z17417Kfed797rrAmlqjxjMN4phjegmjURKaIafhoDYQgWbmvFZGC5jJVWclvWGUK+iNU0P0oFaSXqnarAA6TVEEpajWOg1es/tGhkmTEqlKwZp0xCnkrXuf4wfnNMqFyyRjFOC3Qjm66g+kt9Lwq4KikBSZpVbAGJVYiSsLQomsKy3SxLZ/5GZ9lWY3/89o38K3r/p3//u5XecmLzwBqp04B2LXmSk0jKonD1aq81gqVsmaknq21PXopeRHtik0ndJQa2ILcNRMmKL3e6pUkryVOmgZjYzA25rNuPYBHPg+HD+kcOajxmwdMikWN9Rs81q71mFqu4i/W5LXUiz4F66o/zqMCEvUESldeXK2val/llk2uW7bOd/H06s6K4Tvl52pWdMRz4vFPEdmcQzbnsW6NB+TxXY8jsxaHpi0OTWd5eN8y3MKKINq0vMjKqSIrljk1vUNznWBwYAW15EnzHFSNg2iuW1uG6hwb6EygoL5EufWjPpYBU7bHZK4IlP/i7noacwWbuYLFXN5mdt7mwPQ4s4W1uJ5BxnKZCAVqIhvem3NkrfB8TSSgnyIFzWWqXnn0TmQKmgtVmpLpaYQqbfn0QQoV9F6qulVmPa1Y2ahU0SqloBBFqMKIVCG8n3NyHMxPVklVJE4Zo4htBFIVpf9lDIeMWcTWXXAbT2zcTVqRq0FLFQxOrESUhKHG1H1yep68cvjfn8DxW84G/p0f3vAdXve6KxjLjaEbLoYe9FkMIyjfjK6Hj0vLoqIKhqGCdTr4uo6ZKLRQ+atP8uO1npA1vH6tQ9FqUdS6VXhhFGlFiOrRSJAqaSZMEZE4RSXJI7JZWL/BZ/2G4PHMjMa+x+Ge3Ta6rli12mfDRo9ly2qPrYqvo0KekmXLG8kT1BaoKCLUjkBVUdmcbYiTDqxY7rNieZCypymPuQWDpw9bHDxi8+hvJnALihVTRVZOOayaKrJi0sHwHVQNWakpT75XNnFuvG2r8gSDEShoLlFGgamxGtULXZ3ZvM1c3mZmTuOpmTEeObCc+aKNrvklccoGt2XZAmP6fNlnZdpoVL3ri7etMZ9UP0Uq2Lfx+ToRKei/TEE6oWpnbqo0AtLJPFX9FipIL4pa0SEbFmxpRJQCWPDsWKaSUlVwbYq+ScG1UWhlUapMKFZl92Zwr2uqr9XgRk2qoLlY6Sn7VyJKwtCi5QvovoOJw/NOPIh1jMm2FzyTr/37JvbufZwDe7/N9lf9Aa4LSmkUXRPPD0pr+76GV4SipwWPPXB8IyjJ7WlBuW4/KPdcflItqDynq/AedEOF9y6GHkgWho6hR1XqVChgwba6RknMTIXSjfhxK+F3zzewO+z8p4qgLTFakaFGNBOkSuoJU8SyZYplz4TjfZdDhzQOPwV33G5z8ikOq1eXrrleUYqIRlGnYH3r8gSNBSoqIpEUqMqoU2W0SVdeXK4cgpLlVZJUS5y8KDrnsHmjg6ZmmJ03OHgkEKdH7l+GplxWLy+ydmWB1SuK2JaKj1cpT/WiTm3JE9SPPkHd8zTdr5lAQW2JcptHemw8Vk7kWTmRh9WJy/JhrmAzG0ahpmdt9h1exlwhmKUnkKcCk7lCLFA5262qHNhLiQqOX79jW2+C3kGJFHQnKgUiU0mGWagoOmTN4NYIpYhT/8qlyuZQIUvRs8i7NkXPQqFh6Q4Z0yFrFMskKn5sBnNVQf/La4+iVDVCREkYWgw8dDeP6eeZP1TkmONnwYSz//C1fO7qf+GWnddx3p++qu7+9X4RT+JrBn5SrkKR8rwgXcXxzFCqQsHyg76H8jVcB5y8xpynx/v4noaXmDvJ9fQ4z8dXWlwmPBIu00iKWEnQgoiYF2yvhQKml2SstH35Y6UbZTJWOSlqWtqJni1GWpWhRtQTpEp0HVatUqxaBWZGcfevLU55tsOaNbW/5FOlCybkqdZrolKe6s35VHadibFIyfdapTRF45xalSagTJKiqnqVy5RmMD4O4+OBOOHPcGja4qlDNg89McmvHzSZyuXZsDbPhtU62Yxfdrya4gQ1o05AlTxp8XH6LE9QW6CayROkEigdj2W5IssqUvmUIigkkbeZWcgwPZdlz6FJ5vI2mqaCfbIFpsbyLAtFyjRUTWmpJ1GVAtXoOqG7EgWNRaqRRAX71j9nM4mC7okUNJepVkQKmstUOyIF/ZEpSCdU3ZQpSP/cNA0yhkvGcFnWIEoVpf1FMhXdz7sZDhWWBcvcDJ7SMTQ/lKYiWTMQqJxZIGM4ZI0CWbNYloUyiPmK0krVIIVKU6qV2WJGk+npaaamprjOOJ6xdgY7CwNh35pnYT/nVHLHHsvEScfwgu1ZAH772F62nXU2hmHw65u/w9rVq8r2azSgvda4ivL1Tb5YmshXrXNHchWJmOObsVh5XhDV8lW5iPm+huMZocAFj+N1noavKEXFwslpfRUYkq6XboapytISA+mK0hAr0hR10HSVSFEETVMJuStJmZY4R0Tl2JylRloRaoZThAMHdPY/oTE7o7Fihc8ztjpks+n2byVillaka4lT1XlrFHGoLFdeWRyiVkGIyuPUqqSn16hkV2uZpjwKRY39BzM8sT/DwSM2KyfyrF+dZ/3qAhnbb7g/UFWePKbWXExQs8pe6VgN2rHRHE51zpVq34hmnfRG15agnpREEaiZvM30rM70fJYjCxmKrsmYXWRqrBCI01ieyVyBrBWKZwp5iM/dYse+0fU2Pk97nfJmEtV8/85loBWRakSrEpWGdkUqLd2QqTR0W6bS0Opzc3yDghuIVN6zKbjBfT68L3g2nm9g6m4sUVkjEKqcWYj/zhjFsh9fBz35azNaEapZx+X0/+8mjhw5wuTkZN3tJKIkDC265qH5DqbmMDftc2CPi2UqNoyv5rSTn8kd997Ht6//Dhf9+R9XpeckSa4zvfIcfb9CjKzEd0yzCmKtSFetH03SRLzSXAcEnZSiZ4bRMS2+j0TLdTWUTxjt0nB8IxAtD3xXUSxEQpbcvyR3JSkrzx3UtDClUA9FSleBRMVCFsiZFkXDQhHTdVCaHvxthMcJI2fJiXujdEVND4UvPJdG9LeKZS26sqTE9brSUOX/QKnEzQ8E2HU0XDf4HwRpouA4Gk4RPMePZblQ1Cjkg20mJ1VQ2OE5XvqJa2k9rTCKNDUTpmTEqZ40Ra/TykhTUpbqRZmSwlR5nOg9mhSmWhPg1oo+Kc3AzsCmDQ6b1+fJF3T2PZVh74Fx7nt0ig0r59ly1DyTE27N/aE84lQmTclIUEJk6kWcgmOVv+fLxKkyelTvXDXO2TTyBNXRp8pOeZPUvYhakR3leeg6cQRq4wqAaQDyRYPphUxwm83w+MFJ5os2GctlxdgCU2N5psbyLB/Lx3PG1ROVViJQza63EfUiUc0Eql4kKq1A1YtGtSJQjSJSg4xGQe8iUhHNojfdEqlmkaleiFSrUTdL97BsjwmqK2RGOL4RiJNrk/cyFFybOSfH0wtTsVwBZM0iWTOIQOWMAjkzuGXD6FTl9+wgZSpNhKrV6JSIkjC0ZLU8uu5QKGhsnjzEo79bjxumuT3vGa/jjnvv4/+97hZO2PQXmIbCMn1MQwU3W8cIH8fLo3tLxzJ9DENhmflwXdBBTytcUC5dlcIFvZOuepgNJpuspJUy0pVpWSWJKkW6Agkr/e0rcDwjWB9KgxfKlopSE5WP72i4hZJceJ5WLhsKPE9P7BekNgLxcZrFxGORigQsIVbBMoVSWtlkxJqmyj78yyctLkX0onXRrRamRfgaIx7/ZloKy1Jks8FrzzAhk1FkbEUmq5rKUbfGWbVDM2mqnBC3UpaguvR4PWFKHqeRMEFtaSoTHs0gk4Vjj1rg2KMWmFswePjxHLvuXs3ySYfjN86wekWx4XFTSRPUFafKaFNSnKqiTY3Eqck5U+3fTJygbXlKikjW9sja86ydKpVodj2NI/NZDs9nOTyd5ZGnVlB0DMazRZaH0rRyYp6JbEV7tSBQrcpT5XXXPk93BQrSSVSn6XwR9SSqnShUI5FqNxrVSKS6EY1qJBvdjEYNQqSgdVEMZGqBZXbtVD+lIO/ZLLgZ8m6GBddm3s1wMD/Jgpuh4AUFX4LoUyGORo2ZeXJWIFNWnfT1YZApI+U8WCJKwlBj6R6upjhp/QHG1ubjTsdztzyDr3zH4OE993PUxM84/uhjcVwd19Nwo3tPw3F0HNdm3g0fx9tYuJ5G0U+MmwglyzJ9LFNh2HosV5apMA2vjmyptmUrIpKuWsIFJelKIzjNpCvYJuWcH5qJXhlt0Kj5ydHWHD7ULi7QCpGgRKl/fkKglArkKtpO+aXS0EnBiSQq2j9YXzIlreIDNRo7RiL6ZYTRNChFs/TG35d9p90xa7WPVf+Lrh1ZCpZZDaNLUFuYIHhvVabP1RMmCApCnHLiLM84do7fP5HjVw+sZMPaPM88+gimqaqOUXWcetIE3Y82Qbn41Eq1q3POmvvXOkaz8U7xcSrery1EneJDGIpVyxZYtWwhnGssiDwdns9yeMZg76Fl3LtnLabhsWpigZUT86wcX2AiWx0l7nX0qfLaa5+ruu3Spu8tpihURD2J6iSdb7FIFDQWqV6m9bUqUpoGObNIziwCM1Xb+0oj74YiFQrVnJPjwPwK5t0Mrm9i6Q5jVoGcmWcsjESNWXlyZgHbqP9/G6YUPxElYWjR8gVmZ3VW+wc58MQyjp1cQAsHOa614DUvOJXv/uJOrr/5v/mbt/0/YJf2rTWwGqo7KlEUw/U0HGXhuBqupwdpUp4ePHZ1iq7NgqvhpJAtKylXYWSrJFNeKcJlabFgWWawXzTpbSPZarbe9Ap1hSu+zpTFGtJIV7BddUe47rYVKYdVIlaDhhIWdpzMaMbRBnLSqZQtZdKMU4poZdLZNFSKFwTClEaWGi1XmoFteRx/9Dwb1ubZ/Ztl/PiOtbzw2YdZlqkupV33OKGE1BzPFAlMDXmJPo9qjWuKPsNqjmvqVJrSHAPK5alexzuFOEHjqBMEkaf19hzrlwNM4/twaC7HobksTx6Z4L49azB0xaqJedZOzrFmco6M5VVJSjNBqRSoNOOeml177fO0F32KqCVQrYyBqiVQ7YyB6kcUqtPxUPUkqltjopaCREHrz1PXFGNWgTGr+rMSgtS+eSfLgpth3snG0ah5N6jiZ+ouY6E4jZl5xq0Fxqw841Yeo4EY91uipJiDMLQYK7L8bsvrOeVkB7X+OF50RvlEbN/8+Z2c8+lrOGrVcn7zb/8XvdbP9/XmM4G685nUkyyoFi0oyZajzJJcVchWETuOdDlu+TYF34qjHmiUR7Ws8qiWZfqYZiRXPqapY5il7U2jNGanE9mKt0kReUobSUorXcG27QlN2nFfjWg3MtYNeiFyrQhON0hT1AGqCzuUljcv8BAvr3OMeoUZ6hZsSBz/ngcnmJk1OeM5h4NoYdUcAo2PBQ0KQEDDwgyNCkEEx23yv2xW1KFZUYg0x4DmhSHiY3VWICKJ78ORhSwHDlnsn55gej7D1FieNZNzrJ2cqzlHFLRXmKFfRSOCc3XW0e60gERwjO509rtVSCKiFwUloPdFJaB/hSUiBlFgIqKd5+r6OvNulgUnw7ybZc7JMu/kmHOzeL4RRp4WGDfzjNvB/ZiVbxiFCq4l/f92pujw3K//UIo5CKOLoXngFpnQpnnokMWhJ+ewTQ/b1jANn9c96ziWj2XZ8/RhfnLrnbziWSfUL4db69e9YjjIsbI8cNLHKkQrimhV7hPM6g1ooHKtiRYEsuUqM5aoyjTCInYgXQWN+Vi4zEDClIXr6mVjbCJxskw/TCGsSCs0FJZZbJhCCCllKqVw6Wk6Q6Gk1OosV1JLvJpJQRoBaxQZ64aINSKKrg1S1tKQNmJUry1rSVK9/3m3o1O1UJoRy9JJx86x8+ermZ4zmZpokF5YJ7oEQYSprizpRl1haRRhCo7bIMoEpc+lRueO6HWkCco/jxtIU5qIja7DivE8K8bzPIMZCo7BgekxnjyY4aH9K7FNj00rj7Bp5XQwn1N07BYjTsE+petOK03tRJ2Cc7WftgfVkad2xKmX0adO5GlUI1BQPzrTK4GqF4nqh0C181xN3WfSnmfSnq9al3etUJ5yzDk59s2tYt7JUvBsbMNhwppnwlpgwl5gwp5n3MxjhGXOexGJElEShppJa4aia7Bq7GluvW8tjmcEY0pME9PweNEJL+L7d/2Iv//OvUwYL8A0fDIZLRACw8M0fGzTwzKcULKIKyuViOSnc8mChGjViGbVEy0IZcs0gz8y1aesJ1nRubxw3qYoWuUkRUvZgXwVNBbmQtGKol1+FsfVyyraGRVRLcsMi2GEaYJmMp3QcmLBimSsMtU/rXDpKaIf8RiVJh2RWrLRTMCaRb0aiVi7UbCa50lWiuuxnHWbVuQoWN5gPpsWI0nQejQJyiNKB49Y2LbP5HhnnaZ2ZQmC93qj6JIyzcbRpWbClOIaUh8n+nxs1rFOKU1Qko5GspGxPDatmmHTqhl8/ymenB7n8f1j/O7JVawYX2DTyiNsXDFTNaYpKSatSlOwT2/FKThn63IX0WnKXuk45dfQbtSp2/IE1QLVrchTLYHqdvSpllT0MvpUS6D6FX1qVxajCXpXZsvHRTm+wVwxx6yTY9YZ44m5Vcwc2ozrm4yZeSbsSKDmmbTnqib5rZQoAynmICwCdM3HVwbPXf87rKkDQCgDno6LjXvmcXz/rh/x0/tvw3Jfi6lyFAs685qN45kUPQPHM3BcA8cLPjAMy8A0PKxYokpCZds6luFhmcH6YNxQATsDluHXKDe9UF+woLZkQSBa7UhWnXRBCIbmmKZJ1gJqbNZMtOIUQq+UMui4Ok4UzQofe0WNfBz10in6dhjh0spSCJPpgKatlUWtrIR0WaaPYeqYZqEkYokUwohaA/NrEW/XgnTFzdCGfEU06vC3knZYddw6z6ObYtYL6olRsK4/ctRsHZQkyXU1fvvoGI89kWPrcbMN0+7S0DD9DnovSxB8XjSTJUgnTM2eT1phgtJnZkphgsaioeuwYfkcG5bPUXAOsvfQMn6zbzUPPrmKZ2x4ig3LZ2sfv0VpCvZpPdoE6Z9L7XO2fp1JkvLUbqpet8QJquWp2+IEIk+NGGT0Cdp/vpbusTw7y/Js+fs571rMOmPMFgOBenJ+JfNOFstwWWbPMWnPsayOPKVhuL9phSVPzp9m/8GNnLgwh24F0RiNIM0ta5n8nxMmePUzj+XM445i0/helmWDig410xlUkBfrKDuQp/BWdIN7d0FnrmCHj60ywfKVFkexLCOIVkWiZRoemYweS1cUzbJND9ucx7a1OvP5tCFZdQQrQnPrd+Q1s/GHuzJNdMDSCawrcZqGkgVxh8v3Kasu6Lg6rmYnxmWF8hWmEAbyZeJgxYUzohRCXVelohgZrSpqVfXY0jEMPyj5Xke2oH4Vs0bbxds3qvTWQFwaCVi7aXaVstGJjPWSdqQoXt+mHKVZHwnS4RmTA0/b/H5fjvGcx5mnHWIyW6BRjZGmx04zzqeJnDQbr9RV0kaX0jyvVoUp5TimNFEmCCJNW9Ye5pjVh/n901Pc8/hafvfkSk7fsrcsJa/q+OF1tyIioy5NsDjFCXoXdYJqeerFmKd+yxNUC1Q/xz118nyDCNQRVueOlPb1dWaKY8wUxzhSHGf//ErmnBwZo8hkZo7lmVlM7WCq40sxB2FosVdaZI9bxT3GS3n1cx5mak0uXteonCk0nnei0ZwW9SoVeb6Gi12SKk+Po1VFz8AlgxMtc0sS5vnVgmWbib8TglVKE/TjNMG65aXbiWJBQ8lqp/BFRKsFMBqd1/MoiZYXRrRi8aoev1VKLSytIyFbUYTKsLXE+Kzy8VpxyqCpl6cY1pEtSJdO2M62Zft1IWo0DOOd0owzaiRF0D0xcl2Ng0csDhyyefKpDJ6nsXplkY0r51i/unZBgNTn6IIgQTpJahpNikhzTZCu0EMrx4P0RR8gtTBBa3Lh+Rr3PLqcA9PjvPD4x6vmZqp7jjY7/u0UgoD2i0GUztt5J7obhSGC43SvQ9/tAhERvSoUAf0pFgH9LxgBgy0aAZ09Z9fXmS2OcaQ4weHCBEVvnrO/c7UUcxBGm6xRwNYdZvMZxubypV9y6nzIRb8ieXU+XDXTqPshrltW3ePqlolNETuaQ6jindOWYDkGeSfDjKfjeME2bihekWAZejI6VUOwzPIUwkCw3DoRLKBR2LmRYDX79bvDSFYSEzATUa1WRQvKZatIBtfTgrTCZOXBxHitorJLc2+FwkWN4hiGXTuyZZjlaYWGqVcUyGgtchU/tTod5lbEq1YUrBsC1irNRAiai0jabSIpyhd0ZucNjsxazMwZzMyazC2Y5LIeayYXeM4Jh1g5Vawrw6nO1UURSRtFGpgkDQmaYaQWC0NXPHvLIe591Oe2h47iFSc/ku4cltWWfGiW2ZYspY2Y1T9v6xGxSqIfETsVpuiHym4IU/JH0W5KUzLi1G1p6kfECaqjMP0Qp0FGnaCz52zqfpy6dwxB1btU+7VygYLQb5y8x8T4YfYdmWDVxJM13xTJN069D6RGgtWJXEH7glVPriCYJNXFougZFF0zFqyia+I4eixYRc+OI1hF16hKEYzTAcPHmawWjr3ysMM0QTsck9W2YEH7kqUbjTuaptWwU6hME2p0LiPZUpYFFKvWV15DLaLiGEXsWLAqZct1NJx8kEZYVFbZ+mh7oKzsu2HpJdkqm9C49DhapptaLGWmoeIiGZ0KVDcErFXSSEcr2wHge+QLOgsFg3xBZ37BYHbBYG7eZH7BwPU0clmPqbECy8aLbFw5z9SEQzZT+8u932IEPZAj6I0gtRJJgtaiSX3g+I2zPHxgJa6n1SjoU5tOZAnaiy4tRmEKjrU0pQnKxamX0aZkX6hf0aakOA0i2tQPWRRREoaW4kGHzBqLDVO/5/4Dz2NFdgZLd8lmwdQ9bMNF1xTuQnW6TOWvHpVvnmZyVfaLUAO5gtqCFX2gtxu90gCLIhYw3opgaWYcvSrd9EC2lM3CjMERzwgjWKGAuUYctTGNQKCiCFayyEU0Hss2yyNYlq3XT5sxzcYdpmZpgsUeSxbUFC0Aw7AwTI8MTv1PykbpioTj4hJRragioedpuG6QWui6Gk5eZz5R6j0SrSjFME6QDqNbsWyFQlU2qbGhSimHUcTLUJiJfQxD1ZTiXghUS+JDOM7N8Sk6OkVHp1AMbo6rky+UHhccHccJ3ucZwyGX8RjLeSzLFVi/fJ7xnMd4zq2qwKh7TsPxRxGphQhaEo6BilHEMAlSK88zpB2J2HMgi202+DFoyGglalZz/zYlL0m3hCk4VveiTFD6ju12al78w2mPUvOivkWv0/OWojRBb563iJIw9Iz5R1iX2c/vnlqL45s4vomngg9JXfPJZnws3cXSXWwjunewDSf4W3fJZrXgsRGOU2giV72IXEHj6FUquWpw7ECwHDJApiKC1UiuNMOIKwmWR7AC6XJdm9l8JFx2MCbL03E8AwwTDVWVAhhFrGqlCEaRLNPqVLAaS9IgJQvCqmUExTEs3KoCGdF1pMHTrbIiGa5XEq5koQzX0ZlTVjCZcWIcl1dZlRDisVl6QqBMQ6FHMqUHUS9DD8Qr+tvQA9HSddANDcMATVPo4aE9X8NzfXxfw/fB940y8fPc0vU7bunaHFenWAyuV/M9dF2RzfhkLB/L8rFNn7GMy4pxn6ztYdvBuoztB9dS73/R4GXQkgxBT4SodC0tdpx6JHItHzuiD4IErUuS52s8tHeC3+1fyRkn/B5DX/TDsmO6IUsQfLd0a/ySblldHcOkmUZPxjEtFmGCUt+ln2OaFpM0iSgJQ4/veBw/+SDWmF1apjRc36Tom7i+geNbgUCRw3FMpgs2jj8WlAgP1/lKDzr1uoudUdi6i5UUq1C2zFC4slktELA6clWrxGajyBVUfyh2K3IVtFP1B0EauVKOgw4lwQrLizeSq2BnI6gimKgcGP3t6jZFx2B6gbBMeybYxg320UwTXfPj1L+kYGUyehytqixwYRkehmU0Fiyo32mLn1Od9VHRinqSFYpNM8kKtulctCIM3wum1zII5tmquJ40KNOKI1yVxTB8vyQvkVi5ysLJayx4Gp4fSJnva4EIeRqeT/y3UlpZtULDiAQrGCMSRbEMQ2EZPqbpkzNdJjKlohqW6ZPRi0HE0vLrFzKpemLEMtSy+ES0MUannap0LcsQ9DZa1O45oL3Uuj7JEQRjFB/ZN84jTy0nZ7u84LjHWT7WuFhH2Tk77My3W9Qh3r/D4g7DTC9kCXpT+MGwzJ4WfljswgSlvtIgC0F0Ik0iSsJQ4856sBzcQvAhkpwwzMTD1ApBxzHZXzRAN6t7Wa6vx0Ll+Caub+LpWRzfouCYzPi5cJ0VyldJrrIZH8twsXUHO7y3DLcsapXLEYqWF6d3NJOrdiJXwyBX4QZx02fD4gvaWGO50oxgvqZIrOIqgX4oWMqmMGcw4xpVBS78sPy1oVek/kWpghk9Hm9lVQiWafjoZgPBgu5EsaCpZEGDDn2iumC9TnVZ8YuUogWgFb24tL6dEOJWZKt0DZ2PZWooNX54a5UuFCVotyx3WxIE7UkKtP9c2z1fux3bPsrRfMHkqZkx9j2d5enZMZaPLfDso/exdnI+/XkHLEjQPUnqRjRJ6L0sQfC93q9qeUtZmCDRx5JiDsJiwcl76FaL6Rbh+P2kWOn4ZHDJRBXVSivArC9XQUQqEKyiZ8VRq4VE1Mrxg8hVJFam7pHJ+OVRq8oIluGQyxKPtYrP2aJcNYtawfDIVfTFHfXRxwzQss3lCoICF8XEmKto/JWjBdGqaPxV0bXKBCtKDzQrqgNGjzOZUoGLOD0wLtmuSnOe9DqKBV2TrGC7FKIFLclWfOxGaY0DotvzDrUtPhHtCklEJ9LXybk76Vx30GbtyMFC0eTwfIanD1s8NTPOfMFi+XiedVOznLxpP+OZ3pYCLz/G4hSkbqXclR9z9ARusckSBH2HQZQY1wx94LIEteduqoWIkjDUeAs+bt7FyhoNpvrs0rmK5WIFgVxlccnqC7FQAXWjVp6vx9IURad8PRNHrWZVhqI3geNbFL1gGwjGWuUyfvnYKsPB0r1w/FUQycplwTJcTL30IdPzlECoEqCqyfz6IFcQRkMIClxoucbjriJKJdlLguVqdhzNmqkYf+W4wXoAXYvGX4Vjq0KhymS0soqBlSXaDSsRpelYsKBbkgXpRSvYNqVsDYCORSYNncpOkm6U4O7G9XTaSe2w3duRAd+H2YLNzILN9KzB9EKGI/NZXE9nIltkxfgCJ208wOqJ+VQV7YZFjKC7KXbdjiB1W5J6KUi9mm8poteStNQYFllKw+C/7QShBdxCbz8M3YKLmUn3tqglVho+Ni62nq+KWunZaoFRikCa/ERUSs9RjNMBs7jRulCsFBq65pOxVRyhypTJVWnMVS4HtuGUiRWUy1U3xAo6l6vKSYQbylWN4wfrq+VKB7JAVkuXGgjh/yWSK88MS7CXolfFeYM5zwjGX7nZeL2nR4JVMXlwWDEwk5hg2DKTxS2CbXWzBcGC7kkWpBYt6GAs0CjSyzmGutmO3eqEdkFA2+n8KwV5x2S+YAW3osXcvMZM3mYub6PrimXZAhPZIuumZjlx/dNM5gpNizN0Sx66JUawtOSodNzefGb0WpCgf5LUz2jSMDAqsiSiJAw97qyHk/WqCoY1Qg9/0W9VrMyMEY+H6ja1xArApFA91qqBWLnKiKNVjm/iGzmKvknRsZgpWBRrFLFIilXlOKtktcBsGLGy9fIyuq1GrWqFtFuNWkHncqVXFqRoIFdQ3umIigaOZayg0kUdktEr39fi6oFONP5Ky8TjsfKzRtX8Vw4ZfBU0dlSevTxaVSpwYZlBefZoXqygoqBfdg1A7c5uZRQolWRBatFarPRTCnvRmexy5K3dTr7raeQdk4WiRb5osuCYzC9AvmixUDTJO0GxkYzlMp5xyNkOk7kCG1ZMM5ktkGs0zxvDKUTQ/aIMvRh3NGpyBItLkGAwkjSItLskoyBJIKIkjBBOvv4Ho5Wt7DCn/xDVE2lSrYhVL6UK6ouVgY+BU0oHDBaiZ2qXCXN8I45GOb6FMrMUfAvHMckXLBw/V4pqeaXS6xk7KGCRiSoCJotYRLIVjrNKFrCA7qQDQu9TAqF1udIrzlHZcbEA27KqS4Enr6FCbDxfqypw4eph9MozmJvRw/Wl8VcONgqtbPxVsnqgZfhkMlr5fFjh8mj8Va1rSRXJgvSd+mbVE4eZYRhL0cMUw0468b5P/Pp0XIOCa1B0DAquST6vSstcg7xT+sEmZ7vkbIes5ZKzXFaOL5C1XMYyDjnLqVntUDkOuEGBw24x7EIEvSvGMIpiBP2RI1j8ggQiSa0goiSMBG7exczWf7k2kqhKOpEqGM5oFTQWq5zmkEtGrBqIla+0ONWv6Fv4Zjb427WY800cL0vRt4J5lhIFLOwwYhWlAVqxXJWnBOZyVEWsoHk6YPAcO4xaQe9TAmuco5lcaUDWsshWZ7mVtqkUGkrjryLJCsZemUFUyzGYXgjSA8sELBx/FQlWlAYYRKkCqcpk9HhdcF+KYlmmh6kH5btrXVP1RfboNd+tcVL9GOvUZbrRKff9sBKoa+ApLS7dH5SJD0v/uzqFgornTStGKaiegecH71EzEd3MmG5wb7lM5gqhnLtkTI+s5WKZ5Z2jKhHwQHWhL9xtCYqP26OS3aMmRKXj976zvRjlCJauIMFoSRL0UJQ++clPcsMNN3DnnXdi2zaHDx+u2uaxxx7jwgsv5JZbbmFiYoLzzjuPyy+/HDPxBfijH/2I97///dxzzz1s3ryZj3zkI5x//vm9umxhyCgedLBXtv6LdK+kCvoTrYLWxcrMmHgtffjWbyMLD0svMB76Sj2pguqy676ZjUUr71jM+GNBamAY1XLDAhZRxMqOqwCGRSsisQrTAYOy615ZZUDojlhB+RdWO2IFg5Gr4FosckYowXWolBmlqJr/ytPtUhVBz6AwpzMbjsGKqgd6no6r2XGaYDQPlqn7sVQZmkLXfXRdoWsK0zLQtWDCWiNcZug+uqbQNIWmBQUzom2Sf2tEE9mqcPvSY00D3S00TMUaBZQKbr7SgpuvoQju42WqNH+VrzQUhJP56viqtNzzdTxfw3U8XE/H93W8cLnrhevCfaJ9IRBmI5Th6H8Z3IdRSctjWdYL578KpSgxqbSup+zoK1Ad9NF6JT/x8Xs4b1Gvy3QvBiGC/kkRLB0xguGQIxg9QYromSgVi0XOPvtstm3bxr/+679Wrfc8j7POOov169fz85//nCeeeIK3vvWtWJbFpz71KQAefvhhzjrrLC644AK++tWvctNNN/GXf/mXbNiwgR07dvTq0oUhxZ31MCfSzfni5lv7UKonVq1IFXQvWgWjkQYItcuu63Z9sUpGrBzfwovEyrVY8C2OeMHjgm+VpQLatqqSKNtwyFTMaZUmYgU9TAeEoZErqBG9sqxgDrIWBSvC87UgeuXpOL6Bp1t4oWDFHfGwg+4XNFw/mJDW8zU8peP7ZryNSsiA0s0yOVCqNJFtoE11rhMV/q8DwQJiqda05PqKv6Ntah1Tq53kpVT51smtfF8Ll4X34XUHMhQITmliXi0WlVpEIlklkInHkXAaemlb0wjGsOUsF9PwMXQ/nPg3kB/dK2Lowb6mHsiR3qQYQkNajAD1WnaqzteHSVv7NVdRr2UoOEf/OtSLWYoiRI6GU46itknbRppSqptpv1V8+ctf5uKLL66KKH3ve9/j9a9/PXv37mXdunUAfOELX+DSSy/lwIED2LbNpZdeyg033MDu3bvj/d785jdz+PBhvv/979c9Z6FQoFAodYqmp6fZvHkz1xnHM6a1PrmiMFjslRZGrn7Hux5pparl4zaIVrVCrWhVKySlqhXMTOv7pa0EGFFPqupRq9R6RKVYxRGrRIn1IE0wnOeqxhirUsGKxHircHk2q1XNZVWLelGr8ufdPPpZU6wqj1NDeKqux2z+f6wSq7rbpThfi2ONUqXlpSSSDSyzFHUJRSN4XC4j0bqkrJDYJvobQtlRGmgquE+LVq5vmlaKggWPS2IW32vlkbJoXVKABh0l67fMNKIfolN1zj6PS+uHAJXO1d/n1k8ZiliKUgTDI0YwnHIE1W00U3Q49Ss3cuTIESYnJ+vuN7AxSrt27eLUU0+NJQlgx44dXHjhhdxzzz2cdtpp7Nq1i+3bt5ftt2PHDi6++OKGx7788sv5xCc+0YvLFgaEt+C3LEvubAsRmRakqpVoVbdSAGHxjq0Kz9JwbZQKmDZilSxe4YVjrBzXZMG3mPYzsVglUwGTEasoFTBZvCK6z+a0mhEr6E06YPB8exO1is/XrKBFjfMF29X/vzXqcLYqXRBGfooujd6p3ZSzvqIAr/TnKDEImWlGv2UnST/Fp/y8/X/OgxAhGOycRoMWIhguKYLREaN2GZgo7du3r0ySgPjxvn37Gm4zPT3NwsICuVyu5rE/9KEP8f73vz9+HEWUhNHGW6h+M7YTaarFsEsVjMbYqihaNWixsvGww1RA3dQbbh5FrOI0wCg65VrM+VapeEWiKmBUvMJORKai4hXJVMBuiBV0aawVpJIraF+wIGUUq8kkw+3SbTkTesMgRSYNg5Kd8msYXBsNSoAiBj256zDIUIRIUXp61VYtfStddtllXHnllQ23ue+++9i6dWtHF9UpmUyGTKbBxCfCoqGWPNVDpKo2gy5YEew3mKIVERYellYAb7ZhGiCA5+ulyYHDNMBItILiFeNxGmBlKqBtlCYDjgWrrFKgQy6nYdURKyjJVadiBSnlCsrEpllaYFrJqnk97YpXGlqMiAnDyzCITDMGKTqVDFp8IgYtQBHDJEIwfDIEwy1E0N82a+lb4pJLLmlace64445Ldaz169dz6623li178skn43XRfbQsuc3k5GTdaJKw+Gi38l0lS1WqYHiqAMLoR6s0fGxcbD0P/kxqsXJ8E8/MBRMBe0HEatazcPxsPI9V0QvKrUPtqoD15rGyjPrtmEasoD25ghRjrlJMJNzwulLMhdUuqVIOhUXPMElNI4ZFeJIMi/xEDJsEwXCKUMSwCxF0t/2Knsmsk2OumGPBy6fapyVRWrNmDWvWrGnr4irZtm0bn/zkJ9m/fz9r164FYOfOnUxOTnLyySfH29x4441l++3cuZNt27Z15RqE0aFbspSWxSRVkF6seh2pgvRiVVmsoh2pCvbrXbTKsE18t/FrpSRWoOvzwTirBs2cFCvfypYKVtRIBYzEyjD1smqAmcS8VaVCFkHEqlEqYERauYL0gpUkdSSrHi1EuFohKWXdkrFhoVcSOCqS0W+GUWoaMWzCk2QY5SdimCUIRkOEIrrZlnnXYs7JMe9kAzFycsw5wY+SWbPAhLVA1iimOlbP8g4ee+wxDh48yGOPPYbnedx5550AnHDCCUxMTPDqV7+ak08+mXPPPZerrrqKffv28ZGPfISLLrooTpu74IIL+Md//Ec++MEP8ra3vY2bb76Z6667jhtuuKFXly0MMcWD5W+ifopTIxaTVI1q+h+MQgogpPnILRMrbb7pLtE8VkEaoFWqAOhYzBQsHD8XR6yqyq0bpSqAlu6W/W3pTixYmazeMB0wvpYWBCuiHdFK0rF01SPsOHZTxLpJqyK3VIRm1ASlE4ZZbmoxzMKTZNjlJ2KUJAi6265KwYKbYcHNMO9mWXAyLHiZeJnnG+TMAuPWAhPWAkdNHGDMyjNuLWDqQbvNDLo8+Pnnn89XvvKVquW33HILr3jFKwB49NFHufDCC/nRj37E+Pg45513HldccUXVhLPve9/7uPfee9m0aRMf/ehHW55wdnp6mqmpKSkPvoQZFqlKS7eEqlWGvaQ6dF5WHdovrQ7tlVcP9mu9DbpZZj0iKl7hm9kwDTCqABikBDrKwPGsxNgrszwdUPdimTJDuTJ1Lx5vFf1tGw6ZjN4wLbAerYhWLdoRrkZ0VcDaZFiFTQgYNWlplVGRnEpGRXqSjJoARXSzrT1fp+BZ5D2bgmsHAuRlyLs2eTe41zRF1iiSs/KMmQVyZoGsWWDMzJMzixh643ZMWx685/MoDQMiSkJaRk2oYDBS1SuhApGqYL/hkKqIKB1QWVmKYUl1Jy6vbsRl1qOoVbReoWGYeihQCakyXCyttmCZuoepu2SzetO5rRrRqWxV0m35asYwyJkwOoyqyDRjFEWnklEVn4huR4Ic36TgWRQ8O5Ah147/Lng2BTeYM1HXfDKGQ9YskDWLZI0CObMYC1HWKLY071xlBshM0eG5X//h8M6jJAjDSGV6XyOGRarSpv51U6iGYTwVDNeYKhjMuKpg396kAUZE6YC6KpLTACO8NUApcJWBGwqW4weRKlcFYuW4JgVl4HhZnHC7SLp8paObBrrmx+Jkld3XWuZiaH4sW5ms6ki0yp6/ofelw5aUscXa8Y0YBhFc7G08CBaD2NRj1IUnSbflx1VGXOm16EUTwkeTwVsUXJuib1Jw7eAHNN0jYxTJGkUypkPGKDJpzwWZCKEcNRtLW/58evdeHvwnlSCMKGmlatSECgYjVe1EqUSqon1bG1sFrX+xpClcUbUPPpbpgSoEM8am/MbxlYaygjxzxzcDufIsPBVEtlzXoOCbzKoMrj8eRrAC2fKUHouWofmBQOkelu5haJFkeZiJv43wsaH74XIXU/eDL/OMDj3sICUjX8PWyexlFE0kpTcM22to0CwmualHt//npSkwggwBJ7xPpmhHE7YXveCxQgt+qEpMfxHdxq08mbEimfBxxnCapsWVntvgPydElAShx4yaUMHii1LB0pUq6DxaBe19YbUjVxCmBbp5DMCGliQLQLPsMJoV3sKIlecbuEqPo1cFZeD6Np4qSVa0j6cM9LBggqH5CZlKCFYU8QqXl2/nlz2OomOG5mNmSu/1QXTk0qYlSqdb6DVLQWSa0e33WSmaH/zQFPzglIzaG4n0aSP+QSpKpfaVjoYKovVGqciPqXtkdIdcplBW8CeqrmqmkB+v6IILo1RyRURJEIaExSxUsDilKs14qn5LFQxWrIL9249aAW3JFZTGXSmnGGcHZiAQrRTpghFmxkQp8JQeSpSRuNfjzoerDDxXp6gMXD+YXNhTemmdr8fLMErv20igkvdJkdK1ILKlR7JVsV3NZeG2Ubpho2jQqHdOuz3+bLEx6v/fYaaXPx54vo4bfn5Ufo4E97U+g/RYdJLLgVh2TN0P773SuFDdDSvAlcaGRsstw8XUvLppb1Wf635wGyX5aQURJUEYMUZxHBUMJkoFo5P6B92RKugsWgWdiVWwf+upgNB+1CqiU8GKSF5/mV9p4S3FS7SyIIevtLjzEwlYUqSi5a4y8H09SH9ROq6y8ZUe7qPjh50mX+koI5hHK6pKGFyiKhMsPXFfLlQVy0PJqlweLFM1t0uur6SXaXsiAkKSfkQ+o/evr4IxlNF7N3j/aXHKb/QeLXsvR8vDiLWfWOcm3v8RGiqOXEdR6yhtOEoPtg0Hy8pXpRNHacZmnQhP089YBbiB+wgBIkqCsIiRKFV6hiVKBcMrVdB9seokYgXt57B3Q7CSNJKtiCiD0EwuiH61bfCSalYFsRT90vHDjlepM6fHMhV00MK/w3VOvI+Fr7RSZ08Z4eNgGWZpfVLM4udfJk4+OgotIV6apsIOoB/IXLhOQ5XWawodFR9Hi7ch3lYLxSx5DBLro/0qt9Ugsbxhc3aVfldLHOV0SaVAoaGUhk/w2lMqeJNEr0VF8BqMtlFKw1djKPTE6zNYrogEJtzP1/Hj7TRU/FoPt/WN4Ly+jo+GF23v6+ErKCB6nUURXU2r+GEi/qHCC8ftuGQT6029PBXXSKTqBuuCHx7aHp+zyCM8lbTzHQLgpfzuE1ESBGEkhQqGP0oFi0uqoPNoFXRWuCLYv71xVkk6GSRcqxR7L2QrIu3zjaJf8bs0KWI1aKcMPZQ6tHEnM/GrelWHNdEBVYnlPjrK1xL7hR1gwA+lTSm9tB/BY0wz6ASXdZSD80Sd7KiT3IhIwLRYwIjlKmg2BVqFYCX2JVxOtG28XWl5tK58WbRtab8kWmJ95bJ2qGyH6H9XuU35RDFaYvugnaO/g+1L61RiXdz+UPY34X61/l+E29SS7yR6KNM6fkKGg/+fnhBsTUuIdkKcdUIR0Tw0vX6kVCMQHT2MzOr44DrhNl7dCGrHhB8fyhPBGTZElARBSI0IVev0MvUPBitVEb0oWBHRaRpgcIz2vpDriUSnlZjqzXnVDdmKSDNPVqcdlWjoV92VsVm0L2XtEgmBX9GBJ45IEMiaCpZFslDaNuzwq1ibAOJoRkl7Sp39aN9gefJaEmIQ4Sf2p8bfFX3xWuKnoeLlSZmqjH4k0bXq5YEUVh8biCN9SdkrtSQQSkq8bUI2Cf+Otk1G/UjKafh3IKAq3lYfQASwJku8pzwqQtMrlvi/XxCEXrDYhQpGL0oF3ZeqiLRy1ctxVRGtihXUnxi4kw5CIzHoRsnbZhMMd1O6krQyUXGSQXa2Imfrt6wJQrdZ6tIyCORTQxCEgTGqQgWLO0oFrUkVdDda1c30P2gsVtBduSodszeSFdHN+UWaSVeSXglYJe0KWSOWUidzKUvhUvo/C7VJ8zmV9rNs6b6TBEEYGUa10h+MTpQK+iNW/R5TBd0XK2hPrqC5YAXH7ryj10pHuReTOrYiX7Xol5DVoheS1m9EFoRhY5Dv6U4QURIEYVGRRqqGTaYiBhmlguFJ/+tF2h+kkypoXawgnVxB+4IF6SSrdJ7udJTbjUz0Qr6SdCpijRjFDt1ikDuhfUbxNTsqiCgJgrDkGOWUPxi8UMFozU8V0WqhirRiBb2Vq4hOJAtaE63SObsrPN1KCeu1iFXSSzHrBtJRFoTeMNzvfEEQhAEyyil/MNi0v4g0QjUsKX+V9FKsoD25gtYFK6JT0YL2ZKv8GnojOL0ek9NvMWvGsIubIAwDjd63npPyu6NbFyMIgrCUWSpRKhjcWKp+FadoR6qgu6XVG9GuYEH7khXRDdmCzoWrkl6PyRm24gjDJm69QoSw9yyV11K7yCtQEAShj4y6UMHwT/TbiVBBf6Qqol9yFdGJZEHnohXRLeGK6LZ4VTKo4gjDJmj9RjrxwqBZ2u9AQRCEIWWpCNWgyqd3KlPxuXpQRr0Z/ZarJJ2KFnRPtpJ0W7wiei1gzRjG6nVLXd6EpYW82gVBEEaYUReqZjI1yhX+ap53AGIF7clVRLckK6IbsgW9Ea5KeiVgEYMWsXYYRnnrBSKEo0Wrr0vXlTFKgiAIQsioFqZY7BX+mp67RbGC7slVxDBJVpJuCRf0R7pq0WsRq8UoytkgWCpCKDRGREkQBEEoYxSjVINK80syqChV1XUMKGpVi04kC3orWkm6KV0Rg5KvZgxCzioRWRNGBRElQRAEoS1GbXLfYZCpiFakCoZLrCJ6KVgRnYoW9E+2KumFfEUMq4SlZRhkrR1E8JYeIkqCIAhCz2gmU8MkUjAcqX61GJZoVZJhFqwk3ZAtGJxw1aKXEhYx6jLWC0ZV8IRqXDdlSnWPr0MQBEEQ6jJqIhUxTNGpSoYpWlWLdgUL+i9ZSbolXDBc0lWPfshYJSJnwrAhoiQIgiAMLaMqUjDYSXxboVWxgv7LVcSoSlYl3ZQuGA3xSsMg5KwWImxCxPB8agiCIAhCi4yySCUZ1pS/egx71KoWnUgWDJdoVdJt8YpYLALWKsMibGkRsesdw/uuFwRBEIQOGbWCE80YlShVJe1ErWA4BCuiU9GKGGbhqqRXApZkqcpYNxk1sRs0rYjl6LxbBUEQBKEHLJaoVCWjFqWqxSilBaalG8I1SrLVjH7IGIiQCSV8x8OXYg6CIAiC0DmLLSpVSStRKhhusYLFEb1qRreiW7C4pKsR/RKySkTQRpul8e4QBEEQhB6y2GUqyaim/zWjXcGC0ZKsSropXbB0xCstgxK0eoi4tYa8mgVBEAShDywlmYpYbNGqeixVyapFt8ULRL66ybCJWyOGQerklScIgiAIQ8JSlKkkS0WsknQiWRGLTbYq6YV8JRERG056KXWul+6zRl4ZgiAIgjBCpJEpWNxCFdGqWMHikKtKOpWtxS5azRARE+oh/zlBEARBWIQs9ehUPUSuqpGoVm/ptYglESnrLj175z/yyCO8/e1vZ8uWLeRyOY4//ng+/vGPUywWy7a76667eOlLX0o2m2Xz5s1cddVVVcf6xje+wdatW8lms5x66qnceOONvbpsQRAEQVgyFA86qW5LHW/Bb/m21HBnva7chM5w8+5AbouVnmnn/fffj+/7fPGLX+SEE05g9+7dvOMd72Bubo7PfOYzAExPT/PqV7+a7du384UvfIG7776bt73tbSxfvpx3vvOdAPz85z/nLW95C5dffjmvf/3r+drXvsYb3/hGfvWrX3HKKaf06vIFQRAEQQiRdL/WkchVe3RTliTK1T+GRZa6HVHTlFKqq0dswKc//WmuvvpqHnroIQCuvvpqPvzhD7Nv3z5s2wbgsssu4/rrr+f+++8H4E//9E+Zm5vju9/9bnycF73oRTz3uc/lC1/4Qs3zFAoFCoVC/Hh6eprNmzdznXE8Y5q8aQRBEARhkIhQdRcRrP4g4rV4mPM8Xnf/3Rw5coTJycm62/U1kfHIkSOsXLkyfrxr1y5e9rKXxZIEsGPHDq688koOHTrEihUr2LVrF+9///vLjrNjxw6uv/76uue5/PLL+cQnPtH16xcEQRAEoXMkQtVd2k31E8FqjV6kBop8DTd9E6UHH3yQz3/+83HaHcC+ffvYsmVL2Xbr1q2L161YsYJ9+/bFy5Lb7Nu3r+65PvShD5XJVRRREgRBEARhdGhlfJRIVet0MpZKJKs79GpclghYd2hZlC677DKuvPLKhtvcd999bN26NX68Z88eXvOa13D22Wfzjne8o/WrbJFMJkMmk+n5eQRBEARBGA5EqvqLSNZw08vCGEtJwloWpUsuuYTzzz+/4TbHHXdc/PfevXt55StfyZlnnsmXvvSlsu3Wr1/Pk08+WbYserx+/fqG20TrBUEQBEEQWkGkarB0WhVQRGuw9KM64bDIWMuitGbNGtasWZNq2z179vDKV76S008/nWuuuQZdL39hb9u2jQ9/+MM4joNlBR9EO3fu5KSTTmLFihXxNjfddBMXX3xxvN/OnTvZtm1bq5cuCIIgCILQEiJVw4eI1uKn1zLm+umO37MxSnv27OEVr3gFxxxzDJ/5zGc4cOBAvC6KBv3Zn/0Zn/jEJ3j729/OpZdeyu7du/nc5z7HZz/72Xjb9773vbz85S/nb//2bznrrLO49tprue2226qiU4IgCIIgCINEpGo06MY8VyJbS4OeidLOnTt58MEHefDBB9m0aVPZuqgi+dTUFD/84Q+56KKLOP3001m9ejUf+9jH4jmUAM4880y+9rWv8ZGPfIS//uu/5sQTT+T666+XOZQEQRAEQRhZWp3IV8RquBDZWhr0dR6lQTE9Pc3U1JTMoyQIgiAIwpJAxGrpIMLVOnO+xxt+f+9wzaMkCIIgCIIg9B6JWC0duhHdAhGuWogoCYIgCIIgLHFaFSsQuVpsdEu4IhaDeIkoCYIgCIIgCC0jUSuhEd0Ur0FJl4iSIAiCIAiC0HMkaiW0S7ejXZ6f7ngiSoIgCIIgCMJQInIlDBIRJUEQBEEQBGHRIHIldAsRJUEQBEEQBGFJ045cgQjWYkdESRAEQRAEQRDaQARrcSOiJAiCIAiCIAh9RARrNBBREgRBEARBEIQRQASrv4goCYIgCIIgCMIipl3BgqUtWSJKgiAIgiAIgiDUZClLloiSIAiCIAiCIAhdZ9QlS0RJEARBEARBEIShohPJgu6I1pIQJaUUAPPKH/CVCIIgCIIgCILQa+af9uqvC50gcoR6LAlRmpmZAeB8/+EBX4kgCIIgCIIgCMPAzMwMU1NTdddrqplKLQJ832fv3r0sW7YMTdO6euzp6Wk2b97M73//eyYnJ7t67KWOtG1vkfbtLdK+vUXat7dI+/YWad/eIu3bWxZD+yqlmJmZYePGjei6Xne7JRFR0nWdTZs29fQck5OTI/tiGXakbXuLtG9vkfbtLdK+vUXat7dI+/YWad/eMurt2yiSFFFfoQRBEARBEARBEJYoIkqCIAiCIAiCIAgViCh1SCaT4eMf/ziZTGbQl7LokLbtLdK+vUXat7dI+/YWad/eIu3bW6R9e8tSat8lUcxBEARBEARBEAShFSSiJAiCIAiCIAiCUIGIkiAIgiAIgiAIQgUiSoIgCIIgCIIgCBWIKAmCIAiCIAiCIFQgoiQIgiAIgiAIglCBiFIKHnnkEd7+9rezZcsWcrkcxx9/PB//+McpFotl291111289KUvJZvNsnnzZq666qqqY33jG99g69atZLNZTj31VG688cZ+PY2R45/+6Z849thjyWaznHHGGdx6662DvqSh5/LLL+cFL3gBy5YtY+3atbzxjW/kgQceKNsmn89z0UUXsWrVKiYmJviTP/kTnnzyybJtHnvsMc466yzGxsZYu3YtH/jAB3Bdt59PZSS44oor0DSNiy++OF4m7dsZe/bs4c///M9ZtWoVuVyOU089ldtuuy1er5TiYx/7GBs2bCCXy7F9+3Z++9vflh3j4MGDnHPOOUxOTrJ8+XLe/va3Mzs72++nMnR4nsdHP/rRsu+yv/mbvyFZ/FbaNz0/+clP+IM/+AM2btyIpmlcf/31Zeu71ZZp+haLkUbt6zgOl156Kaeeeirj4+Ns3LiRt771rezdu7fsGNK+9Wn2+k1ywQUXoGkaf//3f1+2fEm0rxKa8r3vfU+df/756gc/+IH63e9+p7797W+rtWvXqksuuSTe5siRI2rdunXqnHPOUbt371Zf//rXVS6XU1/84hfjbf7nf/5HGYahrrrqKnXvvfeqj3zkI8qyLHX33XcP4mkNNddee62ybVv927/9m7rnnnvUO97xDrV8+XL15JNPDvrShpodO3aoa665Ru3evVvdeeed6nWve506+uij1ezsbLzNBRdcoDZv3qxuuukmddttt6kXvehF6swzz4zXu66rTjnlFLV9+3Z1xx13qBtvvFGtXr1afehDHxrEUxpabr31VnXssceqZz/72eq9731vvFzat30OHjyojjnmGHX++eerX/7yl+qhhx5SP/jBD9SDDz4Yb3PFFVeoqakpdf3116tf//rX6g1veIPasmWLWlhYiLd5zWteo57znOeoX/ziF+qnP/2pOuGEE9Rb3vKWQTyloeKTn/ykWrVqlfrud7+rHn74YfWNb3xDTUxMqM997nPxNtK+6bnxxhvVhz/8YfXNb35TAepb3/pW2fputGWavsVipVH7Hj58WG3fvl3953/+p7r//vvVrl271Atf+EJ1+umnlx1D2rc+zV6/Ed/85jfVc57zHLVx40b12c9+tmzdUmhfEaU2ueqqq9SWLVvix//8z/+sVqxYoQqFQrzs0ksvVSeddFL8+E1vepM666yzyo5zxhlnqHe96129v+AR44UvfKG66KKL4see56mNGzeqyy+/fIBXNXrs379fAerHP/6xUir4crEsS33jG9+It7nvvvsUoHbt2qWUCj48dV1X+/bti7e5+uqr1eTkZNnreykzMzOjTjzxRLVz50718pe/PBYlad/OuPTSS9VLXvKSuut931fr169Xn/70p+Nlhw8fVplMRn39619XSil17733KkD97//+b7zN9773PaVpmtqzZ0/vLn4EOOuss9Tb3va2smV//Md/rM455xyllLRvJ1R2NLvVlmn6FkuBRh35iFtvvVUB6tFHH1VKSfu2Qr32ffzxx9VRRx2ldu/erY455pgyUVoq7Supd21y5MgRVq5cGT/etWsXL3vZy7BtO162Y8cOHnjgAQ4dOhRvs3379rLj7Nixg127dvXnokeEYrHI7bffXtZWuq6zfft2aasWOXLkCED8Wr399ttxHKesbbdu3crRRx8dt+2uXbs49dRTWbduXbzNjh07mJ6e5p577unj1Q8vF110EWeddVbV+1natzP+67/+i+c///mcffbZrF27ltNOO41/+Zd/idc//PDD7Nu3r6x9p6amOOOMM8rad/ny5Tz/+c+Pt9m+fTu6rvPLX/6yf09mCDnzzDO56aab+M1vfgPAr3/9a372s5/x2te+FpD27Sbdass0fQsh4MiRI2iaxvLlywFp307xfZ9zzz2XD3zgAzzrWc+qWr9U2ldEqQ0efPBBPv/5z/Oud70rXrZv376yjg8QP963b1/DbaL1QsBTTz2F53nSVh3i+z4XX3wxL37xiznllFOA4DVo23b8RRKRbNs0r+WlzLXXXsuvfvUrLr/88qp10r6d8dBDD3H11Vdz4okn8oMf/IALL7yQ97znPXzlK18BSu3T6LNh3759rF27tmy9aZqsXLlyybfvZZddxpvf/Ga2bt2KZVmcdtppXHzxxZxzzjmAtG836VZbyudFOvL5PJdeeilvectbmJycBKR9O+XKK6/ENE3e85731Fy/VNrXHPQFDJLLLruMK6+8suE29913H1u3bo0f79mzh9e85jWcffbZvOMd7+j1JQpC21x00UXs3r2bn/3sZ4O+lEXD73//e9773veyc+dOstnsoC9n0eH7Ps9//vP51Kc+BcBpp53G7t27+cIXvsB555034Ksbfa677jq++tWv8rWvfY1nPetZ3HnnnVx88cVs3LhR2lcYWRzH4U1vehNKKa6++upBX86i4Pbbb+dzn/scv/rVr9A0bdCXM1CWdETpkksu4b777mt4O+644+Lt9+7dyytf+UrOPPNMvvSlL5Uda/369VWVraLH69evb7hNtF4IWL16NYZhSFt1wLvf/W6++93vcsstt7Bp06Z4+fr16ykWixw+fLhs+2TbpnktL1Vuv/129u/fz/Oe9zxM08Q0TX784x/zD//wD5imybp166R9O2DDhg2cfPLJZcue+cxn8thjjwGl9mn02bB+/Xr2799ftt51XQ4ePLjk2/cDH/hAHFU69dRTOffcc3nf+94XR0elfbtHt9pSPi8aE0nSo48+ys6dO+NoEkj7dsJPf/pT9u/fz9FHHx1/1z366KNccsklHHvsscDSad8lLUpr1qxh69atDW9RXuWePXt4xStewemnn84111yDrpc33bZt2/jJT36C4zjxsp07d3LSSSexYsWKeJubbrqpbL+dO3eybdu2Hj/T0cK2bU4//fSytvJ9n5tuuknaqglKKd797nfzrW99i5tvvpktW7aUrT/99NOxLKusbR944AEee+yxuG23bdvG3XffXfYBGH0BVXZilxqvetWruPvuu7nzzjvj2/Of/3zOOeec+G9p3/Z58YtfXFXO/je/+Q3HHHMMAFu2bGH9+vVl7Ts9Pc0vf/nLsvY9fPgwt99+e7zNzTffjO/7nHHGGX14FsPL/Px81XeXYRj4vg9I+3aTbrVlmr7FUiWSpN/+9rf893//N6tWrSpbL+3bPueeey533XVX2Xfdxo0b+cAHPsAPfvADYAm176CrSYwCjz/+uDrhhBPUq171KvX444+rJ554Ir5FHD58WK1bt06de+65avfu3eraa69VY2NjVeXBTdNUn/nMZ9R9992nPv7xj0t58Dpce+21KpPJqC9/+cvq3nvvVe985zvV8uXLyyqFCdVceOGFampqSv3oRz8qe53Oz8/H21xwwQXq6KOPVjfffLO67bbb1LZt29S2bdvi9VH56le/+tXqzjvvVN///vfVmjVrpHx1HZJV75SS9u2EW2+9VZmmqT75yU+q3/72t+qrX/2qGhsbU//xH/8Rb3PFFVeo5cuXq29/+9vqrrvuUn/4h39Ys+Tyaaedpn75y1+qn/3sZ+rEE09ckuWrKznvvPPUUUcdFZcH/+Y3v6lWr16tPvjBD8bbSPumZ2ZmRt1xxx3qjjvuUID6u7/7O3XHHXfEVde60ZZp+haLlUbtWywW1Rve8Aa1adMmdeedd5Z93yUrrEn71qfZ67eSyqp3Si2N9hVRSsE111yjgJq3JL/+9a/VS17yEpXJZNRRRx2lrrjiiqpjXXfddeoZz3iGsm1bPetZz1I33HBDv57GyPH5z39eHX300cq2bfXCF75Q/eIXvxj0JQ099V6n11xzTbzNwsKC+qu/+iu1YsUKNTY2pv7oj/6oTPqVUuqRRx5Rr33ta1Uul1OrV69Wl1xyiXIcp8/PZjSoFCVp3874zne+o0455RSVyWTU1q1b1Ze+9KWy9b7vq49+9KNq3bp1KpPJqFe96lXqgQceKNvm6aefVm95y1vUxMSEmpycVH/xF3+hZmZm+vk0hpLp6Wn13ve+Vx199NEqm82q4447Tn34wx8u61hK+6bnlltuqfl5e9555ymluteWafoWi5FG7fvwww/X/b675ZZb4mNI+9an2eu3klqitBTaV1MqMSW3IAiCIAiCIAiCsLTHKAmCIAiCIAiCINRCREkQBEEQBEEQBKECESVBEARBEARBEIQKRJQEQRAEQRAEQRAqEFESBEEQBEEQBEGoQERJEARBEARBEAShAhElQRAEQRAEQRCECkSUBEEQBEEQBEEQKhBREgRBEARBEARBqEBESRAEQRAEQRAEoQIRJUEQBEEQBEEQhAr+f85QTBp9GhC1AAAAAElFTkSuQmCC", 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", 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", 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", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -299,7 +291,7 @@ " x_bounds=(X0_BOUND, X1_BOUND),\n", " yaw_angles=np.array([[[20.0]]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[0])\n", + " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[0], clevels=100)\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes[0])\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes[1])\n", "\n", @@ -309,7 +301,7 @@ " x_bounds=(X0_BOUND, X1_BOUND),\n", " yaw_angles=np.array([[[0.0]]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[1])\n", + " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes[1], clevels=100)\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes[0])\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes[1])\n", "\n", @@ -321,7 +313,7 @@ " x_bounds=(X0_BOUND, X1_BOUND),\n", " yaw_angles=np.array([[[20.0, 0.0]]])\n", " )\n", - " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes)\n", + " wakeviz.visualize_cut_plane(horizontal_plane, ax=axes, clevels=100)\n", " wakeviz.plot_turbines_with_fi(fi, ax=axes)" ] }, @@ -345,26 +337,22 @@ "outputs": [ { "data": { - "image/png": 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xCIIi4+deoJifrHd4Zg2q19yb9wEHVfWwqhaAzwEP1ymWy/LJA3FiiRyo4rppmtvvJpXtY/TEP9Q7vHWvv7ebAwezFEt2CdHU1Jqov0x9BJQI/DlUfTQoEfh5Jsf2kp8bY3zk2XqHZ9agel367ANOVL0+CdxfvYKIPAo8CtBZpzADigTBLBr4zEwe4fzZo8QmBNfNU5gfq0tMZsHx02cpFJMA+L7PxamZhakyNKC9OVfnCOtvJl8gE1/bkz02oCvWX9AYdZipPUUJglnmZ05TmL9AKtuF62WYmzpLPNlb7/DMGtSwd7NR1cdVdY+q7mleZgLQWvDIIqJcvLCX2anj5GfPILikmzbSufEH6hKTWXD89FnisYWZ8p8/8BpHTp7h6KkzPHdg/Vya9le49vv80dM1jMRUa4Q6zNSWr4pPHnFcmjp24ft5Ji/sBZSSP4fvz9Q7RLMG1es07xTQX/V6Y1TWUErMARl6Bt+D40Gx0EJbz05cb4LZqaP1Dm/dk6qZ/V3XJZmI84Y7hhFVvvvsS3WMrLa+uf8g5yfnGews0JVL2WjSm29N1F+m9l7UcEin62TINm0l1zrM5IXn6Bp8M5NjLxAE138LNrN+1atF7Wlgm4hsFpE48AjwRJ1iuSzBRQQ0KFGYO0+uZRep7EZaOndRzE/VO7x1z3UdpmfCz2H0wjjJ+PqsBN+6fQstmSaOjY7ztb2HeOn4GUYn7cz9JloT9ZepvbskjRIQBLMUCxNMjIUDCIr5SUq+3XvVXJ+6tKipaklEPgx8mXB4+6dUdV89YllJjAR5Yojjks0OUZjvBqCYn6SlwwZ51dv2LZv4y787QKk0QzIOu3cOV5Z1d7TWMbLaSsXhB7cH5FL9FIoFzoxPcejceV48ml/xsqi5Pmul/jK154qQIEk+uh1fYW6URLqNU4e+CBTp2/JILWcNWvPy+QKF/ByOQDoRX7eThNeth6uqfgn4Ur32fzUCfIKgQH72LKXSONPjB/AmCiQzHi3tt9U7vHWvOZdl98772D50nlw6ahyO5ibbOtBX03nU6slxIJcK526Kex6Dna0MdrYyO5/n3MXpOkd3a1oL9ZepjyQOU9JKS8duXC+GF/cQ91780gSJVLtNeHsVZmeneP6Fl3GcefLz8zTlMpQKBVpbmhjetpXYOpviqGEHEzQCRXHdZpradpFt2gIakMr1k20aZG7mTL3DM5GYt75H1B0fHV+2PBWPsalz/bQsGlNvI1qkQB7VEo6zMNra9ZJ4sSyzU8frGN3aceTIAYaHb+dNP/BG7t2zm0w6zZveeD+tLS3sf2X9DBQrs0RtBW7U4Oi44VxqXryZptYd5Nq24bp2zzbTGFSF2fz6TlaNaRRKgOoMxfzEovJYoom5aTvBvxqKkk6lAWjK5ZieCfvcbujtqTxfTyxRW4GPjx91AJ2bPoUGJYr5CQrzExQLNpjANIae1nb2nuiqdxjGrHtdEsMnjkiWmanD5OdGK8tEnEUj1c3lbeyLcfrMYSYmJnjt9ddpyoVzYuo66c6ylJ2Gr8BDmSsFPPO1U8SSCYQEuGdI5yDwLVEzjWN8ZpKXj59lvhD2VUvGPLqa0nQ1ZescmTHrR1EDlDn8Up5Xn3s7qscROUEq104skUX9TpKZFpLpAHFcxBFaOwtsGArwPAFPcN1weh0vBvE4tHTU+Zeqg/vvv53jRw5z9NhxmrIZtg5tBsKJzXduH77C1qvL9332HTxKLpVgsKcDz6l9sm2J2gru5CIpDdg4eYjxyQ4OcoHSyI8wicvpplaOv9JLLOHT3lsknoQNQyXaemyU3Y24Y3hbvUNYc/afPMvZiSy7BtKkYuGZ51yhyPGxCUYnZ9jZZ61t69GQWPeMWjtMnm3M0aIn+eG5n2GWEqfIsnduOyUcetlDnCM4gAABwik2s592IGwtqp4F0cdlanAnsXi0TCTcTuHpr0IyA/1bIJaAcv96EXAFXA82DUNnT3TDFid8OE40zsoDt0FnNDp37hxbt24BQDRACO8243kezU1NNR0o5rouA71dnDo7wlMvv0pLNs22/g0kanjHF0vUVvCw28oAcbLyp0xoiXMUGZZPAvCtySTdk0McZxv7j76BAgkOfWOA13DIDw/jxYWegRJNHUpLR7Auz4qux3967FfDJ+u0ift6jE1Os73vTja0nq6Mem3NpOhtzvL3+w+HNzwy686jriXotVZA+Q/exujVDM8HM/ykk+cn+Q57dZY75FvX9H6qcPJYP1O0VMqcKKFThJNs5tDX76CAE5UsCHB4iX5KXD6hyL3jAZLJKDlUEAfy80J3r9I3CMM7wI0akJzozR0HYh70D4QtfjeDqqAaJp2NoCWXpTWb5sSZc7x69CQdLU30tNduoJYlaisYIM5BzYOGnfm2SbKybIs7SZ/sZ5j9PMgXgOhL5W/k1VfvZpYm9r28hzHSHKGFCxt2EUtAPAGZZqW1U0mkYdsuaGqFdBMk7ATYXAfXcZiau7SD7cTMHJ5rfWKMqaWiBsTEYV4DfGBeA9ywTeiaiUC/d4LFt5ZdsJ2XeXCFuZZVw4RtObNBhle+chfzpKu3QFAcAk6zmS9e5iyvRIzSO99HLMoBwxYvKs2ByZQytLVILBa+F9EiVfAcZdu2Ipu3FCsJooNTScpirs/ExGbamufIZhvnhH1sYpLTYxfYOdS/KEkrlkpMTs/QlEndtBkILFFbQVZc7pY0JVW8Jal9n1x6KhF+qU7Sz0kAfow/B8I/zpkzYV8hH5cDeg8n2cQ0LTz3V7dRwqNInOLu+4jFoX8r5JqhcwNs3g5xL0zwjFnOHQO9PPH0Kb65f4RULLz+MV8s4QrcOdBT5+iMWT96ibFP50jgkFdlqyR4Tecpogws8z/jZhMBl+W74+TcKe7lO9f93vknf4sCyWWXjQVdvM4uCktSDEEJcPhrtjBN05JloRIeufe8jb7eSfKFEfxiHkFxnTjZTBebBlrYunkekfCSqERXESQI8Fxl26ZZchk/LFPClkINWwKv1/jkNMdOn6OzpZkNne2Lls3M5Tl3YYK9h4/Tmssy3N9DMra6qZUlaldhaZJ2rUQg6y5MPPoAf3/JOnN+krPP9TNFEy889UbmyfIcvXyTZkDw3vQAmWyY9CUy0NMLg9sgmYR05obCM2tcczrFnQOD3DWYXDSYIF5uTbPLyMbURIfEaMEjT0BSHFwRWsRDVW+5e/AmnAIJCssuyzmTbObgdb1voMLzf5djFodWEsRwAaVIwDjzHKaPg3IXURtd1ZaKT4wxepdtRYwxz/2PvR0XRQNFogRWAc/xuWf7RXrb58PqUhVFEA2Yz+d5/fgZhjZkGNrYc0l9mkun2LG5H98POHDsJGMXp9i4ynfGsUStQaTceTbzOgB38mylXBVK6vHqd++o9FOYoINXuYtxSnz9bT+N64HnQd8gZKKkLdcKu+4JE7mW1hs7mzBrQyLmkfAWPmi120cZU3OeCB6LZ86/1ZK0m8kRxXPO8hYnvezyF4Mz3OXsv+z2l7vcOx60ce6/fHPZ9sU54nyRNlQWPjdBKWiBCS5SJMn9v/6v+M7zwaJ+gAQBqtCSnaezpcDYhMvYxDn2DLdd5W97dSxRa3AiEJMSdzgvLCp/F38NQPDt/8i8ppkJMuzlXuZJIShjdPBnDBLgETz4HtIZyGSVLcNKKiU4Ap09yrbbw0ursdoNYDHGGGMuyxGYVp+sLE54p9XHvULOe7nLvR3uGB2MXVMcB3UeB2Gjxgl+9+klwzVCinBBOzimaUaYJEuS5+XJa9rPlViitsY5oqRlhrQzww/xd8uuM/XV32KSVi5oJ69yFz4eoHyfbi7SjiK0/8TbaWkN/7hjcWXnHUVa28LXI+fsbNAYY0xtDJHgKAX8QIlHrZEFVVwRhqhdh+2tkiRQxXEEmGNeAxLIohbSWQ1wZRxPStxNjC6JWj1W8YKGJWrrQM6dIscUfRxnV9Vl1bJAhaOf38JFwk6SMzTxFDvJk0CAAkn2v/Qwnqt0d/t09/gIiucFDN9eoq017LiZ8CCVquVvZowx5laTEZedpCgSUIj6ocVFiNXhzg5OdVJGwCRKFzHyGnCKIgFKCochkqRvUnyWqBkcUYa8g1DV+fPNLG66LXzmP1MkzjHdyhidABSJ849sroz8KRKj99H3kUoFdHT6bB4s4BDgOsrQpjxtrT5CONWJzRphjDFmJTFxVpgFrvbaooEhvipHyXNRfbZIkna5uamUJWrmqsSdInGK7ODFy66jCuOPP06BJKfYzD/SC4CPx9d/4RcpFCRaT2lvLdKcDVAgnSixc3iGbKqEENDcFNDVXlymN4AxxhhTPyKCCwyTYpwSo5Q4FRTolzitNylhs0TNrBoRaHMvANDD6cUL//Rzlae+OpwKBpkjHKI6QxN/zVb86Nxpmibaf+ZhHCe8yN+cKzG4YQ4RxUHZOjDDxt4CaMDMrDXNGWOMqb1W8WjFYwYf5yY2LViiZmrOlYAB98iist38w6LXxb/4eGWEzbmgj7P0A+Gw639giBly0ZoORx57K9lUCYBEwmdT7yypeAlUyWZKbGifs+lJjDHG3BSZJaNTV5slaqYhxaRYed7vHqGfhcTu/iUTBs987PFKP7k50nyDgWhkK0zTTPcvvz+8PYkqiXhAb9scoDgS0NcxR2d7HoIATwLam+avOPzbGGOMqRVL1Myal3FmyBDe67KV82xYel+8/7FwL7zpIMcYHdErh71sqNzKpITHxg/9U2KuX1lfUHraZmnJ5QGIuz5DPZNkEmEiGfMCYjf3ZMoYY8w6ZomaWVeyzhRZpiqvBzi0aLn/P79IUDWruK8O59jIccJ7tRaI83/po0R4374iMe76pbfhOQu3FUl6RTb3TBD3AlClOVOgr2MGJ7rvHIT9+azhzhhjzJXcUKImIr8D/ChQAA4BH1TViWjZY8DPAz7wS6r65aj8IeAPABf4hKp+7EZiMGY1uRIsmtU6JjC4JJmDpyvPVGHmD7+w6JYlc2T5Fv2U8BCUKVro/Rc/Hq5czucUulqmcV2FANqycwx0TOI4AeqHKzWlC7Rm5m/Wr2qwOswY0/hutEXtSeAxVS2JyG8DjwG/KSI7gEeAncAG4Ksiclu0zR8BDwIngadF5AlVvfyNu4xpYCKQlelFZU1M0r101Osnv7ToZVFjnNdONErwTtDJ1+letM4sOYY+8DYcUVSVXCpPT+tMJdlTDejIzdHVPE0QWPvcdbI6zBjT0G4oUVPVr1S9fAr4yej5w8DnVDUPHBGRg8B90bKDqnoYQEQ+F61rlZxZV2JSpEcWkrleTi67nv+Zv6mMfp3QNl6P7h5R9n26GKeDNIf4nlzg3o88SDpRAML56gCak3PkUmEfO4KwLJsskPSKrHdWhxljGt1q9lH7OeAvo+d9hJVe2cmoDFjU0/skcP8qxmDMLcWVhYENHTJCByNL1jiw6NX0x57nwpJ74b1KE/OkF5XNkGPXr74TESXwlWyyQDoeJnhUErxZssnFCV4qVsKp6o93i7E6zBjTcK6YqInIV4GeZRZ9VFW/EK3zUaAEfHa1AhORR4FHATptzIMxV8WTEh6lRWXpaETsUqX//kLl+RnNUKhK8BRhmmbypColIAz/4jtxJKBy/TVQMokCzalwQuJyQpeOF2hJzVG5M3EgOKJIHa7QWh1mjFnLrlh7qOqPrLRcRH4WeC/wdi1fa4FTEM1QGtoYlbFC+dL9Pg48DrBNkrfsKbwxjSAtM5ckdK2cv2Q9/+P7KOribGucHK+RZWGkhDBLhtnKpMTRtrjc/+F7wj53Uf6WiJVoS8/gsNByGHdLNCVmb/h3KrM6zBizlt3oqM+HgN8AflBVq2vWJ4A/F5HfI+yIuw34PuGMBNtEZDNh5fYI8M9uJAZjTG05sjjnyDJJlskrbqcKFz7+MmE1EL5HnhQv01zphwdQIMEP/fqu1Qz5sqwOM8Y0uhttj/84kACelPCaxlOq+i9VdZ+I/BVhB9sS8CFV9QFE5MPAlwmHtn9KVffdYAzGmDVABJIsnm4kyTzNjF+y7rH/9kqtwrI6zBjT0GShpb9xbZOk/r43WO8wjDE19N7Sa8+q6p56x7EarA4zZn1ZzfrLblVtjDHGGNOgLFEzxhhjjGlQlqgZY4wxxjSoNdFHTURGgWN12n0HMFanfV+OxXR1LKar06gxZVS1s96BrIY61mGN+tlaTFfWiDFBY8bViDENq2ruyqtd2ZqYhbGelbWIPNNoHZotpqtjMV2dBo5pU73jWC31qsMa+LO1mK6gEWOCxoyrUWNarfeyS5/GGGOMMQ3KEjVjjDHGmAZlidqVPV7vAJZhMV0di+nqWEy3rkY8jhbT1WnEmKAx47qlY1oTgwmMMcYYY9Yja1EzxhhjjGlQlqhdhog8JCKvishBEflIDffbLyLfEJH9IrJPRH45Kv/3InJKRF6IHu+u2uaxKM5XReSdNymuoyLycrTvZ6KyNhF5UkRej362RuUiIn8YxfSSiOy+CfEMVx2LF0RkUkR+pR7HSUQ+JSIjIrK3quyaj42IfCBa/3UR+cBNiOl3ROSVaL9/KyItUfkmEZmrOmZ/UrXNG6LP/WAUtyyzuxuJ6Zo/r3p9N9caq8MuicvqsOXjsPrr+mOqTf2lqvZY8iC82fIhYAiIAy8CO2q0715gd/Q8B7wG7AD+PfBry6y/I4ovAWyO4nZvQlxHgY4lZf8V+Ej0/CPAb0fP3w38P0CAB4Dv1eDzOgsM1uM4AW8FdgN7r/fYAG3A4ehna/S8dZVjegfgRc9/uyqmTdXrLXmf70dxShT3u1Y5pmv6vOr53VxLD6vDlo3L6rDl92311/XHVJP6y1rUlncfcFBVD6tqAfgc8HAtdqyqZ1T1uej5FHAA6Fthk4eBz6lqXlWPAAcJ46+Fh4FPR88/DbyvqvwzGnoKaBGR3psYx9uBQ6q60oSiN+04qeq3gAvL7O9ajs07gSdV9YKqjgNPAg+tZkyq+hVVLUUvnwI2rvQeUVxNqvqUhrXPZ6p+j1WJaQWX+7zq9t1cY6wOuzrrvg6z+uv6Y1rBqtZflqgtrw84UfX6JCtXNDeFiGwC7gG+FxV9OGr2/VS5KZraxarAV0TkWRF5NCrrVtUz0fOzQHeNYyp7BPiLqtf1PE5l13psah3fzxGeYZZtFpHnReSbIvKWqlhP1iCma/m8GuK7uQY0xHGyOuyqNVodZvXX1bvp9Zclag1KRLLA54FfUdVJ4I+BLcDdwBngd2sc0ptVdTfwLuBDIvLW6oXRGUvNhxCLSBz4MeD/REX1Pk6XqNexuRwR+ShQAj4bFZ0BBlT1HuBXgT8XkaYahdNwn5dZHVaHXZ1Gr8Os/lpRTT4rS9SWdwror3q9MSqrCRGJEVZwn1XVvwFQ1XOq6qtqAPwvFpq8axKrqp6Kfo4Afxvt/1z5ckD0c6SWMUXeBTynquei+Op6nKpc67GpSXwi8rPAe4GfiSpgoub589HzZwn7UNwW7b/68sKqx3Qdn1ddv5triNVhS1gddk2s/roKtaq/LFFb3tPANhHZHJ3tPAI8UYsdR6NSPgkcUNXfqyqv7h/x40B55MkTwCMikhCRzcA2wg6UqxlTRkRy5eeEnTr3Rvsuj+75APCFqpjeH40QegC4WNWMvtp+mqpLBvU8Tktc67H5MvAOEWmNms/fEZWtGhF5CPgN4MdUdbaqvFNE3Oj5EOGxORzFNSkiD0R/l++v+j1WK6Zr/bzq9t1cY6wOWxyT1WHXxuqvq4upNvWX3sSRLGv5QTi65TXC7PyjNdzvmwmbmV8CXoge7wb+N/ByVP4E0Fu1zUejOF/lBka1rBDTEOHolBeBfeXjAbQDXwNeB74KtEXlAvxRFNPLwJ6bdKwywHmguaqs5seJsJI9AxQJ+xz8/PUcG8J+FwejxwdvQkwHCftHlP+u/iRa9yeiz/UF4DngR6veZw9h5XMI+DjRJNmrGNM1f171+m6utYfVYYtisjrs8jFY/XX9MdWk/rI7ExhjjDHGNCi79GmMMcYY06AsUTPGGGOMaVCWqBljjDHGNChL1IwxxhhjGpQlasYYY4wxDcoSNWOMMcaYBmWJmjHGGGNMg7JEzRhjjDGmQf1/ZfoFWLu2cG0AAAAASUVORK5CYII=", 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", 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BdJizWlb98E/FKSEIIB1YdCjUouEEFv+s/s3ScYJIhh+zxPizuG5Ulgg8lgxFUCwa584a/7J3AAsaB/gHDQP/oxWz01Cpd5A3Hw+T+A5jAbWwETSciQluifvz+tmbtfv26rKDQ1d3NDavfifxfXjiKR7XrU6tMS8oxNu5R4Gstp056TT09DkDu0K62ml4jVlDwkgMx0i8ZOtHC+rbtbszMmeuNIi+cHr8WkuGnDkv8fn3Hdc5fjxF184SnV3e8LzPLVKYmPt3Tf59G446NISleghttl6T/zdS8FqByqUCkxPTpAInl28nndafcSHtbeG8LuUFLwfS7JIic76JHT3VFl23zeufMI2hLySfK7Kut8DLbonDykIXvW32bW3Obc1bv9DtLTQOIfmGXv3mulB7wnpXPsBsKcWxMx088Vu/xMRM7sJd7s3eWBP30zwXJu97fgVPfmgu9MaduGFvtg8c/O/+I5lKwDe/8CUefTBLrq2XIIheM0E8ut7dasvJchwsnaqHCYvKU9XtgFQqIJOr0NFdIVWrGx1ar1Si/ZkKUtGHbZxowjDarvo0ssSnmwVBlEyoh5dUqt6GoHpGpYNV00tiOUhZrW4qMIa2JHp3aoHGSKWgYw1su74xuEWPr7680IkE1TrJsuSHdCpIhhvmtT/Z+RPE6516IKsGpJkZGD8H23bPb09yjrSG8DantypqQ305tWD5/MeRDKQN5cyvMz+k+Pz7aFLWeEml5OOY/141ek5nc6x0+sReQUJCirPjPHfwG2RzM3R1dVEqFVnT3cvOXdcpgElTtQ+D6odD8n17XqCpF3SkyuwaHmPXxvPxqkSwaRKU5teZfxhh4bpNgtdl3l9DoKzfCB/6ygyjk3lKqaO0d6ZJp5+gq28nQSpHOlt/zaQSpzWmM8nyxHKcNpLrsRTnT2fpGSyRziQ+YNP1HZ1Ox5OUNpTV000qMc9EOpMMgFF58u6CRN1kQKoup9P19UcPwOCGKKwECwSrqzmmsdbb1iS8zQ1pzZarPVfjV6+JIktO0XkFKVGivaOdHbt/gJtuewlt7R3ccfcr6O7p4+knH2t180SWrSBwLID2zu3kO4aZHHuamaljrW6WiKxCCl4ripPNRl9VO7u6mZqKBnmvWz/M9NTEhTYUWbXKlQpQxIIK5eIY7mXaOkaolKYYPfVwq5snIquMjk2tIAFGsTDD9NQ5Dh44QVdXNwCui8qKLKhQKlEqO3iZ2ZljVMIyqfiwfCqda3HrRGS1UfBaQbKkcZyzpw/yous7Gdm2A4BKpcKuF93Y4taJLE8d+TyQoZIeoLN7A9l8sTaeKznGS0RkKehdZwUpE9KRa2dkyx1s2hrWznhJp9Pk1vS0tnEiy9RssQhkyLfvAc63uDUistppjNcKchVPPhK5ZqUCvc2JyPKhd6QVJI0uLCdyuTLpNJWwgofFpuubzv8lInKV6FDjCnTgyS8zsHYN23fuJpfLt7o5IsuauzM1O0Uw8yjjo+NksiXS2Xay+X7aU+sYP7ePnsFbWt1MEVkl1OO1gjjG7GxIGIZsHNnKU4/v5fjR51rdLJFlbbpYoBQ6a/qG6Vyzh66e6ygVx6iUppgae0ZnBYvIklKP1wriGLPTkwB0relh956b+cr9n+Nfv/yPrN84THt7J3e+5BUtbqXI8lIJQ9JBDqyTIFXCrIJhtK/ZwsToXizQIXwRWTot6/Eys9eY2ZNmdsDM3t2qdqwkRUpUwhnSqTJPP76X5555kmKpyO49t2DUL2ciInXV6yBOTzzK7PQRJsaeJNe+nrPHv8b0xCFybQMtbqGIrCYt6fGy6EqzHwBeDRwFHjSz+9x9fyvas1IEBKQzKVIZo3dgLWG5QNeaHjZu2krKtnD4uQOtbqLIstORzTE1Y5SLp5gcmybfnse9QDq7hp6hW8greInIEmrVoca7gAPufhDAzD4B3AsoeF1AmhTpfCeVcpnpqQlGz55k956b6R9cR8qcw4eeaXUTRZadIAgwM7oH7iHXNkk2XySTawM0gaqILL1WHWrcCBxJ/H40Lqsxs3eY2UNm9tAYlSVt3HKWzbaxdcedpFJptu+4jqENI7V1t97x0ha2TGT5CoIU5dIYQZAmCBrDVlgpUqkUWtQyEVltlu1Zje7+QXe/w93v6Nb8VTWl4ixdawbZOLKVNT19rW6OyIqQDtJMTz7C1PiT885iDMMik6NPtahlIrLatCp4HQNGEr8Px2VyMZq+XuSyzZamCVKQzgZMnn8c96gXvVQYo1QYI1xgclURkcXWquD1ILDTzLaaWRZ4E3Bfi9qyomQymjBV5HKFHjI1dox8ezeZXC/jZx+lUimQznYxM3kcfaMRkaXSkpGl7l42s3cB9wMp4CPuvq8VbVmJCrOTHD10glJpBsNoa+9gaGg96bQGCos0YxjZ/Hamx58l3zFMW+cI42cfJde2hqmxAwxs1Px3IrI0WjbGy90/7+673H27u/9eq9qx0kxPzfDsgf3RN/iJcRynWCzwyHe/ydjouVY3T2TZyrdfR1ffDRRmTlIqjtHZswuCFB09O2nr3NDq5onICjM9O0slvPyT/9RFssKUigU2brqTTVscNm9l396HuPGWu9i4cZj9e7/DrXe+rNVNFFl2zAwPZwmCHF29N1AqnmJ64hDZXBvd/Te0unkisgI9/PgBYB0AhWKJbz6yHy7hEmQKXiuMAe4hYFTKZSrlMgC5XJ7Qw5a2TWS5ymfa8XgGezOjrXOYts5hzeMlIlfkhh3TAOSyGTKZNHfu2Vm7WsZC9K6zwqTSeZ575hus37iGyfGzjGzeDkCpWCSbybW4dSLLVya7Hjjb6maIyDXC3enunAWMYqkEQDaTueh2Cl4rTCaTZ+OmW5mafJj1GzezdiiadzaTzXLjrXe1uHUiy9/5M98hm++hY81W9BYoIi/U+sF+HnzsSQZ6unj+7Cib16+7pO2W7QSqsrB16zoolYrMzEzxxL6HKRRmW90kkWWvXMhQKkZBq61jI5Pnn46nkhARuXzbRzawY9NG0qkUe7ZtZmRo8JK2U/BagYa3OGbG9p17GNm8naefeJRjR55rdbNElrXQAzyM3vLS2S7W9N8AZoydeZRScaLFrRORlebwiVMM9HazdeMQfd1dl7ydgtcKU6mUGB87i4chY+fPUS6XGdm8ncmJMR5+8F9b3TyRZasw+zjTE0/hYZnJ808xef4pysUJzALGzjzS6uaJXLLnnj3A2bNnWt2MVe8iY+gXpAEOK0y5NMvJ489RqZQ5dvjZ2oTbKYNcvq21jRNZxlKpATLZgGLxNJl8H4aRykRvgbn2oRa3TuTSBAEcP3WCbL7MyRPH2blzJ7mcrmjSCiNDa1/QdgpeK0wu38Wu627n1MlHeNGeG2vlKbv43CEiq1k6M0gmZ2Ty28nlB6IyTSchK8zuF5U4dw527drN1MQ4j+/fT3//AJs2jVx8Y1l0k9MznDpzlkKxhJnRkc+xYbD/gtvoUOMKUypGc4bsuu7mFrdEZGXK5vpa3QSRF2R0dJTR0VHCMGR0dJRSuczmLVuZmBjnwW8/0OrmrToHj57gvi+fIXRnbHIKd2e2WOSbe/dfcDt93VthgkB/MhGR1ejokUMYUKlUOHL4UMMYo1xehxuX2rHnT7Np/SvYueks24fX8+C+p3jxDbvZdJFDkPoUX2FS6WyrmyAiIi1w4023YDj79z3GnutvwKgPMUkuy9Iws9oVY8qVCuVKdN3GfO7Cn9MKXitMGFb4xle/w9D6adZv2MDgug0cee4ARsjI5u3kNcBeROSa9OzBZ9i2bRt7rtf1RZeD4aFB/tfX9tLXVWZ0fILtIxsAarPYL0RjvFaYYnESr2zg+pvvBGDfIw+Sy7fR2bmGpx9/tMWtExGRq6VrzZpWN0EStmwYYuem3Qz2dnP7nl1sXBudtHOxywapx2sFSVOmWIC9D2/m9KmQm27dwcT4c7zo+m2kzDl86Eirmyiy7KSCkEoYMH52DefPrMHMgQDHCILou2eurUjf+il6186QSoe4B3h85CYVBlSP4qTi+uVyvcyCgFDXp5clMDAwCDqkuKy05dsZGri8E3YUvFaQPVbhzNmD3PO3P8yz3MGXfuI3OXY0xVe+ME0qyDB2Pst3vpXlxltKbN0WYgaBGV3d0NPrL3iyN5GV7Patg+Dwm6/7a5451YtjPPzsEL0dM5g7oRt7jwzxiX03cPRJwMCC6MVi8X/Vl47FwQuzRPAygpSzprfI0JYKfeujwwypdLyVG+l42VJWD3Sp+gsyFRipNLR36UNVFjY1NcX+x56iUplheHgDQ+vW8exzz+LubN2yhbwG2K8ICl4ryDtSaxnzCs/60wxygLWf/gRD3s8hCpRw1tLL6a/dwje5mX+itzbYskyG9W99LbuvL9PTE5IiJJWGrTsqrFsbTcgncq36gze/Nl6aYqh3KloM4eW7D1FLQTzC/1X8Is+c6mO2mMbDqNxszuzUYbV+NIiWeP68QjnDgwdHePjQBiaezmHmlOOaBhSaNSy+7+rNhxhr8rMcHb6B/vVhfP/R2lRQb4RZfRh1KoiW3aNJlNu6YMMmyLVf8u6RFeTJJ/ZTKGxmz541TE+f5Hvfe5ihofWk0ike37+PW2+7vdVNlEug4LXCdFuKW6yDkjsZM2CG+omr59jBP/MS/pmK19PUpHfzzP+4jye4mQLR4PsKGcbpYeDNr2F4U4V0yjGc7p6QG28qs2lTyEUOU4tcU/LZCtcPnwbAFzp2GAcv9/nrX7ztCGEZxmZyhD6ne9lDgnlFjb1bk4Us3z08zDcOTHPmcCfVLjUzx70e0KIyovVxucULo8UMD+RuoqcfOnuhXIKp8ThABvWcmQrqGTKVAg+pta9/HQxtAk2GvvxUKhUGBjaQy5XpH9jM0SOH2bR5MwAnjh1tcevkUil4rVCZixw3TFn9g6HbRrkt+Ba38a2GOlNhB4c//ikO+U5mMAznMIP8MxvoedNrGR4p86LdJcJQvWIilyIInN6O2fkrmgS1ucGrt2OGkb4x7r3pUcLQ4rFocV2ffwjSDOYOLpuYzfH48S/zzWe3cf75dsycYDQKZY1vGdF2jS/rKOA9P9HN19a8nO4+yObi7TwKbQ3hMd44iNfXilMwvA227YKefhoTo9c6CRvvOWhcNosCocw3MzMF5BgbO0+lUmFsbIx0Wh/lK4n+WqtYRzDFdTzCdTReIHg2zHHsk3/NAW7gO//+3Xz3OzlyOccICQLYvKXMzTfOsnZdOfom7ZBOM+8bvYi8MEHwwsZ6deUL3LnlMHduOXzhihcIgqVKwJHRL/LYiY0Ui+laXTNvfI2bR5mqGiDjdYVSmv33b+Qz33gN6RRYKl5X7Z2L61lAPbAlwlsqiNZt3AK7boDOrihfxlMkRb12cd3q5u6NXw4Dg1SaxtB3DdixYxf/9I/fYey8095h3HLrrTz11JMUCwV2797d6ubJJVLwknnyQYHtPMV2nqLywc9y3vspEU0IV/Acz/EiDrzr/6BYtDh4OUEKtm4qcOONs/R0hw2T+QVEb6Z9fRVS19gboci1JpMK2TZwlm0DZ4ELHHatCueHRPeHODd1P8fHewkT3VlBoifew/kn/BhgXmF0pp2HH97Cl575CSplyLfBN79crwPVAFe/gSCwhp63bBZGtobsvM7p6KwHvup4uGonoiWWg3i9Eb23dXU7bcvokGtvXx833/L9DA1N09uXwXBuvz2aWkgTqK4cCl5yQSkL6bfTDWWbOYj/v5+nQBvxd14mfQ3P+HV8lJ0Uyc27nZAURshd//kN7NxWIAgg8JDungojQwWNJxO5hphBX8c0fR3TC9aZe6i1Jg56L91ygLeVv8JUIdfQm7XgdtX7xglD5+x0J3v/dRP7/m6IcphK9K4lZ3uPb7O2Kmw42cHdsB+4hx27Q3L5xnDTkBm9ehZs8vAwBClnw8aQvn5f1OEamUwWFLRWLAUveUHMIM9M7fc2m2aQk9zNl5vWr3jAOV/Lwd/Zy98zDIATMEUXA7/wRtYPFeldU4lKU86OrUW2j0zTltebi8hqlU+XyafLDWUXC15RnZDetil29D+PO4RuC4+TS0r07rnD2elOnjq4jycf3Ei5kmqon+yxm3sz0QkRTjlM872ZdkovvYe1QyFBClLx+Dr3xEm18YkSteV4vRl0tIfs3FVm48Yymi3i2qDgJUsiZSGDdpJBTjaUu8P5D32cw76TY3QAUCLD19lM79vezJquEqlU9E2yrS3kxuum2L1lmo72Sm28iAb+i8hCzCAVB6GLV07UMVjbOcHazid5+ZYn51W9WACsrp8uZTl87gEOPTsY9YzF95HsXau3tTZqLVoOnfOFTr76sbVM3vFa2tpC3GHr1hTZbHQmuid626pTizT0zBmsWVNhy6Yi/f2h3i+XgSsKXmb2E8B/Aq4D7nL3hxLr3gO8jWjCm19x9/vj8tcAfwykgA+5+/uupA2ysplBr52jlwfmrZv8yMc4w3rC+PSpMe/h07yIzrf9LEFQfdOBnu4yN+2aZPf2abJZwJ1UyunqqCzxoxERadSeKfKiwWO8aPBYQ/lFx85RD2+V0Dg19S+cP9cZ9bKdaKwXvRfGJ0HAvFNHz0x18y93/xLlEmSyjlVPrvDGU1KNRJtqxSE49PZW2Lm1wNC6EkFqTkatTp/SpExHLea70h6vx4B/C/y3ZKGZ7QHeBFwPbAC+aGa74tUfAF4NHAUeNLP73H3/FbZDrkGdwSSdPN1QdisPMPvfP8WsR33ujnHCR3j6Pf+Zrz/UHY3JwAkdOtpC9mwfZ+twNH1lWKm+2UTfJocGivR1lzSjv4gsa6nAWd91nvVd5xesc6Egt6vvOC899stMFvOUKvEEwc3m9UiwRA+hY5yc7OPx4q/wjW9HRyaCZGDzqLet4b00DneZtLN5Q4HNIwUy6bn3WT091esnO3hj+AtSTl93mew1dHzuih6Kuz8O9dmVE+4FPuHuBeBZMzsA3BWvO+DuB+PtPhHXVfCSS5a3GfJWH1/Wyzn4L/+G3Z6q9Y6VyXAiHOYBrud++hNd79HPkBSztHHdO1/N9k1T5LLRgIv2tjI7N88w1DutLnkRuaZ0ZqM55i5lnBxzDs325ie57qn/EI+XS15JwRcMfe7GudlOnntwPV+fGIwOhYbV7ebXn39mplMK05Qqadb/2A+wfqgQzysXHWI1DxvnFGly/7jT0V5m47oCPZ3L46KqVytDboSG2TqPxmUAR+aUv7jZDZjZO4B3AAxqKJpcgpRVSMWXcslQYlvqKbbx1IL1Z72N4x/4Rx5lB+X4OTZLB+cYYPvbX01fd7H25tDdWWKwdzYKaCIiq1Rg83vLfKHeM3MG28cZbB/nzvXROLlLCn1zTJXyHP/GPp6f7q/fdMPZqY2X36pd8iueD+9QoZ2vzfbQ/eofoLM9Oomr3nifl9m84SFGYa2/p8S2jZOs7S826bm7PBdNNGb2RWCoyar3uvvnrujeL8DdPwh8EGCn6SCxLL68zTQNZ0XPcurD9zFG/YrzT7OWid98O50dZT775aE5YxlC8rkKO0emGF47RS67PL5ViYhcCzoys+zoPc6O3uOXVL9ZuCuHAecef4ipcjRMZaaUY9/ZbWRTZSyeDDi5Xa33LU5zBwtrePC2V5LPVchk6vU68yW+HfQ09NXdft3oBdt30eDl7j90sTpNHANGEr8Px2VcoFxkWchakWE7xDCHGsrL7/8S572fWeqTjlVfnOfo5CE2s+43fp5sOjrj0h16OkvsHBln89AUmbTjYTTw/0q/MYmIyKVLByFrO87Xfh8vdGABXNdXf5+/UG9cJQw4d+6bHJ1cS6nSGJ1OPVC9ukP8+8ATF27LZbb9Ut0HfMzM3k80uH4n8G2i7LjTzLYSBa43AW++Sm0QWVRpKzNgzy+4fhtPUv7Df26YQHbU+/k7tjPKQDz+zIGAdiZ5wwfuYXjddG32f6A2AGJNR4l8Rj1nIiLLQSoIGWw7z2Db+XnrLuUM1aQrnU7ix4A/BQaBfzCz77n7D7v7PjP7FNGg+TLwTnevxNu8C7ifaDqJj7j7vitpg8hykrYyaeoTPrbbFBtpvG5e2dOc80G+8s5RztM/9yZwjAJ57nr3D7Fl/SS5TIi5k8tWGF47TWeueNUfh4iIXB1XelbjZ4DPLLDu94Dfa1L+eeDzV3K/IitZ2sqstROsnTsZT0LZ05x731f5OpsISQFQoI3z9HPzb9zD2p7ZuGaip8xgY/8Um9dN0dVeusqPQkREXgidLiiyDC0UzioeMPb+L3GkYYqMSEiK7zDEunf9OzLpCumU1067BsBD2nIVdm44x5Z1k7TnyrXclkppzJmIyFJQ8BJZQVIW0mdn6eNs0/XbeILKB77KFGtqFzBPmvQ1HHrvr/HgU+tqc9wAdLSV2LFulM3rJkgF88+tbsuWadPZmiIiV0zBS+Qak7KQNZxvuq7bRuH/+XVG3KjEL/+QgDHv4yts5Sxrm2zllMly+6+8nO3rx+jpnIXQGyZRTKdChnqnac9q/JmIrGzTpTLtmasXjxS8RFahwJyA+jiwi405K3mG0T/5Gv/CVgq0zV9Plim6uP2XX8r63qmo0EN6O2bZPDhGd7sCmYgsH6GXyQblpuseOzvGXUPzT3xaLApeInJRGSux1k6ylpML1il5hnN/+nX2x2dqOsZ5+hl6+4+RSTn5zNw3uZB0KmTbuvNs7h+ju72g62aKyJLYe+YIffnnOT2TZyCfa3bpw6tGwUtEFkXGSqyzE6ybe0LAh7/KOL21Q5tJ497G59nC0NvunT8iLR5/1tVeYMe6swz3NRt/FpJLl8lnK4v4SETkWvfSDQOcnilwdGKGx8+OM9CWY21bjv587uIbXyEFLxG5qlIW0rvAyQAYDPMclY98tekhTMc47/0c+/Wf53vPDjWMK4uEgNGZL7B97Rn6Omfn3IATmNPXOUNbtvlhBRFZfdJBwPqONtZ3tFGshJyanuW5sSn2nx0nbHLB7UW976t66yIilyBlIe1MNV3XYZPwx7/L8ALbznqec76W+9nCDB3z1ocEFMhz5y/eysbeMcxgdDrHxEyW6nwa7dnS/N40EVkVsqmA4a52Nna0MVuucHqmcFXvT8FLRFa0vM2ywQ6zYc4VApJmPc+5//Yg347P2gwI+aTN1NbP0MFLfvv7GeiaIpuuzLsESDoV0t85TQqFM5FrwdGJaYa72ueV59MpRpqULyYFLxG55l1KODv9+4/xDH1U4isFQP0i6AXyTNDD3f/h5XTmC7hHhzhr9czpbZ+hv31SPWciK0ArT+RR8BIRIQpneY5fsM6Z/7qPY+TnlYcETNDD1l++l3SqQmDzJ6HtyBYZ7Jqkv3NK4UykxTZ2Xt1erQtR8BIRuUR5myHPTNN1vZyl8mfvp+DpplcNeJ4Ogne/kadPDUTrw8bw5W70dkyztmOcNW0zc9ZFdXNpnb0pshgeOzPBho5e+toaT7oZKxSZKVcY6ph/ss9iUfASEVlEaWt+9mSG85z6/Q8uuF3oxkF6+C79zDL/27hjlMjyit+4mYHOSVJBCHPGouXSZTqzs/O2FZFGRyacR8+GvH5ria5splbekUnzxOi4gpeIyLUuMKebUboZXbBO6Maz7z/AXnqa9qoVaGPHL7yS3vZp2jKleeuDIKS/fYquXPNeO5HVIp9Ksb17kEfPPMZ1fd305rNANM3E1abgJSKyQlxSOPv/9nOIbkpk562rkGKcPna8/RV05gqQPEtzztxFBvS0TzHQPt40xImsdF3ZPFvW9PLI6fPRnF7tecaLJVJ2dcOXgpeIyDUkMF/wIukAazlB+KH9nG4yYS3Uz+QMCXiaXkZ+4YcpljPY3BMGgFy6xNr2cQY6J8ilGg+xuhtmrstAybJUfV62pdPcvq6PA+cneODkWXKpgOv6uq/qfSt4iYisMoE5bUxftF47U5Q+9OEmBzUjY97GwK++nn0nN1KupBrWOSGVMCCfKbG2c5w1cw5veugNh0tTFtKZm13wvkQW021r13IsnrM5E0Rhy3uW5mxjBS8REXlB8jbDsT/5nwuuD4iux3mY/qZXFYB6D1uFFFPexYvfvpPetqnalBzJI6CduQLd2Un1osmKpuAlIiJXTd5mGOLopVU2OPPh/RxiTdPVk3Sx6adfRme2QCaYf/ZotQfNw5C2TJHBjnG6cjrLU5YXBS8REVk2OmySDiabrlsL8LEnOecdhKSa1olOGDBmaWPiLT/IVDFPEITzTh4ILKS/fZLBjjGFM1lSCl4iIrKitFvzC6ondTJO4a8+TsppOvVGiTRP0sfEz/4AU8V8dPJA2HyMjxOdSDDYPk5f2yRmId44hRr5dFFXJJBLouAlIiLXLLP6OLKkgBIDPE/xrz9Bpsl2c017nkcYYHKBw6CztHPTT+2kv32cXKpUu9pAlXs9/OXTRbqzExqrtkopeImIiFxE3mYvOlZt+uOPcZI+ygt8tFYD4CztbPi3d9CRLdCWLsyvZ05f2wQ9CmfXpCsKXmb2B8CPAEXgGeDn3P18vO49wNuACvAr7n5/XP4a4I+BFPAhd3/flbRBRERkObiUC63XfOZJxr2Ds00nuk3zCH2s/7E7yKZKtcDW7JCp4fTkp+jPj86bskOWpyvt8foC8B53L5vZ7wPvAX7bzPYAbwKuBzYAXzSzXfE2HwBeDRwFHjSz+9x9/xW2Q0REZEWJxqo1H6/Wx2n47JOU/MIHQh3jGXqY/ImXMVnMX7CHzB2yqTIDbaP058ej633OkQnKGqt2lV1R8HL3f0r8+i3gx+Ple4FPuHsBeNbMDgB3xesOuPtBADP7RFxXwUtERGSOjF38ck0DnKLyN59Z4FoEjQqew376+zgwupGwSQ9asZIhE1QYaDtPe7rxbM+5PW7poEJ3bkqT3l6mxRzj9fPAJ+PljURBrOpoXAZwZE75i5vdmJm9A3gHwKCGoomIiFyxnBU497EvAtEEt3PlicLZk/RTID9vffJEhRJZJlnDDT+6gTXZ+VdCSPabBRbSm5+gKz216setXTTRmNkXgaEmq97r7p+L67wXKAMfXayGufsHgQ8C7LS8+j1FRESWQM4KrL3UsWrA9Gc7GKW99nuzs0grpBijj3Wvu5H2dGHetT+TZ32aOT25yajXLTP/5IOV7qLBy91/6ELrzeytwOuBV3n9/NljwEii2nBcxgXKRUREZIVptynaFxirljTA8/jnH79oT5oT8DQ9jL7hLmbKWQzwuROn1eoabekCA23n43FrFXyB+dgA0lZpeY/blZ7V+Brgt4BXuHuyn/E+4GNm9n6iwfU7gW8DBuw0s61EgetNwJuvpA0iIiKyMphBnotfKSDPDPzd5xa4wmejGW+n8uN38fi5zYQeNJ0I18xxN4phmq7MNNlUuaGXbSld6eCpPwNywBcsipDfcvd/7+77zOxTRIPmy8A73b0CYGbvAu4nmk7iI+6+7wrbICIiIqtUm00z/umvXNJEuFmHkZ+6nTOz3fTmx69625qxubPrLkc7Le9/lN7c6maIiIiILOi6n94JwPa//IfvuPsdzerodEERERGRRfD4R5++aJ1mZ5OKiIiIyFWg4CUiIiKyRBS8RERERJaIgpeIiIjIElHwEhEREVkiCl4iIiIiS0TBS0RERGSJKHiJiIiILBEFLxEREZElouAlIiIiskQUvERERESWyIq4SLaZnQYOAQPAmRY3p9W0D7QPqrQftA9A+6BK+0H7AJbPPtjs7oPNVqyI4FVlZg8tdLXv1UL7QPugSvtB+wC0D6q0H7QPYGXsAx1qFBEREVkiCl4iIiIiS2SlBa8PtroBy4D2gfZBlfaD9gFoH1RpP2gfwArYBytqjJeIiIjISrbSerxEREREViwFLxEREZElsiyDl5n9gZk9YWZ7zewzZtaTWPceMztgZk+a2Q8nyl8Tlx0ws3e3pOFX2Wp4jABmNmJmXzaz/Wa2z8x+NS7vM7MvmNnT8c/euNzM7E/i/bLXzG5r7SNYPGaWMrOHzezv49+3mtkD8WP9pJll4/Jc/PuBeP2WljZ8kZhZj5n9Tfx+8LiZvWSVPg9+PX4tPGZmHzez/LX+XDCzj5jZKTN7LFF22X97M3tLXP9pM3tLKx7LlVhgP6yqz8hm+yCx7jfNzM1sIP59+T8X3H3Z/QPuAdLx8u8Dvx8v7wEeAXLAVuAZIBX/ewbYBmTjOnta/TgWeZ9c848x8VjXA7fFy13AU/Hf/r8A747L3514XrwO+F+AAXcDD7T6MSzivvgN4GPA38e/fwp4U7z8F8D/Fi//EvAX8fKbgE+2uu2L9Pj/Enh7vJwFelbb8wDYCDwLtCWeA2+91p8LwPcDtwGPJcou628P9AEH45+98XJvqx/bIuyHVfUZ2WwfxOUjwP3EE6yvlOfCsuzxcvd/cvdy/Ou3gOF4+V7gE+5ecPdngQPAXfG/A+5+0N2LwCfiuteS1fAYAXD3E+7+3Xh5Anic6MPnXqIPYuKfPxov3wv8lUe+BfSY2fqlbfXiM7Nh4N8AH4p/N+AHgb+Jq8zdB9V98zfAq+L6K5aZdRO94X4YwN2L7n6eVfY8iKWBNjNLA+3ACa7x54K7fxU4N6f4cv/2Pwx8wd3Pufso8AXgNVe98Yuo2X5YbZ+RCzwXAP4Q+C0geZbgsn8uLMvgNcfPE6VXiD58jyTWHY3LFiq/lqyGxzhPfJjkVuABYJ27n4hXnQTWxcvX6r75I6I3lTD+vR84n3jDTT7O2j6I14/F9VeyrcBp4L/Hh1s/ZGYdrLLngbsfA/4rcJgocI0B32F1PReqLvdvf00+J+ZYlZ+RZnYvcMzdH5mzatnvg5YFLzP7YjxeYe6/exN13guUgY+2qp3SOmbWCXwa+DV3H0+u86jv+JqdC8XMXg+ccvfvtLotLZQmOrzw5+5+KzBFdHip5lp/HgDE45juJQqiG4AOVlivzdWwGv72F7NaPyPNrB34HeA/trotL0S6VXfs7j90ofVm9lbg9cCr4hcYwDGiY7pVw3EZFyi/VlzosV9zzCxDFLo+6u5/Gxc/b2br3f1E3HV8Ki6/FvfNy4A3mNnrgDywBvhjom7zdNyTkXyc1X1wND4c1Q2cXfpmL6qjwFF3fyD+/W+Igtdqeh4A/BDwrLufBjCzvyV6fqym50LV5f7tjwGvnFP+lSVo51W3yj8jtxN9EXkkPoo+DHzXzO5iBTwXluWhRjN7DdEhlje4+3Ri1X3Am+KzdrYCO4FvAw8CO+OzfLJEA0rvW+p2X2Wr4TECtbFMHwYed/f3J1bdB1TPRHkL8LlE+c/GZ7PcDYwlDkesSO7+HncfdvctRH/rf3b3nwa+DPx4XG3uPqjumx+P66/o3gB3PwkcMbPdcdGrgP2soudB7DBwt5m1x6+N6n5YNc+FhMv9298P3GNmvXHP4T1x2Yq22j8j3f1Rd1/r7lvi98ijRCdknWQlPBdaMaL/Yv+IBgQeAb4X//uLxLr3Ep2d8STw2kT564jOfnsGeG+rH8NV2i/X/GOMH+fLiQ4h7E08B15HNE7lS8DTwBeBvri+AR+I98ujwB2tfgyLvD9eSf2sxm1Eb6QHgP8J5OLyfPz7gXj9tla3e5Ee+y3AQ/Fz4bNEZyOtuucB8H8CTwCPAX9NdNbaNf1cAD5ONKatRPTB+rYX8rcnGgN1IP73c61+XIu0H1bVZ2SzfTBn/XPUz2pc9s8FXTJIREREZIksy0ONIiIiItciBS8RERGRJaLgJSIiIrJEFLxEREREloiCl4iIiMgSUfASERERWSIKXiIiIiJL5P8HS3PIUlXQuR0AAAAASUVORK5CYII=", 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", 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", 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", 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", 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s5xhVGftjbKK4skweEGNI75mx8NDPkrMdlp5VjtXYQEnt0P0+xgz1sMQqM9QD40oJL8ZY8V4biPXoeDwW/poCyirA5c4eXly2Q7a45x5unzUzFucUmWkUlERERASAkPFy0JnFTrOCHazgJOUJe7Pe6h9nZ+m5sYj1ynRTgLvAS1W9j3POysdVkDOwP3ZcKAx5fkN9Q/oN3bbjJEckE5tNLfGGetuGvDyHunlh+kuKk453Zwk3bleW7VnKi8iZQ0FJRERkGokYN4ecet41K9jBSnoS7suxSBxmlvxBP9t9MomTOfeQRzuluPweqmd5WbigEGtgCNfgNM+WifV+pA0hMxY4mYeWGSwsy1C9IBdPXRFV9VBYRNp0z8fboKsdqhYnb3dlXnQnY8hxuwz9mYuLiIyJgpKIiMg4ixqbNqeW7WYFe1kycC9O5hv/h8LNSD0YhiguuggQpJioP5+iuiKKyxPeyk2s7ybpqMy5Kb4tMdiUeOGC84pxygoorTbkJkwq4Han1z5TgMl4v4wdOzYxGA23Jo6IyOlAQUlERM5IjrE44lSxyyynlXog2wKcaV0nSdtS9/eRy26WcZQawngprM7F7RrojUkLB7HwZMh898pgtLKMiffKBHL7qMqLMGtZAZXnVtBjh+I1cHvstEBk2cln9rgTpykYOG4g8Vg2sUkKcDIGo1OlUCQi05GCkoybD6JNtJpSwrjx4MZKeWcspJ0G6338ds8U1VBEZiJjDPucOlpMI1HykyZjTpQYD45SzXbOoZU6QvjIK/ZknRnNAhyT/DjhVBkOcCiq8HL2WUW4yt1EacfljhV2ue2BOseK2q6EYGPA9riSzj1YHgNOFDqOumlYFI0Hj04Twm0P1cnO0MNjp6yz7srwzp+pF0hE5Eynl0YZF1HjYhtLeIAfADDPuwms/IQSFk5/lALTQaOzk6W8QbV9MOM3tWNlE6XIOkG+lbronoicCfox/J15HoClOS9xMlJHxHiGmWvaYLlc5JfmsHClH28FHDu0NyGkWLg8roTS4EpIF5btSpp4LR5mANvlwnZBQVEUb+4J3J7klOL2JFfKTrnHxu3J/jgage4O8OYMBSIFHBGRiaOXWBlHQx8APvbV2UQLK8griD02Dnj7ezn46lH2vlvB1kPn43KiZApGiTcWD8dFmAAnqaaZpfYbLDJvjVzDUZzYwsFPl4aKiExDl3y2gPdCDez7IEheYeJsaANDy1xW7D4e00eor50e9yFMf5iCUnB7ht4SXZ7kt0e3Z+i+H5crOfwkhqrUoCMiItOXgpJMiOJSB1+jg3FF4uPhwU3B/GpmBW2sbkNfe+xDzOAXszZO2vIYJuH/GTOUXEJ9EGzpJrjvOHv3V7KrfTkuIolHJRkY7IKFk/XeAi/9lFltNFm7OM96jjwzMT1UNlFsLQQoMiFsGwoLDeW1YSxnBwAujye+3+0detvrbA9Mev1ERGT6UFCSSWXbUFQGlFkEZsX+/FJvGB7NzEpuN4TDORxrKeWs43207ewk4tgDkz0NfIucMO7fATAWNg7z5vQB4DhWfH+wy8XxD3ro2FfIq7uq2cJqUmeOGpQ8/e4gJ63M0O/JwcxDP3N4l4XW21TbB8ix+jJeR0RERESmjoKSTFseD1Q3Ao05NJ6bvnp86tj9ni5ofg8azkne7rJjN1YH28Fzoozml1o4cWIomSX2ZAEYJzkUxQNXrHD8n6QoZYb+6Tzaz65D1WxvO4fcaA+5dCX0hsVkDmOxM6THyEzDF9NDngW4CdNk7aTR2k2JdRSXFU0rJyIiIiIKSiJA7N6lQDFQXMLCOSVZFzFM25Zh5fZsq7y7BobbHX6nnb3PH+bg7gK6el2Eoy7CgHHMUFeUyT5dcEKtGQpJQ71jAMY46QcbCPWG2btvIfl0ETDHKaENL6GUs2bvSUs62ShlGuqYbXaxTOd2E6bIOkGj9Z4m7BAREZFJo6AkMkkGJ4coX1hE0ZwiPgLJ9yol/GrbTloWMYDbcjIVx42TPn/g4IxcduyYcBj2vtXL0Tc97Nnax8mTDYS8fckBKPEkWVJaao/a0PUGF5Q0GbePJDH2DfK5wuR7uwke3kPwZBGFVvuozjX8dU4t5E3MdUZ3/mxr++RYvVRwiFxLU+6LiIiMNwUlkUnm8cZ+IPOq9sNtd2fbPkwWcSX0ejln5VIxZw7zP2txcL/FuRdEcLvTP667LCcetJK2kzBUL8N+m/ShfPH1YhJCXuqxtkk/rr8rQtfWD3jtuShbu5axy1lO8mwf1sB50+sxXAAZTYCxEvvzhp14I8s+E4t9g1UbquJAj5oFOXYvHisybF2HKjRUH2MsLAwlnhMEIq10HgtQatqGfV7ZhnGeismeDXI0/736TRTYM/GVERGRM4qCksgZpLI69qEzEjEca3WRlwduT3o5V5Zw4B5htr5sx8X2Ze6JAnBlGu5XasOsJuZcDp88aWg/GtucGOAMYJvk+7uMk/5J3naisfvGko4dKucy4fi5k8oYayjomWhKeMz8Ed5yohnrkHjfWsdJKC8JM2dWb1qZtPNFk5+f3dXBgddbee1lN1u2zmabWZB18pCUGmTZPkaZKpl0/ZHqET9RUvnhz5ltOGjs6IjpB/4EQDhqEY1qbn8REfnwFJRE5LRm21BW5lBWlrUELieSbScALjP8fmDEc8TKhEcsk6l3LNWOHV68Houi0uE/0NvRDNcrC1DUGGDpX0Dn8QjtLT2xlUhTxGNKwtDHxGGQJjLMMWTJQwPDLp0sQTDzeayM9Us7JtMQzchQW6YvTT0UYntDHn59d+z3aGUNkaCNx+sQ0YSSIiLyISgoiYhMQ5YFhWVuCssKgSyhKgM7Hgh9I19jlOeMl3eyhURXxq1WZKTzp79FDfWwDUWn7t6h3z25buweh0hYKUlERD6cLHc8iIiIiIiInLkUlERERERERFIoKImIiIiIiKRQUBIREREREUmhoCQiIiIiIpJCQUlERERERCSFgpKIiIiIiEgKBSUREREREZEUCkoiIiIiIiIpFJRERERERERSKCiJiIiIiIikUFASERERERFJoaAkIiIiIiKSQkFJREREREQkxbQJSv/8z/9MY2MjOTk5nHfeebz22mtTXSUREREREZmhpkVQ+vd//3duuukm7r77bt58802WL1/O2rVrOXLkyFRXTUREREREZqBpEZR+8IMfcO211/LlL3+ZxYsX8+CDD+L3+/npT3861VUTEREREZEZ6LQPSqFQiM2bN7NmzZr4Ntu2WbNmDZs2bcp4TH9/P8FgMOlHRERERERktE77oHTs2DGi0SiVlZVJ2ysrK2ltbc14zPr16wkEAvGf+vr6yaiqiIiIiIjMEKd9UDoVt99+Ox0dHfGfAwcOTHWVRERERERkGnFPdQVGUlZWhsvloq2tLWl7W1sbVVVVGY/x+Xz4fL7JqJ6IiIiIiMxAp32PktfrZeXKlWzcuDG+zXEcNm7cyOrVq6ewZiIiIiIiMlOd9j1KADfddBNXX301q1at4txzz+Wf/umf6O7u5stf/vJUV01ERERERGagaRGU/vIv/5KjR49y11130draytlnn82zzz6bNsGDiIiIiIjIeJgWQQnghhtu4IYbbpjqaoiIiIiIyBngtL9HSUREREREZLIpKImIiIiIiKRQUBIREREREUmhoCQiIiIiIpJCQUlERERERCSFgpKIiIiIiEgKBSUREREREZEUCkoiIiIiIiIpFJRERERERERSKCiJiIiIiIikUFASERERERFJoaAkIiIiIiKSQkFJREREREQkhYKSiIiIiIhICgUlERERERGRFApKIiIiIiIiKRSUREREREREUigoiYiIiIiIpFBQEhERERERSaGgJCIiIiIikkJBSUREREREJIV7qisgM9O+PS4qQ2FC+RaVdeD2THWNRORM0NXcQShUgInUYntqwAEnCpZtgQOh3lg5n9/QE2zBmxue2gqLiMhpS0FJxonBwok/evvRXUQJkBtwUz/Hx5wLirFLi2J9mA7YtoHY/+H1QWkFmHyw1ccpIh/Cyuq9bN3dD8f9YFlYGBh4bbKMBbYBIBRyUZlfSkV5Aybfoae7C2PAdlkYM3Q+23bFzhCNPXa5bJwoOAMvdy7bIjKwzwBO2MYMPLZscOXbeHL7iYS7J/y5i4jI+FJQknHhshxW8ycWWx8DLLzcTTul7Oj4CDvePJsNb1YSxQWAhcEALq9NeY2bWUvyODm7CqvYjdcb+1hjAcUVECgB2zWFT0xETnv+HB9Hn/7pwO+tOM4ROrqHurGtaHKvkWXBy9vKeWNnKfveL6Cv34VlLCw79toT+zFghr78Gdw+yB54YAbOx8AxA5EMOzcHJxAgVJLDiXAUXG4w4HLHghcm9mPZiWcF2zXwbZGxBh7H/jUmFtb6e6Gn04p/qVRQqNdIEZGJoqAk46bY1U4x7fHHNRxkMW8TMW7anBpOUB7fF8VNR7iUXfuX8e7+eXTSgS8wGKQAyyI/D+afnUP9BRX0+vzk5BH/cGEP/uWa2IeUQDHkF07SExWR04plWeTl5sQfu1yGksLQ0P5o+vC6C5YeZXFjO5HoUDe2ZZmE34FIJCkEJbIHyiYeMygctTl8JIc33itnx/vF9IfzwO0ZeG0LgeNgrMTgZWEZk3QFa3C/BTgGy4p1yBdaBroiuIpyyasvJrckh2DfwHjCgTAVP8/AL4NBarCnLOomqdcs1mYDZW3I8ac9JRGRM5KCkkw4txWh1tVMLc1p+z5qnqHdlLDHWUxXRyC+PUgxx1zzOf7+bPZu66UvkvDtMLEPLbbtUF4Os1YECM2rgNmxd3pjYm/6BYGhb3pFRBIVF4QoLggNXyhy6vcvzans4MKlbZzs8nLwaD5OQjCxSH9tsiwztC0Sybh98N/jwRx2txSx6+1iOrbmQ35+/LwAoRA40eQLOIP7B+sxGPAGKjbYk+9xO5TOKSC/Pp8eN+TmD5Q3YBJC2GDQctkJ5xzY7naTHCsNuF1Dxwz+68uBnFxERE5bCkoypWzLUGId51z7D0nbj5kKtp84xvHjeyjFl3ZcCB/HqOLIgQCH93aSU96Lxzs0RMXtcpi1zE/ZkjIKaz2UVEA4lP4tqojIRLEsKCkIUVJwYmwHjiKgXbj4MO1dXva1BejpdyUFr03bK/C4HNyuwYGAiXUyKSHNxHuvQhEXXX0ewsdLONpeQle3K9Yb5SSEnIGj7IRhiQlnSrjWQI+bYxIqYHC5Df5ch7zqAkx+HvWLh77gGnzqTvqpRUSmhIKSnJbKrCN8zPXMsGVCxsshp4Gd7cvZ174Qh8GB+oYe8nn1nWr6OUJpQy7lDbl43Q7FRRHctTm4GmvoD6u7SUSmJ8sa7BU7mravvcPDpcubyfFGx3TO7j43L26rY19bGHfwGIGBXinbTv6GKfV+rUxl4mXN0PDFqGPRFYx98dUfLCToLef47oQvwgxgGYoCUbqtWK9aamgyxEKV5TiY5NvIKCi28FYX4/i9eNO/XxMRGTMFJZm2vFaIRtceGtmTti9qbI6ZSnY7S9nRvILW5noMFt0NOTzXFaDxHAcXTvzb0cJyN1Z9NfklNm79r0JEzkB5ORE+tWp/7MGHGHaYyCScxxg42Z3D/iNFbDtQxt7WdqJmaOyeRWyUgTkEzTsGDkrsAXOy9JANHL+/30s46qKwKo+6pXm4ywPx6w5VAuyBKTeMseLBa7Cci2hC79nQldwuQ15VPr4iL/68D9cmIjJ96COhzEguy6HSOkylfZiL2ECnU0izmcOO5hXsbl7Clt+3A2DhDExs7qJ+3gGalhdQubIKf8HQcBDbig6Uhbx8Q22Tiy5vIMuVRUQkE8uCkvw+SvJb+cjsVsIRG8dk7tnPNEmGCYfTerISdfZ5aT4e4N2D5ez5YynhaMtQ+YTxhtbgrEBOQgizEibsGLwnLKEKEceiL+Ihr7KQmgW55M8qwbFsbGugSyt+81ZiMIvtSxtK6AxeZmhHXpmP4jJb99aKnGYUlOSMUGAHWcJbLOEtwsZDt8mP7wtSzDvmXJp3z+Xl3XWE/0970puxAfx5EeYvsmj6SClHjtaRXxmJDf/I8GbuOOCyHSwrdrNyZa2F5dX/1EREEnncY7sZyWS4LypRLIT1cfasNvpCLvrCQ6+7GcNHNDpsKEmc6TAUddFyopCdh8vY/XopzS+3pL1PDN2KNfTl2uDOtPeKhFkPjbEI46G8MY9AQyG+2gB2jjde1B5IXsYkT6ZhHIj0Rwj3hDEZZjI0BlzWUA+ZbUNBgaHf8SIio6NPb3LG8VhhiqyT8ccFpgMv/Sy13shY/gg1HMmZR9u+Wfz+PYdepyutjIkOvUPZLoucHEPDHDfzzymk5+wafMVu3AOzEHu8UFIGbk/aaUREZBzkeKMj36MVHds9XKX5vZzV0EZvyE1Hb07GMhYGkzBrYWxbhnIJwak/7OHNQ40Ee720vF5I1x+9ScdZZJhEY+B4l+2w6ZW+tCBmTHIQC1R4KawtZNGFeYSLKwFwUiqWGLRiISvbvtgOl5W8OLPHY/Aqg8kMo6AkZzyX5VBltWTdX24OEzh5glqzLb5o7qDUtVUgNiPfoe5Gmo/NY+er5eSUd+ByD63VYtvQ2ARLLwlQuKSWkioXlp1wAkN8MUnLii0oqaEYIiKnh1xvhFxv+hdmg0w4knVfxvIGDPuJOjY0ZX5fGXwPMAlrgg33tpAYnDp6cjnZ4+fdtyr54DU33uIjsQW3hqlP2vlImFl+sC4DgcmyDHWzbHLrSljy0VxCLn/8HENhLf3csaHtma87+LuCl0w1BSWREfitbuZb28Z8XLeTR4tp5OCx2TjY8TeMoClib8sitv6xFFfeMcob/AP3Sg1+i2fI9YSon+WicXUplauqCRTHjk1ZGgXL1huJiMh0ZllQX9IxqrLmFCbZqCuOnXtVYzNHgvl0h7wJvVUpiywDJmGh5dj24dfV2H+8hOC+XPZvC7Lv1QIs7zDDJaLRpEkyEmUKaIWFhtlLveTNKaOkfGhh5NHIFLwsJ8t2a2znljOHgpLIBMmzu5nPduazPW1f2HhoNXXs7l5M2476+HaDRS/5BCngra05vLczQuDp7qH9joNtGXK8ESoqLerOK2feR0txa9FGEREZRo4nQkNp+4jlnDH2iM0pP0Zf2M3RrnyOdOaDsZJnwmCoZ8ke2G5ZJm1h5cRyg5pPVLD9lVn4th8h4tixWRFTgo5JmC0jvi9D6ErdZAxYxqGsOIRVGODsj3rJL7LTD5Qz2oQFpe9+97s89dRTbNmyBa/XS3t7e1qZ5uZmrr/+ep5//nny8/O5+uqrWb9+Pe6E+ZlfeOEFbrrpJrZv3059fT133nkn11xzzURVW2RSeKww9dY+6u19afuixuakKWefWcCu3cvp2D3QnTTwMt9PDg4ugnPyaDloePcPXUnf/hkDOZ4wxcUOVasqWXKBX0P3RERkwuR4ItQXt1Nf3D7qY0x45N6xuqKT7D7ajtMN/RF32oLJsTW90nvFEid3T1zjy4k68fK25XA4WIQ76uHkyTpePFYYn2AkU2wbDGGDucwYK+MQRsdYuGxDdT2Uzy2gvDRMfp5WUZ6uJiwohUIhvvCFL7B69Wr+1//6X2n7o9Eol19+OVVVVbz88sscPnyYv/7rv8bj8fAP//APAOzbt4/LL7+c6667jp///Ods3LiRv/mbv6G6upq1a9dOVNVFppTLciiz2iijjXN4KW04QqcJcMDMZvf7y9j3/gLCDI29c7BwEaW41k15Uz4nu3I5sL0v9mIeH2AObhfk5jgUePz0ko+IiMjppsjfyzmz9o/b+ZxIcrAJRw9ytKuAw8GjnGjNzxK6kocfJsan+PTwDAU2y4Zw1MXeDyp4f0spRZVeoj4/mC76Dp3E2KGkOqTNVpih3jleB29VyeiepIwry5hMo0LHz8MPP8yNN96Y1qP0zDPP8OlPf5pDhw5RWRmbgeXBBx/k1ltv5ejRo3i9Xm699Vaeeuoptm0buj/kiiuuoL29nWeffXbUdQgGgwQCAR5zzcFvaRCqzBwh4yXC0HjwKG5OmDL2mCXsNss4RlXSeHAD1NT2sWiZi6qzqmj6aDmulG/EnIRXhLw8Q25VYfw+KLc1/CxRrmH2D7uP4c9rj3RdZ/ihIi4z8lCSkc4RKzPyN6C2GXkmrR07vHg9hnmze4c/V3T09yOMtqw9iuc5yBrD9QGsYW4Qz1j+FO63sKJjGxYUO+YUF0/9MIuujnFGtXG7LvDUpnouXd488qxvE1iHQadyT03Wc42iB2JMPsx/o2GMdTKHMZ17HNszm7EOvTtV4/7fcxRSg9JECvbl8FrzbGwcukM5WE6YfF9/hpLZJ+8Y1Bf2kFeaz9wlHsoa83C8Q7MuZpoWHsByEnvH0vcPPY5dzLYNAV8vjjcH40q+z+xUXkPH+n4QP+4U/8bH+t4Q7O6h6tN/Q0dHB4WFhVnLTdk9Sps2bWLZsmXxkASwdu1arr/+erZv386KFSvYtGkTa9asSTpu7dq13HjjjcOeu7+/n/7+oT/GYDA4rnUXOV14rRBekr+dKrA6mMX7XGKeot0UYxJm6tvNUloP1bGlpYHgs8B9rcOe3+WFRUuiXPAxF7MurKG/oi5pQcWCgCE/f0K/axEREZl2CnP6WDP/XeDDh0/HsdjeVsubL5Zgv9Qe3546Q2KmYfbpZQxOeCjEuDwuXD4P0bxSLlzdR26NF8eTPPW7NTCT1GCoytbFknSMMUnlvR4Hj3v6fV6YsqDU2tqaFJKA+OPW1tZhywSDQXp7e8nNzXwH+/r16/nOd74zAbUWmT5cVpRS61jyNhOhmg9YZr2W8ZikBRbxsS+8gNCJJn75aBk9jxzF4mh8vwEK8qKsWhFl1WUBXEvm4c9LfpX2+41mEhIREfkQbNuwrPogy6oPfqjzOKHMvTXeQD4bdy3kxWfdwPBfoKYy0cz3XyUOV3TlFxAo83Lxxf1E/em9N0kTdDjD7DPJnzFs28SXU5koYwpKt912G/fee++wZXbs2MHChQs/VKU+rNtvv52bbrop/jgYDFJfXz/MESJnhmLrOMXW8VGVdYxFDr10HDhAnXGndd/3kM/x7jref38B237YTX/k3aTx2gaor46w6qNe6i+uo6A6N2NoMobk1akGlrjXtOciIiITL9TRxYWVm3ESgshIvVWj6c0CsL0eunw1vLpvFr95HAwnMQNDIB0D3f2+eNkR+5tSgpLPE6FpVoTKplzKa9zkeMd/0owxBaWbb755xBnnZs+ePapzVVVV8dpryd9qt7W1xfcN/ju4LbFMYWFh1t4kAJ/Ph8/ny7pfREZmW4Ym672s+3tMHrt7l3By6yFyiY2XTpyBqJtCjh0v4fcdjVi/PYzHnTz0INNaGm4ilBSGWLAqn2WfrsJflpNWRkRERMaXbZn49O3jyQmF8Yc+4PzAIQxWUsBq6wyw9XgduZ6hWwiSJ9RIn2Y+vt8ydHb7ebMtB+v1Lrz5XkpKXWmfLeLD/0geTjjcotGJxhSUysvLKS8vH8shWa1evZrvfve7HDlyhIqKCgA2bNhAYWEhixcvjpd5+umnk47bsGEDq1evHpc6iMip81vdLM8yhA/giKlm54mzOX78OP0mhz5i/ePJ30IN/R7FTS957CWHffsM77zl4PE48Re9QF6ExRcGKFtRSUmpoyF9IiIi00SuJ33Yn88doSKvg3Prdp/SOR1jcaI3n2M9hRzuLKa9xZ+xXNL6XYM8R0Z1jQm7R6m5uZkTJ07Q3NxMNBply5YtAMydO5f8/Hwuu+wyFi9ezFVXXcV9991Ha2srd955J+vWrYv3Bl133XX8z//5P/nWt77FV77yFZ577jkee+wxnnrqqYmqtoiMkwrrMBXW4VGXd4xFhynmgJnNnl1L2LGrJumboX78/ObnYYpr25m/CJoWx14QB78hKq9wqDi/nprqKD7f9LthVEREREbPtgxl/k7K/J0sLGsZ07Gd/aGRCzGBQemuu+7iZz/7WfzxihUrAHj++ee55JJLcLlcPPnkk1x//fWsXr2avLw8rr76av7bf/tv8WOampp46qmn+OY3v8n9999PXV0d//Zv/6Y1lERmINsyFFsnKOYEZ/EGEeNOCkohfBw29ew9tIj3Whaw9fd5JPZI9ZNDbmkPDU02K85zUdqYHw9RqTP0WBZUzCugapaL/HwtBCgiIiLpJnwdpdOB1lESmVnCxkMfyV3sHaaYD8xc3jdLOEYVDjYZ16cgNlbZX2Ixey4sPttH1VnleIuG7nv0uB2q6i1KiqNJM+poHaURymsdpSFaRwnQOkrjfm6to/ShTOY6SknXnaQ2HUm2We+myuFgER8cLz7loXcfRmd/iHn3P3b6rqMkInKqPFYYDx1J2wqsDurYz/nmOdpNCSGyTwTRRh3tviba3q/hN7vKiTx6KGm/waKiymLJUotl5+eQO78Wn9dgGztlqtL0c+d4olSUhfHnqqdKRERkOlNQEpEZxWU5aetHpSo07RxtO0Y1mWfPPGQasCrr2fVWLZtfdwg5+zLM0Tdg4ObQ/JwIc+c4zD27gGPLK2mojS16nbruA0Bvn43XMzXfbIqIiMjoKCiJyBknz+oiz9qTdX/AnOTY+234eTdj0BlkDSz61EUBR6nlnfcDvLMlSm6Zic2uYyDT6OaCQsOlF/dRHSgkr8Sbdf0JERERmToKSiIiKaqsg1RZY1sBvd/4OGYqaT4yl44jpUn7LIaG4TnYHDE1/H+v5PN//q2dRYuilFUk3zvpsh0KfP00zHGz6pOlhPzFp/5kRERE5JQoKImIjAOf1U+t1UwtzSOWDRsPJ00pB07O4YOX57MXb8JeixA+3PRTvrSUAwdOkBfojO9N7eHyeiLM+0g+JXOK8Xpm/Nw8IiIik0ZBSURkknmsMBVWKxW0spI/Je1zjEUnRRwyjezbNp9fb6sBBmcqSg9CYbzUzulg1pxu5izPp7zMGShpQSQCKXdXFZVA5eIS8nJ1j5SIiMhwFJRERE4jtmUIcJKAdZJFvEW/8WGws5bfblZybG8Vb71fzcu/Sw4/VoZgZbCYd1YX56wIUbu0lMLKXIwZmHbb7SZQ4aGoGN03JSIiZzwFJRGR05jP6h92/yLeIoQvteMoo17y2GsW0ra1lse3FhOiG+hOKlNUYVi0KMSFqx0ql9fiLkifZt1kuJhtGyoCw68LJSIiMp0oKImITGN+qxt/StjJptCcxLYcmtiJQ/ri2ycpo8vUcnh/Lf+2O0DEdGITzHo+A3jdUebOjjJ7cS4XXebDnefLWC6V1+2Qq/W/RUTkNKagJCJyhrAtQyUtWXufykwbh493UnTsA2pH6KKyMPSSRwcl7DpQxWsvWmz8nYPbPXyvkt/dy+wmw+xl+Vz0Hzxp+7NNx+5yGQ0HFBGRSaWgJCIiAORYvTSxa1TD+AYZA11WgCOmmmN7qzP2HkHslJ0EaKeY17e72fy2j5deGLqQ4zhpx7itKF5XlKaGEItW5rPkHG9aGRERkYmioCQiIqfMsqCADgqsDuawc8TyPSaPY1Ry4MBsggeKhs5D8uQTYbz48yJULs8h7FTywgs2f/pDPyY6NGFFaiizTGwoYE2Nw4rVPirr9BYnIiKnTu8iIiIyafxWNw3WXhrYO2y5LlPI9p6VvLepnL6XjxKmI74v02x+ADYOeeU2J2b7aT3hJ1AQiu8zKYc4A0P8/N4QgYDFqgtc5Pizzy4oIiJnHgUlERE57eRbQVbxImCNaihgFBdBijl0bBbNR+ew41UnHqcyBSsHF/6iKLPm2JQ2FNDt+MjxRDBJpYdmm6goi1JbbxHIC+leKRGRM4SCkoiInJZcVvp9S1nLEqWMNsqsNpaa1+nDn3Ea80G95HGyo4z9b85n1+YaIsQmlhiKSYnhyoIcN7PmGhYshMaFuQTK3ekTTxgruefKMnhchqL8fqorwoiIyPSioCQiIjOKbZkRp0zPo4syq425ZjvdVgF95GYt20s+hzwL6G0t4OWWMn6/wQ0MH3wC/l6q6t18ZGkve0tnsZS+9EIDocrjdigr7MPrGX0wFBGRiaegJCIiZyzLgnw6yacza5moOYbdHcXpsmkidh9TtvukAI5SQyBgcaR9Hi+96ONEqJOnUspb1tBjC0NNrZsli8Is/IifkoLkUJVpyvShYYVQVtiLrdurRETGnYKSiIjIMFyWQy0fjHra9GJznLaDtRSbIFHclI9QvocCDjU38N6mIly/tiir9mExTO+SEyHPG6G2JkplYz6LzyqkoqgHyL4OlYiIjJ2CkoiIyDgqsDoooGNM61FFjJt2Smg9UU/wRHHWHiuDTS9+2smhfbeX5maL1zcXYOOPl7GtdsI7juG2Y1Ope+wobtthydweKhaUYRcEPtTzExE5UygoiYiITDG3FaGMI5RZR0YsGzU2QYo51lNFy/ZGQvjSymwa+NfBBdiETQ5bl+WxqrmL/KIITjjzPVbeHJu6Jh/FNf6M+0VEziQKSiIiItOIy3Io5jjF1nHmsX3YssZAFwGOU0HLtib+77bygdkAPUnlBnuwInhomttNdZObFct68fmTyxnAhCNp1/F7Q1SUO7jLKz7UcxMROZ0oKImIiMxQlgUFdFBgddDIbsLGM+y06btZRu8eP2/sqeO1DV5Imi598Lihjw4ONtVzHCpq/Vx8bidF0dQQZcWDVep1bctQXOHGZWefGENEZCopKImIiJwhPNbw05rPM+/QZ/mZzzujOl8nRXS8X8zO9+fx9ku52KZ/2PKJUcnBpnJWFx9Z2sv8hRZV84vB4816rCsaxePWFOoiMnkUlERERAQArxXCS2jU5Qtpp9bazwKzlSDFhK3sQSfVcSoIRyvZ/FYeL7xROLA1fVhfvG4em6VNJ1mwxM2iZR7c/tFfS0TkVCgoiYiIyIfisqIUc2xMxxSakxw51EkAi+qBbcOtT9ViGjmR7+Ppl0p4+iWwU0YQph7pRKKU5HZRXFvAZy46jK+qbEz1ExFRUBIREZFJl2P10sD7oy7vo4/290rIN4eJZv34MhSXOiilDXh3i5/m98spLTMJpSxSPwINrkHlRGLTque4+vF4LRbMCXHW2RZ2bu6o6yoiM4OCkoiIiJz2Kq0WKmkZ0/pU3Safk5TRuquOll05oz6un8FQZNh7oIh9B3rxeLMPCzRY+Fyx/Utmd1Izv2j0lRSR05aCkoiIiMxIeVYXeVYXdewf87E9Jo92SmndW8dre6tGKG1YOv8w/tpK3jrYQMvJ9jFfzxgLlx1l6ZxOfKXFYz5eRMafgpKIiIhICr/VjZ9uaqzmEcu+Z5bR8l4J3e9F6KGbxHWqDNaw914ZbADmNh2H0lggKzhpJ5cxg91orqTted5+qivC2B59nBOZCPpfloiIiMiHMId3xzTjX6p2SgnuL+bgvhz2vOHHJnkadCvhsYNNUZVDXV2YQE2Ala7uMQUlv99QmNOHNYYhjCJnKgUlERERkQ/BZUVx0XvKx1dxkCoOMpdtdFKEg521bIgcOtsCtLWWs40cXv29BztLj1WmrVaOm/mzLebMdmic5yG/MLmXyokkP873hrC1KLCcoRSURERERE4DtmUIcHLEcpW0YIAuAnT3FozpGi09jYSKvWx6pYTf/akAlxUlOVL54r8ZLEqKoyya3UVTk0VVlcHjiiadb2hYYLrEs7oso8Al046CkoiIiMg0Y1lQQAcFdIzpOBuH7r0FlNJC6QhlDRZHD1bz+3fKcYCiplx8ucMFo/R9flcP+XlQWu1l7XltWB5PhiNFTk8KSiIiIiJniArr0JjKz7Z20mv8tFPKyX1lRAcmlBhugopBQYqpmBVkbjVsPbCIn7XEFv3NdmSRv5d5s6PU1UMgt29M9RSZCApKIiIiIpJVrtVDLj1UWwfGdNxRU8XBD2Zz/APo50DSvVeZgtY+CnnjBR85lV7qanOoqsp275WVsffKtmJXsCzDqiWdFBdrqJ98OBMWlPbv38/f//3f89xzz9Ha2kpNTQ1/9Vd/xR133IHXOzQzzNatW1m3bh2vv/465eXlfP3rX+db3/pW0rkef/xxvv3tb7N//37mzZvHvffey6c+9amJqrqIiIiIfEjlVivlVuuoy4eNh3ZKOdFWTktbPftJn0lw+Mn6DE0NR7EqanjFKqU4L71XKts9VYnBy+sK85HFXaOut8xcExaUdu7cieM4/Mu//Atz585l27ZtXHvttXR3d/P9738fgGAwyGWXXcaaNWt48MEHeeedd/jKV75CUVERX/va1wB4+eWX+dKXvsT69ev59Kc/zSOPPMJnP/tZ3nzzTZYuXTpR1RcRERGRSeSxwpQTC1fzzTvDzv6XzuIkpbQfKONIcx67KcwQqgxZ5gIkik1VbTezavvoL6wnN8+VcFT6mYqKDOX5nXhcTto+mTksY8yk9Ut+73vf44EHHmDv3r0APPDAA9xxxx20trbGe5luu+02nnjiCXbu3AnAX/7lX9Ld3c2TTz4ZP8/555/P2WefzYMPPjiq6waDQQKBAI+55uC3XCMfICIiIiLTkjHQS17GgDModehfD3kEKeGkKeMEFUlrV8XKJ5wf8JT5KCh1M7+xm8ZG8Bdk73uIhpNnCnTbDsW5XeR4IqN9SuPCCYUn9XojORws4oPjxZxbt3vSr93ZH2Le/Y/R0dFBYWFh1nKTeo9SR0cHJSUl8cebNm3i4osvThqKt3btWu69915OnjxJcXExmzZt4qabbko6z9q1a3niiSeyXqe/v5/+/v7442AwOH5PQkREREROW5YFfrrHdIyfbso4ggG6KaSfnKxlDdB1PMDRY9W8squUV4CcsuH6HVK+pHd7yC/KZe6sELU1UYqKDN6EaddH04MxOITQbUcp9veM4gg5FZMWlPbs2cOPfvSj+LA7gNbWVpqampLKVVZWxvcVFxfT2toa35ZYprU1+5jX9evX853vfGccay8iIiIiM51lQT5B8hn+S/YyjtBo7abX+OmghOjx4T5Sp/ZeFdBrKumwI+x5v4J+x4dNNO2obDMLWhYUuoOUlLuIFlRz8bLDeFwj9045ISsWIj2hEctKzJiD0m233ca99947bJkdO3awcOHC+OOWlhY+8YlP8IUvfIFrr7127LUco9tvvz2pFyoYDFJfXz/h1xURERGRM8fgjIBj0WdyOHQkQt8RiwKOMLYlg6HDlHACL+Fd7QTr8zjYXIFlZQhWaTnLgGVRWdJHY22I6rI+Sv2duGxDthtxUocvWhis4WfUmFHGHJRuvvlmrrnmmmHLzJ49O/77oUOH+PjHP84FF1zAT37yk6RyVVVVtLW1JW0bfFxVVTVsmcH9mfh8Pnw+X9b9IiIiIiJTIcfqYzY7T/0EFvQbH+2U4ToYIZJhdsBsIsbNTsp5Fxf++iICpWW43KOfNCPX3UtTfYjayjCl/k48rvSesJlkzEGpvLyc8vLyUZVtaWnh4x//OCtXruShhx7CtpP/Q6xevZo77riDcDiMZ2Cl5g0bNrBgwQKKi4vjZTZu3MiNN94YP27Dhg2sXr16rFUXEREREZn2fFY/lbRQScvYDrQgZLy0U0r7wTLaD5RknV0w09C/VvLY84qLnGo/hWVFVFUm3FuVZer1VLYVO2/Uk0dRweROaDFWE3aPUktLC5dccgmzZs3i+9//PkePHo3vG+wN+k//6T/xne98h69+9avceuutbNu2jfvvv59//Md/jJf9xje+wcc+9jH+x//4H1x++eU8+uijvPHGG2m9UyIiIiIiMjyvFaKCw1RweKSFqdKEjYcOSmhvLePo4SqOvZM86cXIpzPUN5xkzqw+WiJL6Hcbwj39Ix6VyAlHsD1uPP6JHz02YUFpw4YN7Nmzhz179lBXV5e0b3BG8kAgwO9+9zvWrVvHypUrKSsr46677oqvoQRwwQUX8Mgjj3DnnXfyX//rf2XevHk88cQTWkNJRERERGQSeawwZbRRRhuzeZfoGKNEJ0WcPFDGG81ldHECL/3sIW9Ux0ZxU9IQZfmcY9TWR5OCUjQ0cs+Uyzv22DOp6yhNFa2jJCIiIiJyejAGesjHjGFR4TBejlNB0BRjsCkM9GWdGTCTgvw+Fs0+SVltDk7Az8IH/u/ptY6SiIiIiIic2SwL8uga83HFHBtY66qA3uDwPVGJIcpgcaijkV53Af6DPWDaR3U9BSUREREREZkWYmtddZJP55iO8xCis7mIfsCY0S1IrKAkIiIiIiIzWpF1giJOANCTYYHfTEY/MFBEREREROQMoaAkIiIiIiKSQkFJREREREQkhYKSiIiIiIhICgUlERERERGRFApKIiIiIiIiKRSUREREREREUigoiYiIiIiIpFBQEhERERERSaGgJCIiIiIikkJBSUREREREJIWCkoiIiIiISAr3VFdgMhhjAOgxzhTXREREREREptJgJhjMCNmcEUHp+PHjAFzj7JvimoiIiIiIyOmgs7OTQCCQdf8ZEZRKSkoAaG5uHrYx5MMLBoPU19dz4MABCgsLp7o6M5raevKorSeP2npyqb0nj9p68qitJ890bWtjDJ2dndTU1Axb7owISrYduxUrEAhMq/+I01lhYaHaepKorSeP2nryqK0nl9p78qitJ4/aevJMx7YeTeeJJnMQERERERFJoaAkIiIiIiKS4owISj6fj7vvvhufzzfVVZnx1NaTR209edTWk0dtPbnU3pNHbT151NaTZ6a3tWVGmhdPRERERETkDHNG9CiJiIiIiIiMhYKSiIiIiIhICgUlERERERGRFApKIiIiIiIiKWZMUNq/fz9f/epXaWpqIjc3lzlz5nD33XcTCoWSym3dupWPfvSj5OTkUF9fz3333Zd2rscff5yFCxeSk5PDsmXLePrppyfraUxr//zP/0xjYyM5OTmcd955vPbaa1NdpWln/fr1nHPOORQUFFBRUcFnP/tZdu3alVSmr6+PdevWUVpaSn5+Pp///Odpa2tLKtPc3Mzll1+O3++noqKCW265hUgkMplPZdq55557sCyLG2+8Mb5NbT1+Wlpa+Ku/+itKS0vJzc1l2bJlvPHGG/H9xhjuuusuqquryc3NZc2aNezevTvpHCdOnODKK6+ksLCQoqIivvrVr9LV1TXZT+W0Fo1G+fa3v530Xvj3f//3JM7bpLY+dS+99BJ/9md/Rk1NDZZl8cQTTyTtH6+2Hc1nlZluuLYOh8PceuutLFu2jLy8PGpqavjrv/5rDh06lHQOtfXojPR3nei6667Dsiz+6Z/+KWn7jG1rM0M888wz5pprrjG//e1vzfvvv29+/etfm4qKCnPzzTfHy3R0dJjKykpz5ZVXmm3btplf/OIXJjc31/zLv/xLvMyf/vQn43K5zH333Wfeffddc+eddxqPx2PeeeedqXha08ajjz5qvF6v+elPf2q2b99urr32WlNUVGTa2tqmumrTytq1a81DDz1ktm3bZrZs2WI+9alPmYaGBtPV1RUvc91115n6+nqzceNG88Ybb5jzzz/fXHDBBfH9kUjELF261KxZs8a89dZb5umnnzZlZWXm9ttvn4qnNC289tprprGx0Zx11lnmG9/4Rny72np8nDhxwsyaNctcc8015tVXXzV79+41v/3tb82ePXviZe655x4TCATME088Yd5++23zmc98xjQ1NZne3t54mU984hNm+fLl5pVXXjF/+MMfzNy5c82XvvSlqXhKp63vfve7prS01Dz55JNm37595vHHHzf5+fnm/vvvj5dRW5+6p59+2txxxx3ml7/8pQHMr371q6T949G2o/msciYYrq3b29vNmjVrzL//+7+bnTt3mk2bNplzzz3XrFy5MukcauvRGenvetAvf/lLs3z5clNTU2P+8R//MWnfTG3rGROUMrnvvvtMU1NT/PGPf/xjU1xcbPr7++Pbbr31VrNgwYL44y9+8Yvm8ssvTzrPeeedZ/72b/924is8jZ177rlm3bp18cfRaNTU1NSY9evXT2Gtpr8jR44YwLz44ovGmNibg8fjMY8//ni8zI4dOwxgNm3aZIyJveDZtm1aW1vjZR544AFTWFiY9LcvMZ2dnWbevHlmw4YN5mMf+1g8KKmtx8+tt95qLrrooqz7HccxVVVV5nvf+158W3t7u/H5fOYXv/iFMcaYd9991wDm9ddfj5d55plnjGVZpqWlZeIqP81cfvnl5itf+UrSts997nPmyiuvNMaorcdT6gfK8Wrb0XxWOdMM9+F90GuvvWYA88EHHxhj1NanKltbHzx40NTW1ppt27aZWbNmJQWlmdzWM2boXSYdHR2UlJTEH2/atImLL74Yr9cb37Z27Vp27drFyZMn42XWrFmTdJ61a9eyadOmyan0NBQKhdi8eXNSu9m2zZo1a9RuH1JHRwdA/O948+bNhMPhpLZeuHAhDQ0N8bbetGkTy5Yto7KyMl5m7dq1BINBtm/fPom1nx7WrVvH5Zdfnva/e7X1+Pl//+//sWrVKr7whS9QUVHBihUr+Nd//df4/n379tHa2prU1oFAgPPOOy+prYuKili1alW8zJo1a7Btm1dffXXynsxp7oILLmDjxo289957ALz99tv88Y9/5JOf/CSgtp5I49W2o/msIuk6OjqwLIuioiJAbT2eHMfhqquu4pZbbmHJkiVp+2dyW8/YoLRnzx5+9KMf8bd/+7fxba2trUkfaID449bW1mHLDO6XdMeOHSMajardxpnjONx4441ceOGFLF26FIj9fXq93vgbwaDEth7N37nEPProo7z55pusX78+bZ/aevzs3buXBx54gHnz5vHb3/6W66+/nr/7u7/jZz/7GTDUVsO9hrS2tlJRUZG03+12U1JSorZOcNttt3HFFVewcOFCPB4PK1as4MYbb+TKK68E1NYTabzaVq8rY9fX18ett97Kl770JQoLCwG19Xi69957cbvd/N3f/V3G/TO5rd1TXYGR3Hbbbdx7773DltmxYwcLFy6MP25paeETn/gEX/jCF7j22msnuooiE2LdunVs27aNP/7xj1NdlRnpwIEDfOMb32DDhg3k5ORMdXVmNMdxWLVqFf/wD/8AwIoVK9i2bRsPPvggV1999RTXbmZ57LHH+PnPf84jjzzCkiVL2LJlCzfeeCM1NTVqa5mRwuEwX/ziFzHG8MADD0x1dWaczZs3c//99/Pmm29iWdZUV2fSnfY9SjfffDM7duwY9mf27Nnx8ocOHeLjH/84F1xwAT/5yU+SzlVVVZU2Y9Xg46qqqmHLDO6XdGVlZbhcLrXbOLrhhht48sknef7556mrq4tvr6qqIhQK0d7enlQ+sa1H83cusRf/I0eO8JGPfAS3243b7ebFF1/khz/8IW63m8rKSrX1OKmurmbx4sVJ2xYtWkRzczMw1FbDvYZUVVVx5MiRpP2RSIQTJ06orRPccsst8V6lZcuWcdVVV/HNb34z3muqtp4449W2el0ZvcGQ9MEHH7Bhw4Z4bxKorcfLH/7wB44cOUJDQ0P8vfKDDz7g5ptvprGxEZjZbX3aB6Xy8nIWLlw47M/geMeWlhYuueQSVq5cyUMPPYRtJz+91atX89JLLxEOh+PbNmzYwIIFCyguLo6X2bhxY9JxGzZsYPXq1RP8TKcvr9fLypUrk9rNcRw2btyodhsjYww33HADv/rVr3juuedoampK2r9y5Uo8Hk9SW+/atYvm5uZ4W69evZp33nkn6UVr8A0k9cPqmezSSy/lnXfeYcuWLfGfVatWceWVV8Z/V1uPjwsvvDBtmvv33nuPWbNmAdDU1ERVVVVSWweDQV599dWktm5vb2fz5s3xMs899xyO43DeeedNwrOYHnp6etLe+1wuF47jAGrriTRebTuazyoyFJJ2797N73//e0pLS5P2q63Hx1VXXcXWrVuT3itramq45ZZb+O1vfwvM8Lae6tkkxsvBgwfN3LlzzaWXXmoOHjxoDh8+HP8Z1N7ebiorK81VV11ltm3bZh599FHj9/vTpgd3u93m+9//vtmxY4e5++67NT34KDz66KPG5/OZhx9+2Lz77rvma1/7mikqKkqaDUxGdv3115tAIGBeeOGFpL/hnp6eeJnrrrvONDQ0mOeee8688cYbZvXq1Wb16tXx/YNTVl922WVmy5Yt5tlnnzXl5eWasnoUEme9M0ZtPV5ee+0143a7zXe/+12ze/du8/Of/9z4/X7zv//3/46Xueeee0xRUZH59a9/bbZu3Wr+43/8jxmnVV6xYoV59dVXzR//+Eczb948TVmd4uqrrza1tbXx6cF/+ctfmrKyMvOtb30rXkZtfeo6OzvNW2+9Zd566y0DmB/84Afmrbfeis+0Nh5tO5rPKmeC4do6FAqZz3zmM6aurs5s2bIl6f0ycVY1tfXojPR3nSp11jtjZm5bz5ig9NBDDxkg40+it99+21x00UXG5/OZ2tpac88996Sd67HHHjPz5883Xq/XLFmyxDz11FOT9TSmtR/96EemoaHBeL1ec+6555pXXnllqqs07WT7G37ooYfiZXp7e81//s//2RQXFxu/32/+/M//POkLAWOM2b9/v/nkJz9pcnNzTVlZmbn55ptNOBye5Gcz/aQGJbX1+PnNb35jli5danw+n1m4cKH5yU9+krTfcRzz7W9/21RWVhqfz2cuvfRSs2vXrqQyx48fN1/60pdMfn6+KSwsNF/+8pdNZ2fnZD6N014wGDTf+MY3TENDg8nJyTGzZ882d9xxR9KHR7X1qXv++eczvkZfffXVxpjxa9vRfFaZ6YZr63379mV9v3z++efj51Bbj85If9epMgWlmdrWljEJy3WLiIiIiIjI6X+PkoiIiIiIyGRTUBIREREREUmhoCQiIiIiIpJCQUlERERERCSFgpKIiIiIiEgKBSUREREREZEUCkoiIiIiIiIpFJRERERERERSKCiJiIiIiIikUFASERERERFJoaAkIiIiIiKSQkFJREREREQkxf8PpvT/NgewJnIAAAAASUVORK5CYII=", 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", 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -485,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.12.1" }, "orig_nbformat": 4 }, diff --git a/examples/18_check_turbine.py b/examples/18_check_turbine.py index e71f321ff..b03cc6e9e 100644 --- a/examples/18_check_turbine.py +++ b/examples/18_check_turbine.py @@ -13,7 +13,7 @@ # See https://floris.readthedocs.io for documentation -import os +from pathlib import Path import matplotlib.pyplot as plt import numpy as np @@ -38,12 +38,13 @@ # Apply wind speeds fi.reinitialize(wind_speeds=ws_array) -# Get a list of available turbine models -turbines = os.listdir('../floris/turbine_library') -turbines = [t for t in turbines if 'yaml' in t] -turbines = [t.strip('.yaml') for t in turbines] -# Remove multi-dimensional Cp/Ct turbine definitions as they require different handling -turbines = [i for i in turbines if ('multi_dim' not in i)] +# Get a list of available turbine models provided through FLORIS, and remove +# multi-dimensional Cp/Ct turbine definitions as they require different handling +turbines = [ + t.stem + for t in fi.floris.farm.internal_turbine_library.iterdir() + if t.suffix == ".yaml" and ("multi_dim" not in t.stem) +] # Declare a set of figures for comparing cp and ct across models fig_cp_ct, axarr_cp_ct = plt.subplots(2,1,sharex=True,figsize=(10,10)) @@ -59,15 +60,15 @@ # Plot cp and ct onto the fig_cp_ct plot axarr_cp_ct[0].plot( - fi.floris.farm.turbine_map[0].power_thrust_table.wind_speed, - fi.floris.farm.turbine_map[0].power_thrust_table.power,label=t + fi.floris.farm.turbine_map[0].power_thrust_table["wind_speed"], + fi.floris.farm.turbine_map[0].power_thrust_table["power"],label=t ) axarr_cp_ct[0].grid(True) axarr_cp_ct[0].legend() axarr_cp_ct[0].set_ylabel('Cp') axarr_cp_ct[1].plot( - fi.floris.farm.turbine_map[0].power_thrust_table.wind_speed, - fi.floris.farm.turbine_map[0].power_thrust_table.thrust,label=t + fi.floris.farm.turbine_map[0].power_thrust_table["wind_speed"], + fi.floris.farm.turbine_map[0].power_thrust_table["thrust"],label=t ) axarr_cp_ct[1].grid(True) axarr_cp_ct[1].legend() diff --git a/examples/24_floating_turbine_models.py b/examples/24_floating_turbine_models.py index ceefaa547..364dca157 100644 --- a/examples/24_floating_turbine_models.py +++ b/examples/24_floating_turbine_models.py @@ -20,7 +20,7 @@ """ -This example demonstrates the impact of floating on turbine power and thurst (not wake behavior). +This example demonstrates the impact of floating on turbine power and thrust (not wake behavior). A floating turbine in FLORIS is defined by including a `floating_tilt_table` in the turbine input yaml which sets the steady tilt angle of the turbine based on wind speed. This tilt angle is computed for each turbine based on effective velocity. This tilt angle is then passed on @@ -29,10 +29,10 @@ The value of the parameter ref_tilt_cp_ct is the value of tilt at which the ct/cp curves have been defined. -If floating_correct_cp_ct_for_tilt is True, then the difference between the current tilt as +If `correct_cp_ct_for_tilt` is True, then the difference between the current tilt as interpolated from the floating tilt table is used to scale the turbine power and thrust. -If floating_correct_cp_ct_for_tilt is False, then it is assumed that the Cp/Ct tables provided +If `correct_cp_ct_for_tilt` is False, then it is assumed that the Cp/Ct tables provided already account for the variation in tilt with wind speed (for example they were computed from a turbine simulator with tilt degree-of-freedom enabled and the floating platform simulated), and no correction is made. diff --git a/examples/26_empirical_gauss_velocity_deficit_parameters.py b/examples/26_empirical_gauss_velocity_deficit_parameters.py index b2787059c..f11e9e07f 100644 --- a/examples/26_empirical_gauss_velocity_deficit_parameters.py +++ b/examples/26_empirical_gauss_velocity_deficit_parameters.py @@ -10,7 +10,7 @@ # License for the specific language governing permissions and limitations under # the License. -# See https://nrel.github.io/floris/intro.html for documentation +# See https://nrel.github.io/floris for documentation import copy diff --git a/examples/27_empirical_gauss_deflection_parameters.py b/examples/27_empirical_gauss_deflection_parameters.py index 5e453a7ad..2137999a4 100644 --- a/examples/27_empirical_gauss_deflection_parameters.py +++ b/examples/27_empirical_gauss_deflection_parameters.py @@ -10,7 +10,7 @@ # License for the specific language governing permissions and limitations under # the License. -# See https://nrel.github.io/floris/intro.html for documentation +# See https://nrel.github.io/floris for documentation import copy diff --git a/examples/29_floating_vs_fixedbottom_farm.py b/examples/29_floating_vs_fixedbottom_farm.py index d7c3dc29d..e3c908c1e 100644 --- a/examples/29_floating_vs_fixedbottom_farm.py +++ b/examples/29_floating_vs_fixedbottom_farm.py @@ -32,7 +32,7 @@ The value of the parameter ref_tilt_cp_ct is the value of tilt at which the ct/cp curves have been defined. -With floating_correct_cp_ct_for_tilt True, the difference between the current +With `correct_cp_ct_for_tilt` True, the difference between the current tilt as interpolated from the floating tilt table is used to scale the turbine power and thrust. diff --git a/examples/32_plot_velocity_deficit_profiles.py b/examples/32_plot_velocity_deficit_profiles.py new file mode 100644 index 000000000..9b12dcc4e --- /dev/null +++ b/examples/32_plot_velocity_deficit_profiles.py @@ -0,0 +1,205 @@ +# Copyright 2021 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. + +# See https://floris.readthedocs.io for documentation + + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib import ticker + +import floris.tools.visualization as wakeviz +from floris.tools import cut_plane, FlorisInterface +from floris.tools.visualization import VelocityProfilesFigure +from floris.utilities import reverse_rotate_coordinates_rel_west + + +""" +This example illustrates how to plot velocity deficit profiles at several locations +downstream of a turbine. Here we use the following definition: + velocity_deficit = (homogeneous_wind_speed - u) / homogeneous_wind_speed + , where u is the wake velocity obtained when the incoming wind speed is the + same at all heights and equal to `homogeneous_wind_speed`. +""" + +# The first two functions are just used to plot the coordinate system in which the +# profiles are sampled. Please go to the main function to begin the example. +def plot_coordinate_system(x_origin, y_origin, wind_direction): + quiver_length = 1.4 * D + plt.quiver( + [x_origin, x_origin], + [y_origin, y_origin], + [quiver_length, quiver_length], + [0, 0], + angles=[270 - wind_direction, 360 - wind_direction], + scale_units='x', + scale=1, + ) + annotate_coordinate_system(x_origin, y_origin, quiver_length) + +def annotate_coordinate_system(x_origin, y_origin, quiver_length): + x1 = np.array([quiver_length + 0.35 * D, 0.0]) + x2 = np.array([0.0, quiver_length + 0.35 * D]) + x3 = np.array([90.0, 90.0]) + x, y, _ = reverse_rotate_coordinates_rel_west( + fi.floris.flow_field.wind_directions, + x1[None, :], + x2[None, :], + x3[None, :], + x_center_of_rotation=0.0, + y_center_of_rotation=0.0, + ) + x = np.squeeze(x, axis=0) + x_origin + y = np.squeeze(y, axis=0) + y_origin + plt.text(x[0], y[0], '$x_1$', bbox={'facecolor': 'white'}) + plt.text(x[1], y[1], '$x_2$', bbox={'facecolor': 'white'}) + +if __name__ == '__main__': + D = 126.0 # Turbine diameter + hub_height = 90.0 + homogeneous_wind_speed = 8.0 + + fi = FlorisInterface("inputs/gch.yaml") + fi.reinitialize(layout_x=[0.0], layout_y=[0.0]) + + # ------------------------------ Single-turbine layout ------------------------------ + # We first show how to sample and plot velocity deficit profiles on a single-turbine layout. + # Lines are drawn on a horizontal plane to indicate were the velocity is sampled. + downstream_dists = D * np.array([3, 5, 7]) + # Sample three profiles along three corresponding lines that are all parallel to the y-axis + # (cross-stream direction). The streamwise location of each line is given in `downstream_dists`. + profiles = fi.sample_velocity_deficit_profiles( + direction='cross-stream', + downstream_dists=downstream_dists, + homogeneous_wind_speed=homogeneous_wind_speed, + ) + + horizontal_plane = fi.calculate_horizontal_plane(height=hub_height) + fig, ax = plt.subplots(figsize=(6.4, 3)) + wakeviz.visualize_cut_plane(horizontal_plane, ax) + colors = ['b', 'g', 'c'] + for i, profile in enumerate(profiles): + # Plot profile coordinates on the horizontal plane + ax.plot(profile['x'], profile['y'], colors[i], label=f'x/D={downstream_dists[i] / D:.1f}') + ax.set_xlabel('x [m]') + ax.set_ylabel('y [m]') + ax.set_title('Streamwise velocity in a horizontal plane: gauss velocity model') + fig.tight_layout(rect=[0, 0, 0.82, 1]) + ax.legend(bbox_to_anchor=[1.29, 1.04]) + + # Initialize a VelocityProfilesFigure. The workflow is similar to a matplotlib Figure: + # Initialize it, plot data, and then customize it further if needed. + profiles_fig = VelocityProfilesFigure( + downstream_dists_D=downstream_dists / D, + layout=['cross-stream'], + coordinate_labels=['x/D', 'y/D'], + ) + # Add profiles to the VelocityProfilesFigure. This method automatically matches the supplied + # profiles to the initialized axes in the figure. + profiles_fig.add_profiles(profiles, color='k') + + # Change velocity model to jensen, get the velocity deficit profiles, + # and add them to the figure. + floris_dict = fi.floris.as_dict() + floris_dict['wake']['model_strings']['velocity_model'] = 'jensen' + fi = FlorisInterface(floris_dict) + profiles = fi.sample_velocity_deficit_profiles( + direction='cross-stream', + downstream_dists=downstream_dists, + homogeneous_wind_speed=homogeneous_wind_speed, + resolution=400, + ) + profiles_fig.add_profiles(profiles, color='r') + + # The dashed reference lines show the extent of the rotor + profiles_fig.add_ref_lines_x2([-0.5, 0.5]) + for ax in profiles_fig.axs[0]: + ax.xaxis.set_major_locator(ticker.MultipleLocator(0.2)) + + profiles_fig.axs[0,0].legend(['gauss', 'jensen'], fontsize=11) + profiles_fig.fig.suptitle( + 'Velocity deficit profiles from different velocity models', + fontsize=14, + ) + + # -------------------------------- Two-turbine layout -------------------------------- + # This is a two-turbine case where the wind direction is north-west. Velocity profiles + # are sampled behind the second turbine. This illustrates the need for a + # sampling-coordinate-system (x1, x2, x3) that is rotated such that x1 is always in the + # streamwise direction. The user may define the origin of this coordinate system + # (i.e. where to start sampling the profiles). + wind_direction = 315.0 # Try to change this + downstream_dists = D * np.array([3, 5]) + floris_dict = fi.floris.as_dict() + floris_dict['wake']['model_strings']['velocity_model'] = 'gauss' + fi = FlorisInterface(floris_dict) + # Let (x_t1, y_t1) be the location of the second turbine + x_t1 = 2 * D + y_t1 = -2 * D + fi.reinitialize(wind_directions=[wind_direction], layout_x=[0.0, x_t1], layout_y=[0.0, y_t1]) + + # Extract profiles at a set of downstream distances from the starting point (x_start, y_start) + cross_profiles = fi.sample_velocity_deficit_profiles( + direction='cross-stream', + downstream_dists=downstream_dists, + homogeneous_wind_speed=homogeneous_wind_speed, + x_start=x_t1, + y_start=y_t1, + ) + + horizontal_plane = fi.calculate_horizontal_plane(height=hub_height, x_bounds=[-2 * D, 9 * D]) + ax = wakeviz.visualize_cut_plane(horizontal_plane) + colors = ['b', 'g', 'c'] + for i, profile in enumerate(cross_profiles): + ax.plot( + profile['x'], + profile['y'], + colors[i], + label=f'$x_1/D={downstream_dists[i] / D:.1f}$', + ) + ax.set_xlabel('x [m]') + ax.set_ylabel('y [m]') + ax.set_title('Streamwise velocity in a horizontal plane') + ax.legend() + plot_coordinate_system(x_origin=x_t1, y_origin=y_t1, wind_direction=wind_direction) + + # Sample velocity deficit profiles in the vertical direction at the same downstream + # locations as before. We stay directly downstream of the turbine (i.e. x2 = 0). These + # profiles are almost identical to the cross-stream profiles. However, we now explicitly + # set the profile range. The default range is [-2 * D, 2 * D]. + vertical_profiles = fi.sample_velocity_deficit_profiles( + direction='vertical', + profile_range=[-1.5 * D, 1.5 * D], + downstream_dists=downstream_dists, + homogeneous_wind_speed=homogeneous_wind_speed, + x_start=x_t1, + y_start=y_t1, + ) + + profiles_fig = VelocityProfilesFigure( + downstream_dists_D=downstream_dists / D, + layout=['cross-stream', 'vertical'], + ) + profiles_fig.add_profiles(cross_profiles + vertical_profiles, color='k') + + profiles_fig.set_xlim([-0.05, 0.85]) + profiles_fig.axs[1,0].set_ylim([-2.2, 2.2]) + for ax in profiles_fig.axs[0]: + ax.xaxis.set_major_locator(ticker.MultipleLocator(0.4)) + + profiles_fig.fig.suptitle( + 'Cross-stream profiles at hub-height, and\nvertical profiles at $x_2 = 0$', + fontsize=14, + ) + + + plt.show() diff --git a/examples/32_specify_turbine_power_curve.py b/examples/32_specify_turbine_power_curve.py new file mode 100644 index 000000000..03fbf9978 --- /dev/null +++ b/examples/32_specify_turbine_power_curve.py @@ -0,0 +1,78 @@ +# Copyright 2021 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. + +# See https://floris.readthedocs.io for documentation + + +import matplotlib.pyplot as plt +import numpy as np + +import floris.tools.visualization as wakeviz +from floris.tools import FlorisInterface +from floris.turbine_library.turbine_utilities import build_turbine_dict + + +""" +This example demonstrates how to specify a turbine model based on a power +and thrust curve for the wind turbine, as well as possible physical parameters +(which default to the parameters of the NREL 5MW reference turbine). + +Note that it is also possible to have a .yaml created, if the file_path +argument to build_turbine_dict is set. +""" + +# Generate an example turbine power and thrust curve for use in the FLORIS model +turbine_data_dict = { + "wind_speed":[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20], + "power_absolute":[0, 30, 200, 500, 1000, 2000, 4000, 4000, 4000, 4000, 4000], + "thrust_coefficient":[0, 0.9, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2] +} + +turbine_dict = build_turbine_dict( + turbine_data_dict, + "example_turbine", + file_path=None, + generator_efficiency=1, + hub_height=90, + pP=1.88, + pT=1.88, + rotor_diameter=126, + TSR=8, + air_density=1.225, + ref_tilt_cp_ct=5 +) + +fi = FlorisInterface("inputs/gch.yaml") +wind_speeds = np.linspace(1, 15, 100) +# Replace the turbine(s) in the FLORIS model with the created one +fi.reinitialize( + layout_x=[0], + layout_y=[0], + wind_speeds=wind_speeds, + turbine_type=[turbine_dict] +) +fi.calculate_wake() + +powers = fi.get_farm_power() + +fig, ax = plt.subplots(1,1) + +ax.scatter(wind_speeds, powers[0,:]/1000, color="C0", s=5, label="Test points") +ax.scatter(turbine_data_dict["wind_speed"], turbine_data_dict["power_absolute"], + color="red", s=20, label="Specified points") + +ax.grid() +ax.set_xlabel("Wind speed [m/s]") +ax.set_ylabel("Power [kW]") +ax.legend() + +plt.show() diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml index f9321cb17..b1755ab6c 100644 --- a/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml +++ b/examples/inputs_floating/turbine_files/nrel_5MW_fixed.yaml @@ -7,7 +7,7 @@ rotor_diameter: 126.0 TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 5.0 -floating_correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct +correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct power_thrust_table: power: - 0.0 @@ -165,3 +165,16 @@ power_thrust_table: - 25.01 - 25.02 - 50.0 +floating_tilt_table: + tilt: + - 5.0 + - 5.0 + - 5.0 + - 5.0 + - 5.0 + wind_speed: + - 0.0 + - 4.0 + - 11.0 + - 25.0 + - 50.0 diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml index 834b0f85b..cf3bc3049 100644 --- a/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml +++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating.yaml @@ -7,7 +7,7 @@ rotor_diameter: 126.0 TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 5.0 -floating_correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct +correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct power_thrust_table: power: - 0.0 @@ -172,7 +172,7 @@ floating_tilt_table: - 9.0 - 5.0 - 5.0 - wind_speeds: + wind_speed: - 0.0 - 4.0 - 11.0 diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml index 0c7ae770e..4fa506e25 100644 --- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml +++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_defined_floating.yaml @@ -7,7 +7,7 @@ rotor_diameter: 126.0 TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 5.0 -floating_correct_cp_ct_for_tilt: False # Do not apply tilt correction to cp/ct +correct_cp_ct_for_tilt: False # Do not apply tilt correction to cp/ct power_thrust_table: power: - 0.0 @@ -172,7 +172,7 @@ floating_tilt_table: - 9.0 - 5.0 - 5.0 - wind_speeds: + wind_speed: - 0.0 - 4.0 - 11.0 diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml index 234807512..da0d15a37 100644 --- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml +++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt15.yaml @@ -7,7 +7,7 @@ rotor_diameter: 126.0 TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 5.0 -floating_correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct +correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct power_thrust_table: power: - 0.0 @@ -172,7 +172,7 @@ floating_tilt_table: - 15.0 - 15.0 - 15.0 - wind_speeds: + wind_speed: - 0.0 - 4.0 - 11.0 diff --git a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml index 9eac120ec..b1755ab6c 100644 --- a/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml +++ b/examples/inputs_floating/turbine_files/nrel_5MW_floating_fixedtilt5.yaml @@ -7,7 +7,7 @@ rotor_diameter: 126.0 TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 5.0 -floating_correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct +correct_cp_ct_for_tilt: True # Apply tilt correction to cp/ct power_thrust_table: power: - 0.0 @@ -172,7 +172,7 @@ floating_tilt_table: - 5.0 - 5.0 - 5.0 - wind_speeds: + wind_speed: - 0.0 - 4.0 - 11.0 diff --git a/floris/logging_manager.py b/floris/logging_manager.py index 981241f36..abdeff0e9 100644 --- a/floris/logging_manager.py +++ b/floris/logging_manager.py @@ -143,10 +143,10 @@ def filter(self, record): return True -class LoggerBase: +class LoggingManager: """ - Convenience super-class to any class requiring access to the logging - module. The virtual property here allows a simple and dynamic method + This class provide an easy access to the global logger. + The virtual property here allows a simple and dynamic method for obtaining the correct logger for the calling class. """ diff --git a/floris/simulation/__init__.py b/floris/simulation/__init__.py index 6da5c5ac5..b7b41ed16 100644 --- a/floris/simulation/__init__.py +++ b/floris/simulation/__init__.py @@ -44,7 +44,6 @@ Ct, power, rotor_effective_velocity, - TiltTable, Turbine ) from .turbine_multi_dim import ( diff --git a/floris/simulation/base.py b/floris/simulation/base.py index 6125b519d..4edd11d6f 100644 --- a/floris/simulation/base.py +++ b/floris/simulation/base.py @@ -17,7 +17,7 @@ Defines the BaseClass parent class for all models to be based upon. """ -from abc import ABC, abstractmethod +from abc import abstractmethod from enum import Enum from typing import ( Any, @@ -25,9 +25,15 @@ Final, ) -import attrs +from attrs import ( + Attribute, + define, + field, + fields, + setters, +) -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager from floris.type_dec import FromDictMixin @@ -37,44 +43,31 @@ class State(Enum): USED = 2 -class BaseClass(LoggerBase, FromDictMixin): +@define +class BaseClass(FromDictMixin): """ BaseClass object class. This class does the logging and MixIn class inheritance. """ - state = State.UNINITIALIZED - - - @classmethod - def get_model_defaults(cls) -> Dict[str, Any]: - """Produces a dictionary of the keyword arguments and their defaults. - - Returns - ------- - Dict[str, Any] - Dictionary of keyword argument: default. - """ - return {el.name: el.default for el in attrs.fields(cls)} - - def _get_model_dict(self) -> dict: - """Convenience method that wraps the `attrs.asdict` method. Returns the object's - parameters as a dictionary. - - Returns - ------- - dict - The provided or default, if no input provided, model settings as a dictionary. - """ - return attrs.asdict(self) + # Initialize `state` and ensure it is treated as an attribute rather than a constant parameter. + # See https://www.attrs.org/en/stable/api-attr.html#attr.ib + state = field(init=False, default=State.UNINITIALIZED) + _logging_manager: LoggingManager = field(init=False, default=LoggingManager()) + @property + def logger(self): + """Returns the logger manager object.""" + return self._logging_manager.logger -class BaseModel(BaseClass, ABC): +@define +class BaseModel(BaseClass): """ BaseModel is the generic class for any wake models. It defines the API required to create a valid model. """ - NUM_EPS: Final[float] = 0.001 # This is a numerical epsilon to prevent divide by zeros + # This is a numerical epsilon to prevent divide by zeros + NUM_EPS: Final[float] = field(init=False, default=0.001, on_setattr=setters.frozen) @abstractmethod def prepare_function() -> dict: diff --git a/floris/simulation/farm.py b/floris/simulation/farm.py index dc1435a88..ce289ace2 100644 --- a/floris/simulation/farm.py +++ b/floris/simulation/farm.py @@ -14,11 +14,16 @@ import copy from pathlib import Path -from typing import Any, List +from typing import ( + Any, + Dict, + List, +) import attrs import numpy as np from attrs import define, field +from scipy.interpolate import interp1d from floris.simulation import ( BaseClass, @@ -34,7 +39,7 @@ NDArrayFloat, NDArrayObject, ) -from floris.utilities import load_yaml, Vec3 +from floris.utilities import load_yaml default_turbine_library_path = Path(__file__).parents[1] / "turbine_library" @@ -52,6 +57,18 @@ class Farm(BaseClass): Wake, FlowField) and packages everything into the appropriate data type. Farm should also be used as an entry point to probe objects for generating output. + + Args: + layout_x (NDArrayFloat): A sequence of x-axis locations for the turbines that can be + converted to a 1-D :py:obj:`numpy.ndarray`. + layout_y (NDArrayFloat): A sequence of y-axis locations for the turbines that can be + converted to a 1-D :py:obj:`numpy.ndarray`. + turbine_type (list[dict | str]): A list of turbine definition dictionaries, or string + references to the filename of the turbine type in either the FLORIS-provided turbine + library (.../floris/turbine_library/), or a user-provided + :py:attr:`turbine_library_path`. + turbine_library_path (:obj:`str`): Either an absolute file path to the turbine library, or a + path relative to the file that is running the analysis. """ layout_x: NDArrayFloat = field(converter=floris_array_converter) @@ -63,9 +80,11 @@ class Farm(BaseClass): ) turbine_definitions: list = field(init=False, validator=iter_validator(list, dict)) - coordinates: List[Vec3] = field(init=False) - turbine_fCts: tuple = field(init=False, default=[]) - turbine_fTilts: list = field(init=False, default=[]) + + turbine_fCts: Dict[str, interp1d] | List[interp1d] = field(init=False, factory=list) + turbine_fCts_sorted: NDArrayFloat = field(init=False, factory=list) + + turbine_tilt_interps: dict[str, interp1d] = field(init=False, factory=dict) yaw_angles: NDArrayFloat = field(init=False) yaw_angles_sorted: NDArrayFloat = field(init=False) @@ -74,28 +93,38 @@ class Farm(BaseClass): tilt_angles_sorted: NDArrayFloat = field(init=False) hub_heights: NDArrayFloat = field(init=False) - hub_heights_sorted: NDArrayFloat = field(init=False, default=[]) + hub_heights_sorted: NDArrayFloat = field(init=False, factory=list) + + turbine_map: List[Turbine | TurbineMultiDimensional] = field(init=False, factory=list) - turbine_type_map: NDArrayObject = field(init=False, default=[]) - turbine_type_map_sorted: NDArrayObject = field(init=False, default=[]) + turbine_type_map: NDArrayObject = field(init=False, factory=list) + turbine_type_map_sorted: NDArrayObject = field(init=False, factory=list) - rotor_diameters: NDArrayFloat = field(init=False, default=[]) - rotor_diameters_sorted: NDArrayFloat = field(init=False, default=[]) + turbine_power_interps: Dict[str, interp1d] | List[interp1d] = field(init=False, factory=list) + turbine_power_interps_sorted: NDArrayFloat = field(init=False, factory=list) - TSRs: NDArrayFloat = field(init=False, default=[]) - TSRs_sorted: NDArrayFloat = field(init=False, default=[]) + rotor_diameters: NDArrayFloat = field(init=False, factory=list) + rotor_diameters_sorted: NDArrayFloat = field(init=False, factory=list) - pPs: NDArrayFloat = field(init=False, default=[]) - pPs_sorted: NDArrayFloat = field(init=False, default=[]) + TSRs: NDArrayFloat = field(init=False, factory=list) + TSRs_sorted: NDArrayFloat = field(init=False, factory=list) - pTs: NDArrayFloat = field(init=False, default=[]) - pTs_sorted: NDArrayFloat = field(init=False, default=[]) + pPs: NDArrayFloat = field(init=False, factory=list) + pPs_sorted: NDArrayFloat = field(init=False, factory=list) - ref_tilt_cp_cts: NDArrayFloat = field(init=False, default=[]) - ref_tilt_cp_cts_sorted: NDArrayFloat = field(init=False, default=[]) + pTs: NDArrayFloat = field(init=False, factory=list) + pTs_sorted: NDArrayFloat = field(init=False, factory=list) - correct_cp_ct_for_tilt: NDArrayFloat = field(init=False, default=[]) - correct_cp_ct_for_tilt_sorted: NDArrayFloat = field(init=False, default=[]) + ref_density_cp_cts: NDArrayFloat = field(init=False, factory=list) + ref_density_cp_cts_sorted: NDArrayFloat = field(init=False, factory=list) + + ref_tilt_cp_cts: NDArrayFloat = field(init=False, factory=list) + ref_tilt_cp_cts_sorted: NDArrayFloat = field(init=False, factory=list) + + correct_cp_ct_for_tilt: NDArrayFloat = field(init=False, factory=list) + correct_cp_ct_for_tilt_sorted: NDArrayFloat = field(init=False, factory=list) + + internal_turbine_library: Path = field(init=False, default=default_turbine_library_path) def __attrs_post_init__(self) -> None: # Turbine definitions can be supplied in three ways: @@ -128,7 +157,7 @@ def __attrs_post_init__(self) -> None: continue # Check if the file exists in the internal and/or external library - internal_fn = (default_turbine_library_path / t).with_suffix(".yaml") + internal_fn = (self.internal_turbine_library / t).with_suffix(".yaml") external_fn = (self.turbine_library_path / t).with_suffix(".yaml") in_internal = internal_fn.exists() in_external = external_fn.exists() @@ -248,11 +277,16 @@ def construct_turbine_correct_cp_ct_for_tilt(self): ) def construct_turbine_map(self): - if 'multi_dimensional_cp_ct' in self.turbine_definitions[0].keys() \ - and self.turbine_definitions[0]['multi_dimensional_cp_ct'] is True: - self.turbine_map = [ - TurbineMultiDimensional.from_dict(turb) for turb in self.turbine_definitions - ] + multi_key = "multi_dimensional_cp_ct" + if multi_key in self.turbine_definitions[0] and self.turbine_definitions[0][multi_key]: + self.turbine_map = [] + for turb in self.turbine_definitions: + _turb = {**turb, **{"turbine_library_path": self.internal_turbine_library}} + try: + self.turbine_map.append(TurbineMultiDimensional.from_dict(_turb)) + except FileNotFoundError: + _turb["turbine_library_path"] = self.turbine_library_path + self.turbine_map.append(TurbineMultiDimensional.from_dict(_turb)) else: self.turbine_map = [Turbine.from_dict(turb) for turb in self.turbine_definitions] @@ -264,8 +298,10 @@ def construct_turbine_fCts(self): def construct_multidim_turbine_fCts(self): self.turbine_fCts = [turb.fCt_interp for turb in self.turbine_map] - def construct_turbine_fTilts(self): - self.turbine_fTilts = [(turb.turbine_type, turb.fTilt_interp) for turb in self.turbine_map] + def construct_turbine_tilt_interps(self): + self.turbine_tilt_interps = { + turb.turbine_type: turb.tilt_interp for turb in self.turbine_map + } def construct_turbine_power_interps(self): self.turbine_power_interps = { @@ -275,11 +311,6 @@ def construct_turbine_power_interps(self): def construct_multidim_turbine_power_interps(self): self.turbine_power_interps = [turb.power_interp for turb in self.turbine_map] - def construct_coordinates(self): - self.coordinates = np.array([ - Vec3([x, y, z]) for x, y, z in zip(self.layout_x, self.layout_y, self.hub_heights) - ]) - def expand_farm_properties( self, n_wind_directions: int, @@ -394,7 +425,7 @@ def calculate_tilt_for_eff_velocities(self, rotor_effective_velocities): tilt_angles = compute_tilt_angles_for_floating_turbines( self.turbine_type_map_sorted, self.tilt_angles_sorted, - self.turbine_fTilts, + self.turbine_tilt_interps, rotor_effective_velocities, ) return tilt_angles @@ -437,7 +468,6 @@ def finalize(self, unsorted_indices): unsorted_indices[:,:,:,0,0], axis=2 ) - # TODO: do these need to be unsorted? Maybe we should just for completeness... self.ref_density_cp_cts = np.take_along_axis( self.ref_density_cp_cts_sorted, unsorted_indices[:,:,:,0,0], @@ -470,6 +500,16 @@ def finalize(self, unsorted_indices): ) self.state.USED + @property + def coordinates(self): + return np.array([ + np.array([x, y, z]) for x, y, z in zip( + self.layout_x, + self.layout_y, + self.hub_heights if len(self.hub_heights.shape) == 1 else self.hub_heights[0,0] + ) + ]) + @property def n_turbines(self): return len(self.layout_x) diff --git a/floris/simulation/floris.py b/floris/simulation/floris.py index 09722a2d7..a24a33939 100644 --- a/floris/simulation/floris.py +++ b/floris/simulation/floris.py @@ -16,6 +16,8 @@ from pathlib import Path +import numpy as np +import pandas as pd import yaml from attrs import define, field @@ -42,7 +44,11 @@ turbopark_solver, WakeModelManager, ) -from floris.utilities import load_yaml +from floris.type_dec import NDArrayFloat +from floris.utilities import ( + load_yaml, + reverse_rotate_coordinates_rel_west, +) @define @@ -81,7 +87,7 @@ def __attrs_post_init__(self) -> None: self.check_deprecated_inputs() - # Initialize farm quanitities that depend on other objects + # Initialize farm quantities that depend on other objects self.farm.construct_turbine_map() if self.wake.model_strings['velocity_model'] == 'multidim_cp_ct': self.farm.construct_multidim_turbine_fCts() @@ -96,9 +102,8 @@ def __attrs_post_init__(self) -> None: self.farm.construct_turbine_pTs() self.farm.construct_turbine_ref_density_cp_cts() self.farm.construct_turbine_ref_tilt_cp_cts() - self.farm.construct_turbine_fTilts() + self.farm.construct_turbine_tilt_interps() self.farm.construct_turbine_correct_cp_ct_for_tilt() - self.farm.construct_coordinates() self.farm.set_yaw_angles(self.flow_field.n_wind_directions, self.flow_field.n_wind_speeds) self.farm.set_tilt_to_ref_tilt( self.flow_field.n_wind_directions, @@ -108,7 +113,7 @@ def __attrs_post_init__(self) -> None: if self.solver["type"] == "turbine_grid": self.grid = TurbineGrid( turbine_coordinates=self.farm.coordinates, - reference_turbine_diameter=self.farm.rotor_diameters, + turbine_diameters=self.farm.rotor_diameters, wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, grid_resolution=self.solver["turbine_grid_points"], @@ -117,7 +122,7 @@ def __attrs_post_init__(self) -> None: elif self.solver["type"] == "turbine_cubature_grid": self.grid = TurbineCubatureGrid( turbine_coordinates=self.farm.coordinates, - reference_turbine_diameter=self.farm.rotor_diameters, + turbine_diameters=self.farm.rotor_diameters, wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, time_series=self.flow_field.time_series, @@ -126,7 +131,7 @@ def __attrs_post_init__(self) -> None: elif self.solver["type"] == "flow_field_grid": self.grid = FlowFieldGrid( turbine_coordinates=self.farm.coordinates, - reference_turbine_diameter=self.farm.rotor_diameters, + turbine_diameters=self.farm.rotor_diameters, wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, grid_resolution=self.solver["flow_field_grid_points"], @@ -135,7 +140,7 @@ def __attrs_post_init__(self) -> None: elif self.solver["type"] == "flow_field_planar_grid": self.grid = FlowFieldPlanarGrid( turbine_coordinates=self.farm.coordinates, - reference_turbine_diameter=self.farm.rotor_diameters, + turbine_diameters=self.farm.rotor_diameters, wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, normal_vector=self.solver["normal_vector"], @@ -196,11 +201,10 @@ def check_deprecated_inputs(self): "\n\n".join(error_messages) ) - # @profile def initialize_domain(self): """Initialize solution space prior to wake calculations""" - # Initialize field quanitities; doing this immediately prior to doing + # Initialize field quantities; doing this immediately prior to doing # the calculation step allows for manipulating inputs in a script # without changing the data structures self.flow_field.initialize_velocity_field(self.grid) @@ -216,12 +220,9 @@ def steady_state_atmospheric_condition(self): vel_model = self.wake.model_strings["velocity_model"] - # <> - # start = time.time() - if vel_model in ["gauss", "cc", "turbopark", "jensen"] and \ self.farm.correct_cp_ct_for_tilt.any(): - self.logger.warn( + self.logger.warning( "The current model does not account for vertical wake deflection due to " + "tilt. Corrections to Cp and Ct can be included, but no vertical wake " + "deflection will occur." @@ -262,11 +263,8 @@ def steady_state_atmospheric_condition(self): self.grid, self.wake ) - # end = time.time() - # elapsed_time = end - start self.finalize() - # return elapsed_time def solve_for_viz(self): # Do the calculation with the TurbineGrid for a single wind speed @@ -301,7 +299,7 @@ def solve_for_points(self, x, y, z): points_y=y, points_z=z, turbine_coordinates=self.farm.coordinates, - reference_turbine_diameter=self.farm.rotor_diameters, + turbine_diameters=self.farm.rotor_diameters, wind_directions=self.flow_field.wind_directions, wind_speeds=self.flow_field.wind_speeds, grid_resolution=1, @@ -326,6 +324,81 @@ def solve_for_points(self, x, y, z): return self.flow_field.u_sorted[:,:,:,0,0] # Remove turbine grid dimensions + def solve_for_velocity_deficit_profiles( + self, + direction: str, + downstream_dists: NDArrayFloat | list, + profile_range: NDArrayFloat | list, + resolution: int, + homogeneous_wind_speed: float, + ref_rotor_diameter: float, + x_start: float, + y_start: float, + reference_height: float, + ) -> list[pd.DataFrame]: + """ + Extract velocity deficit profiles. See + :py:meth:`~floris.tools.floris_interface.FlorisInterface.sample_velocity_deficit_profiles` + for more details. + """ + + # Create a grid that contains coordinates for all the sample points in all profiles. + # Effectively, this is a grid of parallel lines. + n_lines = len(downstream_dists) + + # Coordinate system (x1, x2, x3) is used to define the sample points. The origin is at + # (x_start, y_start, reference_height) and x1 is in the streamwise direction. + # The x1-coordinate is fixed for every line (every row in `x1`). + x1 = np.atleast_2d(downstream_dists).T * np.ones((n_lines, resolution)) + + if resolution == 1: + single_line = [0.0] + else: + single_line = np.linspace(profile_range[0], profile_range[1], resolution) + + if direction == 'cross-stream': + x2 = single_line * np.ones((n_lines, resolution)) + x3 = np.zeros((n_lines, resolution)) + elif direction == 'vertical': + x3 = single_line * np.ones((n_lines, resolution)) + x2 = np.zeros((n_lines, resolution)) + + # Find the coordinates of the sample points in the inertial frame (x, y, z). This is done + # through one rotation and one translation. + x, y, z = reverse_rotate_coordinates_rel_west( + self.flow_field.wind_directions, + x1[None, :, :], + x2[None, :, :], + x3[None, :, :], + x_center_of_rotation=0.0, + y_center_of_rotation=0.0, + ) + x = np.squeeze(x, axis=0) + x_start + y = np.squeeze(y, axis=0) + y_start + z = np.squeeze(z, axis=0) + reference_height + + u = self.solve_for_points(x.flatten(), y.flatten(), z.flatten()) + u = np.reshape(u[0, 0, :], (n_lines, resolution)) + velocity_deficit = (homogeneous_wind_speed - u) / homogeneous_wind_speed + + velocity_deficit_profiles = [] + + for i in range(n_lines): + df = pd.DataFrame( + { + 'x': x[i], + 'y': y[i], + 'z': z[i], + 'x1/D': x1[i]/ref_rotor_diameter, + 'x2/D': x2[i]/ref_rotor_diameter, + 'x3/D': x3[i]/ref_rotor_diameter, + 'velocity_deficit': velocity_deficit[i], + } + ) + velocity_deficit_profiles.append(df) + + return velocity_deficit_profiles + def finalize(self): # Once the wake calculation is finished, unsort the values to match # the user-supplied order of things. diff --git a/floris/simulation/flow_field.py b/floris/simulation/flow_field.py index 2781d8c12..305260c92 100644 --- a/floris/simulation/flow_field.py +++ b/floris/simulation/flow_field.py @@ -48,21 +48,25 @@ class FlowField(BaseClass): n_wind_speeds: int = field(init=False) n_wind_directions: int = field(init=False) - u_initial_sorted: NDArrayFloat = field(init=False, default=np.array([])) - v_initial_sorted: NDArrayFloat = field(init=False, default=np.array([])) - w_initial_sorted: NDArrayFloat = field(init=False, default=np.array([])) - u_sorted: NDArrayFloat = field(init=False, default=np.array([])) - v_sorted: NDArrayFloat = field(init=False, default=np.array([])) - w_sorted: NDArrayFloat = field(init=False, default=np.array([])) - u: NDArrayFloat = field(init=False, default=np.array([])) - v: NDArrayFloat = field(init=False, default=np.array([])) - w: NDArrayFloat = field(init=False, default=np.array([])) + u_initial_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + v_initial_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + w_initial_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + u_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + v_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + w_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + u: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + v: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + w: NDArrayFloat = field(init=False, factory=lambda: np.array([])) het_map: list = field(init=False, default=None) - dudz_initial_sorted: NDArrayFloat = field(init=False, default=np.array([])) + dudz_initial_sorted: NDArrayFloat = field(init=False, factory=lambda: np.array([])) - turbulence_intensity_field: NDArrayFloat = field(init=False, default=np.array([])) - turbulence_intensity_field_sorted: NDArrayFloat = field(init=False, default=np.array([])) - turbulence_intensity_field_sorted_avg: NDArrayFloat = field(init=False, default=np.array([])) + turbulence_intensity_field: NDArrayFloat = field(init=False, factory=lambda: np.array([])) + turbulence_intensity_field_sorted: NDArrayFloat = field( + init=False, factory=lambda: np.array([]) + ) + turbulence_intensity_field_sorted_avg: NDArrayFloat = field( + init=False, factory=lambda: np.array([]) + ) @wind_speeds.validator def wind_speeds_validator(self, instance: attrs.Attribute, value: NDArrayFloat) -> None: @@ -131,10 +135,13 @@ def initialize_velocity_field(self, grid: Grid) -> None: dwind_profile_plane = ( self.wind_shear * (1 / self.reference_wind_height) ** self.wind_shear - * (grid.z_sorted) ** (self.wind_shear - 1) + * np.power( + grid.z_sorted, + (self.wind_shear - 1), + where=grid.z_sorted != 0.0 + ) ) - - # If no hetergeneous inflow defined, then set all speeds ups to 1.0 + # If no heterogeneous inflow defined, then set all speeds ups to 1.0 if self.het_map is None: speed_ups = 1.0 diff --git a/floris/simulation/grid.py b/floris/simulation/grid.py index 80f711f72..42e70289d 100644 --- a/floris/simulation/grid.py +++ b/floris/simulation/grid.py @@ -22,6 +22,7 @@ import numpy as np from attrs import define, field +from floris.simulation import BaseClass from floris.type_dec import ( floris_array_converter, floris_float_type, @@ -31,19 +32,16 @@ from floris.utilities import ( reverse_rotate_coordinates_rel_west, rotate_coordinates_rel_west, - Vec3, ) @define -class Grid(ABC): +class Grid(ABC, BaseClass): """ Grid should establish domain bounds based on given criteria, and develop three arrays to contain components of the grid - locations in space. - - This could be generalized to any number of dimensions to be - used by perhaps a turbulence field. + locations in space. This could be generalized to any number + of dimensions to be used by perhaps a turbulence field. The grid will have to be reestablished for each wind direction since the planform area of the farm will be different. @@ -57,26 +55,26 @@ class Grid(ABC): all of these arrays are the same size Args: - turbine_coordinates (`list[Vec3]`): The series of turbine coordinate (`Vec3`) objects. - reference_turbine_diameter (:py:obj:`float`): A reference turbine's rotor diameter. - grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): Grid resolution with values - specific to each grid type. + turbine_coordinates (:py:obj:`NDArrayFloat`): The arrays of turbine coordinates as Numpy + arrays with shape (N coordinates, 3). + turbine_diameters (:py:obj:`NDArrayFloat`): The rotor diameters of each turbine. wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time series. + grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): Grid resolution with values + specific to each grid type. """ - turbine_coordinates: list[Vec3] = field() - reference_turbine_diameter: float - grid_resolution: int | Iterable = field() + turbine_coordinates: NDArrayFloat = field(converter=floris_array_converter) + turbine_diameters: NDArrayFloat = field(converter=floris_array_converter) wind_directions: NDArrayFloat = field(converter=floris_array_converter) wind_speeds: NDArrayFloat = field(converter=floris_array_converter) time_series: bool = field() + grid_resolution: int | Iterable = field() n_turbines: int = field(init=False) n_wind_speeds: int = field(init=False) n_wind_directions: int = field(init=False) - turbine_coordinates_array: NDArrayFloat = field(init=False) x_sorted: NDArrayFloat = field(init=False) y_sorted: NDArrayFloat = field(init=False) z_sorted: NDArrayFloat = field(init=False) @@ -85,18 +83,18 @@ class Grid(ABC): z_sorted_inertial_frame: NDArrayFloat = field(init=False) cubature_weights: NDArrayFloat = field(init=False, default=None) - def __attrs_post_init__(self) -> None: - self.turbine_coordinates_array = np.array([c.elements for c in self.turbine_coordinates]) - @turbine_coordinates.validator - def check_coordinates(self, instance: attrs.Attribute, value: list[Vec3]) -> None: + def check_coordinates(self, instance: attrs.Attribute, value: np.ndarray) -> None: """ - Ensures all elements are `Vec3` objects and keeps the `n_turbines` + Ensures all elements are Numpy arrays and keeps the `n_turbines` attribute up to date. """ - types = np.unique([isinstance(c, Vec3) for c in value]) + types = np.unique([isinstance(c, np.ndarray) for c in value]) if not all(types): - raise TypeError("'turbine_coordinates' must be `Vec3` objects.") + raise TypeError( + "'turbine_coordinates' must be `np.array` objects " + "with three components of type `float`." + ) self.n_turbines = len(value) @@ -116,7 +114,7 @@ def wind_directions_validator(self, instance: attrs.Attribute, value: NDArrayFlo @grid_resolution.validator def grid_resolution_validator(self, instance: attrs.Attribute, value: int | Iterable) -> None: # TODO move this to the grid types and off of the base class - """Check that grid resolution is given as int or Vec3 with int components.""" + """Check that grid resolution is given as appropriate for the chosen Grid-type.""" if isinstance(value, int) and \ isinstance(self, (TurbineGrid, TurbineCubatureGrid, PointsGrid)): return @@ -139,15 +137,16 @@ class TurbineGrid(Grid): """See `Grid` for more details. Args: - turbine_coordinates (`list[Vec3]`): The series of turbine coordinate (`Vec3`) objects. - reference_turbine_diameter (:py:obj:`float`): A reference turbine's rotor diameter. - grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): The number of points in each - direction of the square grid on the rotor plane. For example, grid_resolution=3 - creates a 3x3 grid within the rotor swept area. + turbine_coordinates (:py:obj:`NDArrayFloat`): The arrays of turbine coordinates as Numpy + arrays with shape (N coordinates, 3). + turbine_diameters (:py:obj:`NDArrayFloat`): The rotor diameters of each turbine. wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time series. + grid_resolution (:py:obj:`int`): The number of points in each + direction of the square grid on the rotor plane. For example, grid_resolution=3 + creates a 3x3 grid within the rotor swept area. """ # TODO: describe these and the differences between `sorted_indices` and `sorted_coord_indices` sorted_indices: NDArrayInt = field(init=False) @@ -158,7 +157,6 @@ class TurbineGrid(Grid): average_method = "cubic-mean" def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() self.set_grid() def set_grid(self) -> None: @@ -217,7 +215,7 @@ def set_grid(self) -> None: # These are the rotated coordinates of the wind turbines based on the wind direction x, y, z, self.x_center_of_rotation, self.y_center_of_rotation = rotate_coordinates_rel_west( self.wind_directions, - self.turbine_coordinates_array, + self.turbine_coordinates, ) # - **rloc** (*float, optional): A value, from 0 to 1, that determines @@ -227,7 +225,7 @@ def set_grid(self) -> None: # Create the data for the turbine grids radius_ratio = 0.5 - disc_area_radius = radius_ratio * self.reference_turbine_diameter / 2 + disc_area_radius = radius_ratio * self.turbine_diameters / 2 template_grid = np.ones( ( self.n_wind_directions, @@ -300,15 +298,16 @@ class TurbineCubatureGrid(Grid): a more accurate average velocity, thrust coefficient, and axial induction. Args: - turbine_coordinates (`list[Vec3]`): The list of turbine coordinates as `Vec3` objects. - reference_turbine_diameter (:py:obj:`float`): The reference turbine's rotor diameter. + turbine_coordinates (:py:obj:`NDArrayFloat`): The arrays of turbine coordinates as Numpy + arrays with shape (N coordinates, 3). + turbine_diameters (:py:obj:`NDArrayFloat`): The rotor diameters of each turbine. wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. - grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): The number of points to - include in the cubature method. This value must be in the range [1, 10], and the - corresponding cubature weights are set automatically. time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time series. + grid_resolution (:py:obj:`int`): The number of points to + include in the cubature method. This value must be in the range [1, 10], and the + corresponding cubature weights are set automatically. """ sorted_indices: NDArrayInt = field(init=False) sorted_coord_indices: NDArrayInt = field(init=False) @@ -318,7 +317,6 @@ class TurbineCubatureGrid(Grid): average_method = "simple-cubature" def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() self.set_grid() def set_grid(self) -> None: @@ -327,7 +325,7 @@ def set_grid(self) -> None: # These are the rotated coordinates of the wind turbines based on the wind direction x, y, z, self.x_center_of_rotation, self.y_center_of_rotation = rotate_coordinates_rel_west( self.wind_directions, - self.turbine_coordinates_array + self.turbine_coordinates ) # Coefficients @@ -359,8 +357,8 @@ def set_grid(self) -> None: _y = y[:, :, :, None, None] * template_grid _z = z[:, :, :, None, None] * template_grid for ti in range(self.n_turbines): - _y[:, :, ti, :, :] += yv[None, None, :, None]*self.reference_turbine_diameter[ti] / 2.0 - _z[:, :, ti, :, :] += zv[None, None, :, None]*self.reference_turbine_diameter[ti] / 2.0 + _y[:, :, ti, :, :] += yv[None, None, :, None]*self.turbine_diameters[ti] / 2.0 + _z[:, :, ti, :, :] += zv[None, None, :, None]*self.turbine_diameters[ti] / 2.0 # Sort the turbines at each wind direction @@ -465,22 +463,25 @@ def get_cubature_coefficients(cls, N: int): class FlowFieldGrid(Grid): """ Args: - grid_resolution (`Vec3`): The number of grid points to be created in each direction. - turbine_coordinates (`list[Vec3]`): The collection of turbine coordinate (`Vec3`) objects. - reference_turbine_diameter (:py:obj:`float`): The reference turbine's rotor diameter. - grid_resolution (:py:obj:`int`): The number of points on each turbine + turbine_coordinates (:py:obj:`NDArrayFloat`): The arrays of turbine coordinates as Numpy + arrays with shape (N coordinates, 3). + turbine_diameters (:py:obj:`NDArrayFloat`): The rotor diameters of each turbine. + wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. + wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. + time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time + series. + grid_resolution (:py:obj:`Iterable(int,)`): The number of grid points to create in each + planar direction. Must be 3 components for resolution in the x, y, and z directions. """ x_center_of_rotation: NDArrayFloat = field(init=False) y_center_of_rotation: NDArrayFloat = field(init=False) def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() self.set_grid() def set_grid(self) -> None: """ Create a structured grid for the entire flow field domain. - resolution: Vec3 Calculates the domain bounds for the current wake model. The bounds are calculated based on preset extents from the @@ -496,15 +497,15 @@ def set_grid(self) -> None: # These are the rotated coordinates of the wind turbines based on the wind direction x, y, z, self.x_center_of_rotation, self.y_center_of_rotation = rotate_coordinates_rel_west( self.wind_directions, - self.turbine_coordinates_array + self.turbine_coordinates ) # Construct the arrays storing the grid points eps = 0.01 - xmin = min(x[0,0]) - 2 * self.reference_turbine_diameter - xmax = max(x[0,0]) + 10 * self.reference_turbine_diameter - ymin = min(y[0,0]) - 2 * self.reference_turbine_diameter - ymax = max(y[0,0]) + 2 * self.reference_turbine_diameter + xmin = min(x[0,0]) - 2 * self.turbine_diameters + xmax = max(x[0,0]) + 10 * self.turbine_diameters + ymin = min(y[0,0]) - 2 * self.turbine_diameters + ymax = max(y[0,0]) + 2 * self.turbine_diameters zmin = 0 + eps zmax = 6 * max(z[0,0]) @@ -534,10 +535,17 @@ def set_grid(self) -> None: class FlowFieldPlanarGrid(Grid): """ Args: - grid_resolution (`Vec3`): The number of grid points to be created in each direction. - turbine_coordinates (`list[Vec3]`): The collection of turbine coordinate (`Vec3`) objects. - reference_turbine_diameter (:py:obj:`float`): The reference turbine's rotor diameter. - grid_resolution (:py:obj:`int`): The number of points on each turbine + turbine_coordinates (:py:obj:`NDArrayFloat`): The arrays of turbine coordinates as Numpy + arrays with shape (N coordinates, 3). + turbine_diameters (:py:obj:`NDArrayFloat`): The rotor diameters of each turbine. + wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. + wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. + time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time + series. + grid_resolution (:py:obj:`Iterable(int,)`): The number of grid points to create in each + planar direction. Must be 2 components for resolution in the x and y directions. + The z direction is set to 3 planes at -10.0, 0.0, and +10.0 relative to the + `planar_coordinate`. """ normal_vector: str = field() planar_coordinate: float = field() @@ -549,30 +557,26 @@ class FlowFieldPlanarGrid(Grid): unsorted_indices: NDArrayInt = field(init=False) def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() self.set_grid() def set_grid(self) -> None: """ Create a structured grid for the entire flow field domain. - resolution: Vec3 Calculates the domain bounds for the current wake model. The bounds are calculated based on preset extents from the given layout. The bounds consist of the minimum and maximum values in the x-, y-, and z-directions. - If the Curl model is used, the predefined bounds are always set. - First, sort the turbines so that we know the bounds in the correct orientation. Then, create the grid based on this wind-from-left orientation """ # These are the rotated coordinates of the wind turbines based on the wind direction x, y, z, self.x_center_of_rotation, self.y_center_of_rotation = rotate_coordinates_rel_west( self.wind_directions, - self.turbine_coordinates_array + self.turbine_coordinates ) - max_diameter = np.max(self.reference_turbine_diameter) + max_diameter = np.max(self.turbine_diameters) if self.normal_vector == "z": # Rules of thumb for horizontal plane if self.x1_bounds is None: @@ -648,14 +652,18 @@ def set_grid(self) -> None: class PointsGrid(Grid): """ Args: - turbine_coordinates (`list[Vec3]`): The list of turbine coordinates as `Vec3` objects. - reference_turbine_diameter (:py:obj:`float`): The reference turbine's rotor diameter. + turbine_coordinates (:py:obj:`NDArrayFloat`): Not used for PointsGrid, but + required for the `Grid` super-class. + turbine_diameters (:py:obj:`NDArrayFloat`): Not used for PointsGrid, but + required for the `Grid` super-class. wind_directions (:py:obj:`NDArrayFloat`): Wind directions supplied by the user. - wind_speeds (:py:obj:`NDArrayFloat`): Wind speeds supplied by the user. + wind_speeds (:py:obj:`NDArrayFloat`): Not used for PointsGrid, but + required for the `Grid` super-class. + time_series (:py:obj:`bool`): Not used for PointsGrid, but + required for the `Grid` super-class. grid_resolution (:py:obj:`int` | :py:obj:`Iterable(int,)`): Not used for PointsGrid, but required for the `Grid` super-class. - time_series (:py:obj:`bool`): Flag to indicate whether the supplied wind data is a time - series. + points_x (:py:obj:`NDArrayFloat`): Array of x-components for the points in the grid. points_y (:py:obj:`NDArrayFloat`): Array of y-components for the points in the grid. points_z (:py:obj:`NDArrayFloat`): Array of z-components for the points in the grid. @@ -675,7 +683,6 @@ class PointsGrid(Grid): y_center_of_rotation: float | None = field(default=None) def __attrs_post_init__(self) -> None: - super().__attrs_post_init__() self.set_grid() def set_grid(self) -> None: diff --git a/floris/simulation/solver.py b/floris/simulation/solver.py index 6e53a718a..f173a96e7 100644 --- a/floris/simulation/solver.py +++ b/floris/simulation/solver.py @@ -107,7 +107,7 @@ def sequential_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -123,7 +123,7 @@ def sequential_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -282,14 +282,13 @@ def full_flow_sequential_solver( turbine_grid_farm.construct_turbine_pTs() turbine_grid_farm.construct_turbine_ref_density_cp_cts() turbine_grid_farm.construct_turbine_ref_tilt_cp_cts() - turbine_grid_farm.construct_turbine_fTilts() + turbine_grid_farm.construct_turbine_tilt_interps() turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt() - turbine_grid_farm.construct_coordinates() turbine_grid_farm.set_tilt_to_ref_tilt(flow_field.n_wind_directions, flow_field.n_wind_speeds) turbine_grid = TurbineGrid( turbine_coordinates=turbine_grid_farm.coordinates, - reference_turbine_diameter=turbine_grid_farm.rotor_diameters, + turbine_diameters=turbine_grid_farm.rotor_diameters, wind_directions=turbine_grid_flow_field.wind_directions, wind_speeds=turbine_grid_flow_field.wind_speeds, grid_resolution=3, @@ -340,7 +339,7 @@ def full_flow_sequential_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, ix_filter=[i], @@ -354,7 +353,7 @@ def full_flow_sequential_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, ix_filter=[i], @@ -502,7 +501,7 @@ def cc_solver( farm.tilt_angles_sorted, farm.ref_tilt_cp_cts_sorted, farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, average_method=grid.average_method, @@ -515,7 +514,7 @@ def cc_solver( farm.tilt_angles_sorted, farm.ref_tilt_cp_cts_sorted, farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -533,7 +532,7 @@ def cc_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -688,14 +687,13 @@ def full_flow_cc_solver( turbine_grid_farm.construct_turbine_pTs() turbine_grid_farm.construct_turbine_ref_density_cp_cts() turbine_grid_farm.construct_turbine_ref_tilt_cp_cts() - turbine_grid_farm.construct_turbine_fTilts() + turbine_grid_farm.construct_turbine_tilt_interps() turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt() - turbine_grid_farm.construct_coordinates() turbine_grid_farm.set_tilt_to_ref_tilt(flow_field.n_wind_directions, flow_field.n_wind_speeds) turbine_grid = TurbineGrid( turbine_coordinates=turbine_grid_farm.coordinates, - reference_turbine_diameter=turbine_grid_farm.rotor_diameters, + turbine_diameters=turbine_grid_farm.rotor_diameters, wind_directions=turbine_grid_flow_field.wind_directions, wind_speeds=turbine_grid_flow_field.wind_speeds, grid_resolution=3, @@ -750,7 +748,7 @@ def full_flow_cc_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, average_method=turbine_grid.average_method, @@ -764,7 +762,7 @@ def full_flow_cc_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, ix_filter=[i], @@ -901,7 +899,7 @@ def turbopark_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, average_method=grid.average_method, @@ -914,7 +912,7 @@ def turbopark_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -930,7 +928,7 @@ def turbopark_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -967,7 +965,7 @@ def turbopark_solver( # Model calculations # NOTE: exponential - if not np.all(farm.yaw_angles_sorted): + if np.any(farm.yaw_angles_sorted): model_manager.deflection_model.logger.warning( "WARNING: Deflection with the TurbOPark model has not been fully validated." "This is an initial implementation, and we advise you use at your own risk" @@ -987,7 +985,7 @@ def turbopark_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[ii], @@ -1125,11 +1123,10 @@ def full_flow_turbopark_solver( # turbine_grid_farm.construct_rotor_diameters() # turbine_grid_farm.construct_turbine_TSRs() # turbine_grid_farm.construc_turbine_pPs() - # turbine_grid_farm.construct_coordinates() # turbine_grid = TurbineGrid( # turbine_coordinates=turbine_grid_farm.coordinates, - # reference_turbine_diameter=turbine_grid_farm.rotor_diameters, + # turbine_diameters=turbine_grid_farm.rotor_diameters, # wind_directions=turbine_grid_flow_field.wind_directions, # wind_speeds=turbine_grid_flow_field.wind_speeds, # grid_resolution=11, @@ -1220,7 +1217,7 @@ def empirical_gauss_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -1236,7 +1233,7 @@ def empirical_gauss_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=farm.turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -1366,14 +1363,13 @@ def full_flow_empirical_gauss_solver( turbine_grid_farm.construct_turbine_pTs() turbine_grid_farm.construct_turbine_ref_density_cp_cts() turbine_grid_farm.construct_turbine_ref_tilt_cp_cts() - turbine_grid_farm.construct_turbine_fTilts() + turbine_grid_farm.construct_turbine_tilt_interps() turbine_grid_farm.construct_turbine_correct_cp_ct_for_tilt() - turbine_grid_farm.construct_coordinates() turbine_grid_farm.set_tilt_to_ref_tilt(flow_field.n_wind_directions, flow_field.n_wind_speeds) turbine_grid = TurbineGrid( turbine_coordinates=turbine_grid_farm.coordinates, - reference_turbine_diameter=turbine_grid_farm.rotor_diameters, + turbine_diameters=turbine_grid_farm.rotor_diameters, wind_directions=turbine_grid_flow_field.wind_directions, wind_speeds=turbine_grid_flow_field.wind_speeds, grid_resolution=3, @@ -1425,7 +1421,7 @@ def full_flow_empirical_gauss_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, ix_filter=[i], @@ -1439,7 +1435,7 @@ def full_flow_empirical_gauss_solver( tilt_angle=turbine_grid_farm.tilt_angles_sorted, ref_tilt_cp_ct=turbine_grid_farm.ref_tilt_cp_cts_sorted, fCt=turbine_grid_farm.turbine_fCts, - tilt_interp=turbine_grid_farm.turbine_fTilts, + tilt_interp=turbine_grid_farm.turbine_tilt_interps, correct_cp_ct_for_tilt=turbine_grid_farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=turbine_grid_farm.turbine_type_map_sorted, ix_filter=[i], @@ -1562,7 +1558,7 @@ def sequential_multidim_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=downselect_turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], @@ -1578,7 +1574,7 @@ def sequential_multidim_solver( tilt_angle=farm.tilt_angles_sorted, ref_tilt_cp_ct=farm.ref_tilt_cp_cts_sorted, fCt=downselect_turbine_fCts, - tilt_interp=farm.turbine_fTilts, + tilt_interp=farm.turbine_tilt_interps, correct_cp_ct_for_tilt=farm.correct_cp_ct_for_tilt_sorted, turbine_type_map=farm.turbine_type_map_sorted, ix_filter=[i], diff --git a/floris/simulation/turbine.py b/floris/simulation/turbine.py index 0c056322d..b99ba1906 100644 --- a/floris/simulation/turbine.py +++ b/floris/simulation/turbine.py @@ -16,7 +16,6 @@ import copy from collections.abc import Iterable -from typing import Any import attrs import numpy as np @@ -25,8 +24,7 @@ from floris.simulation import BaseClass from floris.type_dec import ( - floris_array_converter, - FromDictMixin, + floris_numeric_dict_converter, NDArrayBool, NDArrayFilter, NDArrayFloat, @@ -36,51 +34,6 @@ from floris.utilities import cosd -def _filter_convert( - ix_filter: NDArrayFilter | Iterable[int] | None, sample_arg: NDArrayFloat | NDArrayInt -) -> NDArrayFloat | None: - """This function selects turbine indeces from the given array of turbine properties - over the simulation's atmospheric conditions (wind directions / wind speeds). - It converts the ix_filter to a standard format of `np.ndarray`s for filtering - certain arguments. - - Args: - ix_filter (NDArrayFilter | Iterable[int] | None): The indices, or truth - array-like object to use for filtering. None implies that all indeces in the - sample_arg should be selected. - sample_arg (NDArrayFloat | NDArrayInt): Any argument that will be filtered, to be used for - creating the shape. This should be of shape: - (n wind directions, n wind speeds, n turbines) - - Returns: - NDArrayFloat | None: Returns an array of a truth or index list if a list is - passed, a truth array if ix_filter is None, or None if ix_filter is None - and the `sample_arg` is a single value. - """ - # Check that the ix_filter is either None or an Iterable. Otherwise, - # there's nothing we can do with it. - if not isinstance(ix_filter, Iterable) and ix_filter is not None: - raise TypeError("Expected ix_filter to be an Iterable or None") - - # Check that the sample_arg is a Numpy array. If it isn't, we - # can't get its shape. - if not isinstance(sample_arg, np.ndarray): - raise TypeError("Expected sample_arg to be a float or integer np.ndarray") - - # At this point, the arguments have this type: - # ix_filter: Union[Iterable, None] - # sample_arg: np.ndarray - - # Return all values in the turbine-dimension - # if the index filter is None - if ix_filter is None: - return np.ones(sample_arg.shape[-1], dtype=bool) - - # Finally, we should have an index filter list of type Iterable, - # so cast it to Numpy array and return - return np.array(ix_filter) - - def _rotor_velocity_yaw_correction( pP: float, yaw_angle: NDArrayFloat, @@ -114,22 +67,19 @@ def _rotor_velocity_tilt_correction( tilt_angle = np.where(correct_cp_ct_for_tilt, tilt_angle, old_tilt_angle) # Compute the rotor effective velocity adjusting for tilt - rotor_effective_velocities = ( - rotor_effective_velocities - * cosd(tilt_angle - ref_tilt_cp_ct) ** (pT / 3.0) - ) + relative_tilt = tilt_angle - ref_tilt_cp_ct + rotor_effective_velocities = rotor_effective_velocities * cosd(relative_tilt) ** (pT / 3.0) return rotor_effective_velocities def compute_tilt_angles_for_floating_turbines( turbine_type_map: NDArrayObject, tilt_angle: NDArrayFloat, - tilt_interp: NDArrayObject, + tilt_interp: dict[str, interp1d], rotor_effective_velocities: NDArrayFloat, ) -> NDArrayFloat: # Loop over each turbine type given to get tilt angles for all turbines tilt_angles = np.zeros(np.shape(rotor_effective_velocities)) - tilt_interp = dict(tilt_interp) turb_types = np.unique(turbine_type_map) for turb_type in turb_types: # If no tilt interpolation is specified, assume no modification to tilt @@ -148,7 +98,7 @@ def compute_tilt_angles_for_floating_turbines( # TODO: Not sure if this is the best way to do this? Basically replaces the initialized # tilt_angles if there are non-zero tilt angles calculated above (meaning that the turbine # definition contained a wind_speed/tilt table definition) - if not tilt_angles.all() == 0.: + if not tilt_angles.all() == 0.0: tilt_angle = tilt_angles return tilt_angle @@ -178,7 +128,6 @@ def rotor_effective_velocity( # Down-select inputs if ix_filter is given if ix_filter is not None: - ix_filter = _filter_convert(ix_filter, yaw_angle) velocities = velocities[:, :, ix_filter] yaw_angle = yaw_angle[:, :, ix_filter] tilt_angle = tilt_angle[:, :, ix_filter] @@ -219,7 +168,7 @@ def rotor_effective_velocity( def power( ref_density_cp_ct: float, rotor_effective_velocities: NDArrayFloat, - power_interp: NDArrayObject, + power_interp: dict[str, interp1d], turbine_type_map: NDArrayObject, ix_filter: NDArrayInt | Iterable[int] | None = None, ) -> NDArrayFloat: @@ -228,10 +177,10 @@ def power( Args: ref_density_cp_cts (NDArrayFloat[wd, ws, turbines]): The reference density for each turbine - rotor_effective_velocities (NDArrayFloat[wd, ws, turbines, grid1, grid2]): The rotor + rotor_effective_velocities (NDArrayFloat[wd, ws, turbines]): The rotor effective velocities at a turbine. - power_interp (NDArrayObject[wd, ws, turbines]): The power interpolation function - for each turbine. + power_interp (dict[str, interp1d]): A dictionary of power interpolation functions for + each turbine type. turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition for each turbine. ix_filter (NDArrayInt, optional): The boolean array, or @@ -255,7 +204,6 @@ def power( # Down-select inputs if ix_filter is given if ix_filter is not None: - ix_filter = _filter_convert(ix_filter, rotor_effective_velocities) rotor_effective_velocities = rotor_effective_velocities[:, :, ix_filter] turbine_type_map = turbine_type_map[:, :, ix_filter] @@ -265,10 +213,7 @@ def power( for turb_type in turb_types: # Using a masked array, apply the thrust coefficient for all turbines of the current # type to the main thrust coefficient array - p += ( - power_interp[turb_type](rotor_effective_velocities) - * (turbine_type_map == turb_type) - ) + p += power_interp[turb_type](rotor_effective_velocities) * (turbine_type_map == turb_type) return p * ref_density_cp_ct @@ -322,7 +267,6 @@ def Ct( # Down-select inputs if ix_filter is given if ix_filter is not None: - ix_filter = _filter_convert(ix_filter, yaw_angle) velocities = velocities[:, :, ix_filter] yaw_angle = yaw_angle[:, :, ix_filter] tilt_angle = tilt_angle[:, :, ix_filter] @@ -394,7 +338,7 @@ def axial_induction( turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition for each turbine. ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or - integer indices (as an aray or iterable) to filter out before calculation. + integer indices (as an array or iterable) to filter out before calculation. Defaults to None. Returns: @@ -425,7 +369,6 @@ def axial_induction( ) # Then, process the input arguments as needed for this function - ix_filter = _filter_convert(ix_filter, yaw_angle) if ix_filter is not None: yaw_angle = yaw_angle[:, :, ix_filter] tilt_angle = tilt_angle[:, :, ix_filter] @@ -509,112 +452,42 @@ def average_velocity( raise ValueError("Incorrect method given.") @define -class PowerThrustTable(FromDictMixin): - """Helper class to convert the dictionary and list-based inputs to a object of arrays. - - Args: - power (NDArrayFloat): The power produced at a given wind speed. - thrust (NDArrayFloat): The thrust at a given wind speed. - wind_speed (NDArrayFloat): Wind speed values, m/s. - - Raises: - ValueError: Raised if the power, thrust, and wind_speed are not all 1-d array-like shapes. - ValueError: Raised if power, thrust, and wind_speed don't have the same number of values. +class Turbine(BaseClass): """ - power: NDArrayFloat = field(default=[], converter=floris_array_converter) - thrust: NDArrayFloat = field(default=[], converter=floris_array_converter) - wind_speed: NDArrayFloat = field(default=[], converter=floris_array_converter) - - def __attrs_post_init__(self) -> None: - # Validate the power, thrust, and wind speed inputs. - - inputs = (self.power, self.thrust, self.wind_speed) - - if any(el.ndim > 1 for el in inputs): - raise ValueError("power, thrust, and wind_speed inputs must be 1-D.") - - if len( {self.power.size, self.thrust.size, self.wind_speed.size} ) > 1: - raise ValueError("power, thrust, and wind_speed tables must be the same size.") - - # Remove any duplicate wind speed entries - _, duplicate_filter = np.unique(self.wind_speed, return_index=True) - self.power = self.power[duplicate_filter] - self.thrust = self.thrust[duplicate_filter] - self.wind_speed = self.wind_speed[duplicate_filter] - - -@define -class TiltTable(FromDictMixin): - """Helper class to convert the dictionary and list-based inputs to a object of arrays. + A class containing the parameters and infrastructure to model a wind turbine's performance + for a particular atmospheric condition. Args: - tilt (NDArrayFloat): The tilt angle at a given wind speed. - wind_speeds (NDArrayFloat): Wind speed values, m/s. - - Raises: - ValueError: Raised if tilt and wind_speeds are not all 1-d array-like shapes. - ValueError: Raised if tilt and wind_speeds don't have the same number of values. + turbine_type (str): An identifier for this type of turbine such as "NREL_5MW". + rotor_diameter (float): The rotor diameter in meters. + hub_height (float): The hub height in meters. + pP (float): The cosine exponent relating the yaw misalignment angle to turbine power. + pT (float): The cosine exponent relating the rotor tilt angle to turbine power. + TSR (float): The Tip Speed Ratio of the turbine. + generator_efficiency (float): The efficiency of the generator used to scale + power production. + ref_density_cp_ct (float): The density at which the provided Cp and Ct curves are defined. + ref_tilt_cp_ct (float): The implicit tilt of the turbine for which the Cp and Ct + curves are defined. This is typically the nacelle tilt. + power_thrust_table (dict[str, float]): Contains power coefficient and thrust coefficient + values at a series of wind speeds to define the turbine performance. + The dictionary must have the following three keys with equal length values: + { + "wind_speeds": List[float], + "power": List[float], + "thrust": List[float], + } + correct_cp_ct_for_tilt (bool): A flag to indicate whether to correct Cp and Ct for tilt + usually for a floating turbine. + Optional, defaults to False. + floating_tilt_table (dict[str, float]): Look up table of tilt angles at a series of + wind speeds. The dictionary must have the following keys with equal length values: + { + "wind_speeds": List[float], + "tilt": List[float], + } + Required if `correct_cp_ct_for_tilt = True`. Defaults to None. """ - tilt: NDArrayFloat = field(converter=floris_array_converter) - wind_speeds: NDArrayFloat = field(converter=floris_array_converter) - - def __attrs_post_init__(self) -> None: - # Validate the power, thrust, and wind speed inputs. - - inputs = (self.tilt, self.wind_speeds) - - if any(el.ndim > 1 for el in inputs): - raise ValueError("tilt and wind_speed inputs must be 1-D.") - - if len({self.tilt.size, self.wind_speeds.size}) > 1: - raise ValueError("tilt and wind_speed tables must be the same size.") - - # Remove any duplicate wind speed entries - _, duplicate_filter = np.unique(self.wind_speeds, return_index=True) - self.tilt = self.tilt[duplicate_filter] - self.wind_speeds = self.wind_speeds[duplicate_filter] - - -@define -class Turbine(BaseClass): - """ - Turbine is a class containing objects pertaining to the individual - turbines. - - Turbine is a model class representing a particular wind turbine. It - is largely a container of data and parameters, but also contains - methods to probe properties for output. - - Parameters: - rotor_diameter (:py:obj: float): The rotor diameter (m). - hub_height (:py:obj: float): The hub height (m). - pP (:py:obj: float): The cosine exponent relating the yaw - misalignment angle to power. - pT (:py:obj: float): The cosine exponent relating the rotor - tilt angle to power. - generator_efficiency (:py:obj: float): The generator - efficiency factor used to scale the power production. - ref_density_cp_ct (:py:obj: float): The density at which the provided - cp and ct is defined - power_thrust_table (PowerThrustTable): A dictionary containing the - following key-value pairs: - - power (:py:obj: List[float]): The coefficient of power at - different wind speeds. - thrust (:py:obj: List[float]): The coefficient of thrust - at different wind speeds. - wind_speed (:py:obj: List[float]): The wind speeds for - which the power and thrust values are provided (m/s). - ngrid (*int*, optional): The square root of the number - of points to use on the turbine grid. This number will be - squared so that the points can be evenly distributed. - Defaults to 5. - rloc (:py:obj: float, optional): A value, from 0 to 1, that determines - the width/height of the grid of points on the rotor as a ratio of - the rotor radius. - Defaults to 0.5. - """ - turbine_type: str = field() rotor_diameter: float = field() hub_height: float = field() @@ -624,51 +497,56 @@ class Turbine(BaseClass): generator_efficiency: float = field() ref_density_cp_ct: float = field() ref_tilt_cp_ct: float = field() - power_thrust_table: PowerThrustTable = field(default=None) - floating_tilt_table: TiltTable = field(default=None) - floating_correct_cp_ct_for_tilt: bool = field(default=None) - power_thrust_data_file: str = field(default=None) - multi_dimensional_cp_ct: bool = field(default=False) + power_thrust_table: dict[str, NDArrayFloat] = field(converter=floris_numeric_dict_converter) - # rloc: float = float_attrib() # TODO: goes here or on the Grid? - # use_points_on_perimeter: bool = bool_attrib() + correct_cp_ct_for_tilt: bool = field(default=False) + floating_tilt_table: dict[str, NDArrayFloat] | None = field(default=None) + + # Even though this Turbine class does not support the multidimensional features as they + # are implemented in TurbineMultiDim, providing the following two attributes here allows + # the turbine data inputs to keep the multidimensional Cp and Ct curve but switch them off + # with multi_dimensional_cp_ct = False + multi_dimensional_cp_ct: bool = field(default=False) + power_thrust_data_file: str = field(default=None) # Initialized in the post_init function rotor_radius: float = field(init=False) rotor_area: float = field(init=False) - fCp_interp: interp1d = field(init=False) fCt_interp: interp1d = field(init=False) power_interp: interp1d = field(init=False) - tilt_interp: interp1d = field(init=False) + tilt_interp: interp1d = field(init=False, default=None) + def __attrs_post_init__(self) -> None: + self._initialize_power_thrust_interpolation() + self.__post_init__() - # For the following parameters, use default values if not user-specified - # self.rloc = float(input_dictionary["rloc"]) if "rloc" in input_dictionary else 0.5 - # if "use_points_on_perimeter" in input_dictionary: - # self.use_points_on_perimeter = bool(input_dictionary["use_points_on_perimeter"]) - # else: - # self.use_points_on_perimeter = False + def __post_init__(self) -> None: + self._initialize_tilt_interpolation() - def __attrs_post_init__(self) -> None: + def _initialize_power_thrust_interpolation(self) -> None: + # TODO This validation for the power thrust tables should go in the turbine library + # since it's preprocessing + # Remove any duplicate wind speed entries + # _, duplicate_filter = np.unique(self.wind_speed, return_index=True) + # self.power = self.power[duplicate_filter] + # self.thrust = self.thrust[duplicate_filter] + # self.wind_speed = self.wind_speed[duplicate_filter] - # Post-init initialization for the power curve interpolation functions - self.power_thrust_table = PowerThrustTable.from_dict(self.power_thrust_table) - wind_speeds = self.power_thrust_table.wind_speed - self.fCp_interp = interp1d( + wind_speeds = self.power_thrust_table["wind_speed"] + cp_interp = interp1d( wind_speeds, - self.power_thrust_table.power, + self.power_thrust_table["power"], fill_value=(0.0, 1.0), bounds_error=False, ) - inner_power = ( - 0.5 * self.rotor_area - * self.fCp_interp(wind_speeds) - * self.generator_efficiency - * wind_speeds ** 3 - ) self.power_interp = interp1d( wind_speeds, - inner_power, + ( + 0.5 * self.rotor_area + * cp_interp(wind_speeds) + * self.generator_efficiency + * wind_speeds ** 3 + ), bounds_error=False, fill_value=0 ) @@ -685,28 +563,55 @@ def __attrs_post_init__(self) -> None: """ self.fCt_interp = interp1d( wind_speeds, - self.power_thrust_table.thrust, + self.power_thrust_table["thrust"], fill_value=(0.0001, 0.9999), bounds_error=False, ) + def _initialize_tilt_interpolation(self) -> None: + # TODO: + # Remove any duplicate wind speed entries + # _, duplicate_filter = np.unique(self.wind_speeds, return_index=True) + # self.tilt = self.tilt[duplicate_filter] + # self.wind_speeds = self.wind_speeds[duplicate_filter] + + if self.floating_tilt_table is not None: + self.floating_tilt_table = floris_numeric_dict_converter(self.floating_tilt_table) + # If defined, create a tilt interpolation function for floating turbines. # fill_value currently set to apply the min or max tilt angles if outside # of the interpolation range. - if self.floating_tilt_table is not None: - self.floating_tilt_table = TiltTable.from_dict(self.floating_tilt_table) - self.fTilt_interp = interp1d( - self.floating_tilt_table.wind_speeds, - self.floating_tilt_table.tilt, - fill_value=(0.0, self.floating_tilt_table.tilt[-1]), + if self.correct_cp_ct_for_tilt: + self.tilt_interp = interp1d( + self.floating_tilt_table["wind_speed"], + self.floating_tilt_table["tilt"], + fill_value=(0.0, self.floating_tilt_table["tilt"][-1]), bounds_error=False, ) - self.tilt_interp = self.fTilt_interp - self.correct_cp_ct_for_tilt = self.floating_correct_cp_ct_for_tilt - else: - self.fTilt_interp = None - self.tilt_interp = None - self.correct_cp_ct_for_tilt = False + + @power_thrust_table.validator + def check_power_thrust_table(self, instance: attrs.Attribute, value: dict) -> None: + """ + Verify that the power and thrust tables are given with arrays of equal length + to the wind speed array. + """ + if len(value.keys()) != 3 or set(value.keys()) != {"wind_speed", "power", "thrust"}: + raise ValueError( + """ + power_thrust_table dictionary must have the form: + { + "wind_speed": List[float], + "power": List[float], + "thrust": List[float], + } + """ + ) + + if any(e.ndim > 1 for e in (value["power"], value["thrust"], value["wind_speed"])): + raise ValueError("power, thrust, and wind_speed inputs must be 1-D.") + + if len( {value["power"].size, value["thrust"].size, value["wind_speed"].size} ) > 1: + raise ValueError("power, thrust, and wind_speed tables must be the same size.") @rotor_diameter.validator def reset_rotor_diameter_dependencies(self, instance: attrs.Attribute, value: float) -> None: @@ -734,33 +639,42 @@ def reset_rotor_area(self, instance: attrs.Attribute, value: float) -> None: self.rotor_radius = (value / np.pi) ** 0.5 @floating_tilt_table.validator - def check_floating_tilt_table(self, instance: attrs.Attribute, value: Any) -> None: + def check_floating_tilt_table(self, instance: attrs.Attribute, value: dict | None) -> None: """ - Check that if the tile/wind_speed table is defined, that the tilt and - wind_speed arrays are the same length so that the interpolation will work. + If the tilt / wind_speed table is defined, verify that the tilt and + wind_speed arrays are the same length. """ - if self.floating_tilt_table is not None: - if ( - len(self.floating_tilt_table["tilt"]) - != len(self.floating_tilt_table["wind_speeds"]) - ): - raise ValueError( - "tilt and wind_speeds must be the same length for the interpolation to work." - ) - - @floating_correct_cp_ct_for_tilt.validator + if value is None: + return + + if len(value.keys()) != 2 or set(value.keys()) != {"wind_speed", "tilt"}: + raise ValueError( + """ + floating_tilt_table dictionary must have the form: + { + "wind_speed": List[float], + "tilt": List[float], + } + """ + ) + + if any(len(np.shape(e)) > 1 for e in (value["tilt"], value["wind_speed"])): + raise ValueError("tilt and wind_speed inputs must be 1-D.") + + if len( {len(value["tilt"]), len(value["wind_speed"])} ) > 1: + raise ValueError("tilt and wind_speed inputs must be the same size.") + + @correct_cp_ct_for_tilt.validator def check_for_cp_ct_correct_flag_if_floating( self, instance: attrs.Attribute, - value: Any + value: bool ) -> None: """ Check that the boolean flag exists for correcting Cp/Ct for tilt if a tile/wind_speed table is also defined. """ - if self.floating_tilt_table is not None: - if self.floating_correct_cp_ct_for_tilt is None: - raise ValueError( - "If a floating tilt/wind_speed table is defined, the boolean flag" - "floating_correct_cp_ct_for_tilt must also be defined." - ) + if self.correct_cp_ct_for_tilt and self.floating_tilt_table is None: + raise ValueError( + "To enable the Cp and Ct tilt correction, a tilt table must be given." + ) diff --git a/floris/simulation/turbine_multi_dim.py b/floris/simulation/turbine_multi_dim.py index 3e2cc7b8d..d101462a8 100644 --- a/floris/simulation/turbine_multi_dim.py +++ b/floris/simulation/turbine_multi_dim.py @@ -16,22 +16,22 @@ import copy from collections.abc import Iterable +from pathlib import Path +import attrs import numpy as np import pandas as pd from attrs import define, field from flatten_dict import flatten from scipy.interpolate import interp1d -# import floris.simulation.turbine as turbine from floris.simulation import ( average_velocity, compute_tilt_angles_for_floating_turbines, - TiltTable, Turbine, ) -from floris.simulation.turbine import _filter_convert from floris.type_dec import ( + convert_to_path, NDArrayBool, NDArrayFilter, NDArrayFloat, @@ -77,7 +77,6 @@ def power_multidim( # Down-select inputs if ix_filter is given if ix_filter is not None: - ix_filter = _filter_convert(ix_filter, rotor_effective_velocities) power_interp = power_interp[:, :, ix_filter] rotor_effective_velocities = rotor_effective_velocities[:, :, ix_filter] # Loop over each turbine to get power for all turbines @@ -139,7 +138,6 @@ def Ct_multidim( # Down-select inputs if ix_filter is given if ix_filter is not None: - ix_filter = _filter_convert(ix_filter, yaw_angle) velocities = velocities[:, :, ix_filter] yaw_angle = yaw_angle[:, :, ix_filter] tilt_angle = tilt_angle[:, :, ix_filter] @@ -207,7 +205,7 @@ def axial_induction_multidim( turbine_type_map: (NDArrayObject[wd, ws, turbines]): The Turbine type definition for each turbine. ix_filter (NDArrayFilter | Iterable[int] | None, optional): The boolean array, or - integer indices (as an aray or iterable) to filter out before calculation. + integer indices (as an array or iterable) to filter out before calculation. Defaults to None. Returns: @@ -238,7 +236,6 @@ def axial_induction_multidim( ) # Then, process the input arguments as needed for this function - ix_filter = _filter_convert(ix_filter, yaw_angle) if ix_filter is not None: yaw_angle = yaw_angle[:, :, ix_filter] tilt_angle = tilt_angle[:, :, ix_filter] @@ -270,7 +267,7 @@ def multidim_Ct_down_select( conditions (dict): The conditions at which to determine which Ct interpolant to use. Returns: - NDArray: The downselected Ct interpolants for the selected conditions. + NDArray: The down selected Ct interpolants for the selected conditions. """ downselect_turbine_fCts = np.empty_like(turbine_fCts) # Loop over the wind directions, wind speeds, and turbines, finding the Ct interpolant @@ -307,7 +304,7 @@ def multidim_power_down_select( conditions (dict): The conditions at which to determine which Ct interpolant to use. Returns: - NDArray: The downselected power interpolants for the selected conditions. + NDArray: The down selected power interpolants for the selected conditions. """ downselect_power_interps = np.empty_like(power_interps) # Loop over the wind directions, wind speeds, and turbines, finding the power interpolant @@ -422,31 +419,36 @@ class TurbineMultiDimensional(Turbine): the width/height of the grid of points on the rotor as a ratio of the rotor radius. Defaults to 0.5. + power_thrust_data_file (:py:obj:`str`): The path and name of the file containing the + multidimensional power thrust curve. The path may be an absolute location or a relative + path to where FLORIS is being run. + multi_dimensional_cp_ct (:py:obj:`bool`, optional): Indicates if the turbine definition is + single dimensional (False) or multidimensional (True). + turbine_library_path (:py:obj:`pathlib.Path`, optional): The + :py:attr:`Farm.turbine_library_path` or :py:attr:`Farm.internal_turbine_library_path`, + whichever is being used to load turbine definitions. + Defaults to the internal turbine library. """ - - power_thrust_data_file: str = field(default=None) multi_dimensional_cp_ct: bool = field(default=False) + power_thrust_table: dict = field(default={}) + # TODO power_thrust_data_file is actually required and should not default to None. + # However, the super class has optional attributes so a required attribute here breaks + power_thrust_data_file: str = field(default=None) + power_thrust_data: MultiDimensionalPowerThrustTable = field(default=None) + turbine_library_path: Path = field( + default=Path(__file__).parents[1] / "turbine_library", + converter=convert_to_path, + validator=attrs.validators.instance_of(Path) + ) - # rloc: float = float_attrib() # TODO: goes here or on the Grid? - # use_points_on_perimeter: bool = bool_attrib() - - # Initialized in the post_init function - # rotor_radius: float = field(init=False) - # rotor_area: float = field(init=False) - # fCp_interp: interp1d = field(init=False) - # fCt_interp: interp1d = field(init=False) - # power_interp: interp1d = field(init=False) - # tilt_interp: interp1d = field(init=False) - - - # For the following parameters, use default values if not user-specified - # self.rloc = float(input_dictionary["rloc"]) if "rloc" in input_dictionary else 0.5 - # if "use_points_on_perimeter" in input_dictionary: - # self.use_points_on_perimeter = bool(input_dictionary["use_points_on_perimeter"]) - # else: - # self.use_points_on_perimeter = False + # Not to be provided by the user + condition_keys: list[str] = field(init=False, factory=list) def __attrs_post_init__(self) -> None: + super().__post_init__() + + # Solidify the data file path and name + self.power_thrust_data_file = self.turbine_library_path / self.power_thrust_data_file # Read in the multi-dimensional data supplied by the user. df = pd.read_csv(self.power_thrust_data_file) @@ -460,6 +462,7 @@ def __attrs_post_init__(self) -> None: # Down-select the DataFrame to have just the ws, Cp, and Ct values index_col = df.columns.values[:-3] + self.condition_keys = index_col.tolist() df2 = df.set_index(index_col.tolist()) # Loop over the multi-dimensional keys to get the correct ws/Cp/Ct data to make @@ -470,22 +473,21 @@ def __attrs_post_init__(self) -> None: # Build the interpolants wind_speeds = data['ws'].values - self.fCp_interp = interp1d( + cp_interp = interp1d( wind_speeds, data['Cp'].values, fill_value=(0.0, 1.0), bounds_error=False, ) - inner_power = ( - 0.5 * self.rotor_area - * self.fCp_interp(wind_speeds) - * self.generator_efficiency - * wind_speeds ** 3 - ) self.power_interp.update({ key: interp1d( wind_speeds, - inner_power, + ( + 0.5 * self.rotor_area + * cp_interp(wind_speeds) + * self.generator_efficiency + * wind_speeds ** 3 + ), bounds_error=False, fill_value=0 ) @@ -498,21 +500,3 @@ def __attrs_post_init__(self) -> None: bounds_error=False, ) }) - - # If defined, create a tilt interpolation function for floating turbines. - # fill_value currently set to apply the min or max tilt angles if outside - # of the interpolation range. - if self.floating_tilt_table is not None: - self.floating_tilt_table = TiltTable.from_dict(self.floating_tilt_table) - self.fTilt_interp = interp1d( - self.floating_tilt_table.wind_speeds, - self.floating_tilt_table.tilt, - fill_value=(0.0, self.floating_tilt_table.tilt[-1]), - bounds_error=False, - ) - self.tilt_interp = self.fTilt_interp - self.correct_cp_ct_for_tilt = self.floating_correct_cp_ct_for_tilt - else: - self.fTilt_interp = None - self.tilt_interp = None - self.correct_cp_ct_for_tilt = False diff --git a/floris/simulation/wake.py b/floris/simulation/wake.py index 558f6ecbe..877ca45fa 100644 --- a/floris/simulation/wake.py +++ b/floris/simulation/wake.py @@ -91,7 +91,7 @@ class WakeModelManager(BaseClass): wake_deflection_parameters: dict = field(converter=dict) wake_turbulence_parameters: dict = field(converter=dict) - wake_velocity_parameters: dict = field(converter=dict, default={}) + wake_velocity_parameters: dict = field(converter=dict, factory=dict) combination_model: BaseModel = field(init=False) deflection_model: BaseModel = field(init=False) diff --git a/floris/simulation/wake_combination/fls.py b/floris/simulation/wake_combination/fls.py index 13e897189..f64c23dc1 100644 --- a/floris/simulation/wake_combination/fls.py +++ b/floris/simulation/wake_combination/fls.py @@ -29,7 +29,7 @@ def prepare_function(self) -> dict: def function(self, wake_field: np.ndarray, velocity_field: np.ndarray): """ Combines the base flow field with the velocity deficits - using freestream linear superpostion. In other words, the wake + using freestream linear superposition. In other words, the wake field and base fields are simply added together. Args: diff --git a/floris/simulation/wake_combination/max.py b/floris/simulation/wake_combination/max.py index 9aefeb93b..f9d5ae5b2 100644 --- a/floris/simulation/wake_combination/max.py +++ b/floris/simulation/wake_combination/max.py @@ -35,7 +35,7 @@ def prepare_function(self) -> dict: def function(self, wake_field: np.ndarray, velocity_field: np.ndarray): """ - Incorporates the velicty deficits into the base flow field by + Incorporates the velocity deficits into the base flow field by selecting the maximum of the two for each point. Args: diff --git a/floris/simulation/wake_combination/sosfs.py b/floris/simulation/wake_combination/sosfs.py index 045a734ef..0f6d280f9 100644 --- a/floris/simulation/wake_combination/sosfs.py +++ b/floris/simulation/wake_combination/sosfs.py @@ -28,7 +28,7 @@ def prepare_function(self) -> dict: def function(self, wake_field: np.ndarray, velocity_field: np.ndarray): """ - Combines the base flow field with the velocity defecits + Combines the base flow field with the velocity deficits using sum of squares. Args: diff --git a/floris/simulation/wake_deflection/empirical_gauss.py b/floris/simulation/wake_deflection/empirical_gauss.py index 864eafed8..fc3772f0e 100644 --- a/floris/simulation/wake_deflection/empirical_gauss.py +++ b/floris/simulation/wake_deflection/empirical_gauss.py @@ -29,7 +29,7 @@ class EmpiricalGaussVelocityDeflection(BaseModel): """ The Empirical Gauss deflection model is based on the form of previous the - Guass deflection model (see :cite:`bastankhah2016experimental` and + Gauss deflection model (see :cite:`bastankhah2016experimental` and :cite:`King2019Controls`) but simplifies the formulation for simpler tuning and more independence from the velocity deficit model. @@ -38,10 +38,10 @@ class EmpiricalGaussVelocityDeflection(BaseModel): in `parameter_dictionary`. Possible key-value pairs include: - **horizontal_deflection_gain_D** (*float*): Gain for the - maximum (y-direction) deflection acheived far downstream + maximum (y-direction) deflection achieved far downstream of a yawed turbine. - **vertical_deflection_gain_D** (*float*): Gain for the - maximum vertical (z-direction) deflection acheived at a + maximum vertical (z-direction) deflection achieved at a far downstream location due to rotor tilt. Specifying as -1 will mean that vertical deflections due to tilt match horizontal deflections due to yaw. @@ -101,7 +101,7 @@ def function( mixing_i (np.array): The wake-induced mixing term for the ith turbine. ct_i (np.array): Thrust coefficient for the ith turbine (-). - rotor_diameter_i (np.array): Rotor diamter for the ith + rotor_diameter_i (np.array): Rotor diameter for the ith turbine (m). x (np.array): Streamwise direction grid coordinates of the diff --git a/floris/simulation/wake_deflection/gauss.py b/floris/simulation/wake_deflection/gauss.py index 25caff58e..8ba77ad7f 100644 --- a/floris/simulation/wake_deflection/gauss.py +++ b/floris/simulation/wake_deflection/gauss.py @@ -16,7 +16,11 @@ import numexpr as ne import numpy as np -from attrs import define, field +from attrs import ( + define, + field, + fields, +) from numpy import pi from floris.simulation import ( @@ -29,6 +33,8 @@ from floris.utilities import cosd, sind +NUM_EPS = fields(BaseModel).NUM_EPS.default + @define class GaussVelocityDeflection(BaseModel): """ @@ -124,21 +130,7 @@ def function( for details on the methods used. Args: - x_locations (np.array): An array of floats that contains the - streamwise direction grid coordinates of the flow field - domain (m). - y_locations (np.array): An array of floats that contains the grid - coordinates of the flow field domain in the direction normal to - x and parallel to the ground (m). - z_locations (np.array): An array of floats that contains the grid - coordinates of the flow field domain in the vertical - direction (m). - turbine (:py:obj:`floris.simulation.turbine`): Object that - represents the turbine creating the wake. - coord (:py:obj:`floris.utilities.Vec3`): Object containing - the coordinate of the turbine creating the wake (m). - flow_field (:py:class:`floris.simulation.flow_field`): Object - containing the flow field information for the wind farm. + # TODO Returns: np.array: Deflection field for the wake. @@ -309,11 +301,11 @@ def wake_added_yaw( ### compute the spanwise and vertical velocities induced by yaw # decay = eps ** 2 / (4 * nu * delta_x / Uinf + eps ** 2) # This is the decay downstream - yLocs = delta_y + BaseModel.NUM_EPS + yLocs = delta_y + NUM_EPS # top vortex # NOTE: this is the top of the grid, not the top of the rotor - zT = z_i - (HH + D / 2) + BaseModel.NUM_EPS # distance from the top of the grid + zT = z_i - (HH + D / 2) + NUM_EPS # distance from the top of the grid rT = ne.evaluate("yLocs ** 2 + zT ** 2") # TODO: This is (-) in the paper # This looks like spanwise decay; # it defines the vortex profile in the spanwise directions @@ -323,7 +315,7 @@ def wake_added_yaw( # w_top = (-1 * Gamma_top * yLocs) / (2 * pi * rT) * core_shape * decay # bottom vortex - zB = z_i - (HH - D / 2) + BaseModel.NUM_EPS + zB = z_i - (HH - D / 2) + NUM_EPS rB = ne.evaluate("yLocs ** 2 + zB ** 2") core_shape = ne.evaluate("1 - exp(-rB / (eps ** 2))") v_bottom = ne.evaluate("(Gamma_bottom * zB) / (2 * pi * rB) * core_shape") @@ -331,7 +323,7 @@ def wake_added_yaw( # w_bottom = (-1 * Gamma_bottom * yLocs) / (2 * pi * rB) * core_shape * decay # wake rotation vortex - zC = z_i - HH + BaseModel.NUM_EPS + zC = z_i - HH + NUM_EPS rC = ne.evaluate("yLocs ** 2 + zC ** 2") core_shape = ne.evaluate("1 - exp(-rC / (eps ** 2))") v_core = ne.evaluate("(Gamma_wake_rotation * zC) / (2 * pi * rC) * core_shape") @@ -411,10 +403,10 @@ def calculate_transverse_velocity( # This is the decay downstream decay = ne.evaluate("eps ** 2 / (4 * nu * delta_x / Uinf + eps ** 2)") - yLocs = delta_y + BaseModel.NUM_EPS + yLocs = delta_y + NUM_EPS # top vortex - zT = z - (HH + D / 2) + BaseModel.NUM_EPS + zT = z - (HH + D / 2) + NUM_EPS rT = ne.evaluate("yLocs ** 2 + zT ** 2") # TODO: This is - in the paper # This looks like spanwise decay; # it defines the vortex profile in the spanwise directions @@ -423,14 +415,14 @@ def calculate_transverse_velocity( W1 = ne.evaluate("(-1 * Gamma_top * yLocs) / (2 * pi * rT) * core_shape * decay") # bottom vortex - zB = z - (HH - D / 2) + BaseModel.NUM_EPS + zB = z - (HH - D / 2) + NUM_EPS rB = ne.evaluate("yLocs ** 2 + zB ** 2") core_shape = ne.evaluate("1 - exp(-rB / (eps ** 2))") V2 = ne.evaluate("(Gamma_bottom * zB) / (2 * pi * rB) * core_shape * decay") W2 = ne.evaluate("(-1 * Gamma_bottom * yLocs) / (2 * pi * rB) * core_shape * decay") # wake rotation vortex - zC = z - HH + BaseModel.NUM_EPS + zC = z - HH + NUM_EPS rC = ne.evaluate("yLocs ** 2 + zC ** 2") core_shape = ne.evaluate("1 - exp(-rC / (eps ** 2))") V5 = ne.evaluate("(Gamma_wake_rotation * zC) / (2 * pi * rC) * core_shape * decay") @@ -439,7 +431,7 @@ def calculate_transverse_velocity( ### Boundary condition - ground mirror vortex # top vortex - ground - zTb = z + (HH + D / 2) + BaseModel.NUM_EPS + zTb = z + (HH + D / 2) + NUM_EPS rTb = ne.evaluate("yLocs ** 2 + zTb ** 2") # This looks like spanwise decay; # it defines the vortex profile in the spanwise directions @@ -448,14 +440,14 @@ def calculate_transverse_velocity( W3 = ne.evaluate("(Gamma_top * yLocs) / (2 * pi * rTb) * core_shape * decay") # bottom vortex - ground - zBb = z + (HH - D / 2) + BaseModel.NUM_EPS + zBb = z + (HH - D / 2) + NUM_EPS rBb = ne.evaluate("yLocs ** 2 + zBb ** 2") core_shape = ne.evaluate("1 - exp(-rBb / (eps ** 2))") V4 = ne.evaluate("(-1 * Gamma_bottom * zBb) / (2 * pi * rBb) * core_shape * decay") W4 = ne.evaluate("(Gamma_bottom * yLocs) / (2 * pi * rBb) * core_shape * decay") # wake rotation vortex - ground effect - zCb = z + HH + BaseModel.NUM_EPS + zCb = z + HH + NUM_EPS rCb = ne.evaluate("yLocs ** 2 + zCb ** 2") core_shape = ne.evaluate("1 - exp(-rCb / (eps ** 2))") V6 = ne.evaluate("(-1 * Gamma_wake_rotation * zCb) / (2 * pi * rCb) * core_shape * decay") diff --git a/floris/simulation/wake_deflection/jimenez.py b/floris/simulation/wake_deflection/jimenez.py index 204fe40e2..ceb6a3e8f 100644 --- a/floris/simulation/wake_deflection/jimenez.py +++ b/floris/simulation/wake_deflection/jimenez.py @@ -29,7 +29,7 @@ @define class JimenezVelocityDeflection(BaseModel): """ - Jiménez wake deflection model, dervied from + Jiménez wake deflection model, derived from :cite:`jdm-jimenez2010application`. References: @@ -67,7 +67,7 @@ def function( x: np.ndarray, ): """ - Calcualtes the deflection field of the wake in relation to the yaw of + Calculates the deflection field of the wake in relation to the yaw of the turbine. This is coded as defined in [1]. Args: diff --git a/floris/simulation/wake_velocity/cumulative_gauss_curl.py b/floris/simulation/wake_velocity/cumulative_gauss_curl.py index 7c603f5d3..ba337ab3e 100644 --- a/floris/simulation/wake_velocity/cumulative_gauss_curl.py +++ b/floris/simulation/wake_velocity/cumulative_gauss_curl.py @@ -34,14 +34,14 @@ class CumulativeGaussCurlVelocityDeficit(BaseModel): """ The cumulative curl model is an implementation of the model described in - :cite:`gdm-bay_2022`, which itself is based on the cumulative model of - :cite:`bastankhah_2021` + :cite:`cc-bay_2022`, which itself is based on the cumulative model of + :cite:`cc-bastankhah_2021`. References: - .. bibliography:: /references.bib - :style: unsrt - :filter: docname in docnames - :keyprefix: gdm- + .. bibliography:: /references.bib + :style: unsrt + :filter: docname in docnames + :keyprefix: cc- """ a_s: float = field(default=0.179367259) @@ -135,8 +135,8 @@ def function( y_coord_m = y_coord[:, :, m:m+1] z_coord_m = z_coord[:, :, m:m+1] - # For computing crossplanes, we don't need to compute downstream - # turbines from out crossplane position. + # For computing cross planes, we don't need to compute downstream + # turbines from out cross plane position. if x_coord[:, :, m:m+1].size == 0: break diff --git a/floris/simulation/wake_velocity/empirical_gauss.py b/floris/simulation/wake_velocity/empirical_gauss.py index 517ebf73f..2043e8138 100644 --- a/floris/simulation/wake_velocity/empirical_gauss.py +++ b/floris/simulation/wake_velocity/empirical_gauss.py @@ -65,8 +65,8 @@ class EmpiricalGaussVelocityDeficit(BaseModel): :style: unsrt :filter: docname in docnames """ - wake_expansion_rates: list = field(default=[0.023, 0.008]) - breakpoints_D: list = field(default=[10]) + wake_expansion_rates: list = field(factory=lambda: [0.023, 0.008]) + breakpoints_D: list = field(factory=lambda: [10]) sigma_0_D: float = field(default=0.28) smoothing_length_D: float = field(default=2.0) mixing_gain_velocity: float = field(default=2.0) @@ -227,7 +227,7 @@ def function( sigma_y0, sigma_z0 ) - # Normalize to match end of acuator disk model tube + # Normalize to match end of actuator disk model tube C_mirr = C_mirr / (8 * self.sigma_0_D**2) # ASSUME sum-of-squares superposition for the real and mirror wakes diff --git a/floris/simulation/wake_velocity/gauss.py b/floris/simulation/wake_velocity/gauss.py index 12b82b7b5..e98672a68 100644 --- a/floris/simulation/wake_velocity/gauss.py +++ b/floris/simulation/wake_velocity/gauss.py @@ -119,7 +119,7 @@ def function( # Compute the velocity deficit in the NEAR WAKE region # ONLY If there are points within the near wake boundary - # TODO: for the turbinegrid, do we need to do this near wake calculation at all? + # TODO: for the TurbineGrid, do we need to do this near wake calculation at all? # same question for any grid with a resolution larger than the near wake region if np.sum(near_wake_mask): diff --git a/floris/simulation/wake_velocity/jensen.py b/floris/simulation/wake_velocity/jensen.py index d485fecf3..b5efce92e 100644 --- a/floris/simulation/wake_velocity/jensen.py +++ b/floris/simulation/wake_velocity/jensen.py @@ -14,7 +14,11 @@ import numexpr as ne import numpy as np -from attrs import define, field +from attrs import ( + define, + field, + fields, +) from floris.simulation import ( BaseModel, @@ -25,6 +29,8 @@ ) +NUM_EPS = fields(BaseModel).NUM_EPS.default + @define class JensenVelocityDeficit(BaseModel): """ @@ -107,7 +113,6 @@ def function( dz = ne.evaluate("z - z_i") we = self.we - NUM_EPS = JensenVelocityDeficit.NUM_EPS # Construct a boolean mask to include all points downstream of the turbine downstream_mask = ne.evaluate("dx > 0 + NUM_EPS") diff --git a/floris/simulation/wake_velocity/turbopark.py b/floris/simulation/wake_velocity/turbopark.py index f66a447a9..cf0443347 100644 --- a/floris/simulation/wake_velocity/turbopark.py +++ b/floris/simulation/wake_velocity/turbopark.py @@ -104,7 +104,7 @@ def function( downstream_mask = (x_i - x >= self.NUM_EPS) x_dist = (x_i - x) * downstream_mask / rotor_diameters - # Radial distance between turbine i and the centerlines of wakes from all + # Radial distance between turbine i and the center lines of wakes from all # real/image turbines r_dist = np.sqrt((y_i - (y + deflection_field)) ** 2 + (z_i - z) ** 2) r_dist_image = np.sqrt((y_i - (y + deflection_field)) ** 2 + (z_i - (-z)) ** 2) diff --git a/floris/tools/__init__.py b/floris/tools/__init__.py index cdf5cf020..6a2cca91b 100644 --- a/floris/tools/__init__.py +++ b/floris/tools/__init__.py @@ -31,9 +31,9 @@ >>> dir(floris.tools) ['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'cut_plane', - 'floris_interface', 'flow_data', + 'floris_interface', 'layout_functions', 'optimization', 'plotting', 'power_rose', - 'rews', 'sowfa_utilities', 'visualization', 'wind_rose'] + 'rews', 'visualization', 'wind_rose'] """ from .floris_interface import FlorisInterface @@ -52,14 +52,12 @@ # from floris.tools import ( # cut_plane, # floris_interface, - # flow_data, # interface_utilities, # layout_functions, # optimization, # plotting, # power_rose, # rews, - # sowfa_utilities, # visualization, # wind_rose, # ) diff --git a/floris/tools/cc_blade_utilities.py b/floris/tools/cc_blade_utilities.py deleted file mode 100644 index 849c6dab2..000000000 --- a/floris/tools/cc_blade_utilities.py +++ /dev/null @@ -1,627 +0,0 @@ -# functions to couple floris with CCBlade and a controller - -import copy -import os -import pickle -from os import path - -import matplotlib.pyplot as plt -import numpy as np -from scipy import interpolate - -import floris.tools as wfct - -from ..logging_manager import LoggerBase - - -# Attempt CCBlade import and raise error if no success -try: - from ccblade import CCAirfoil, CCBlade -except ImportError: - err_msg = ( - "CCBlade was not found. See http://wisdem.github.io/CCBlade for " - + "installation instructions." - ) - logger = LoggerBase() - logger.logger.error(err_msg, stack_info=True) - raise ImportError(err_msg) - - -# Some useful constants -degRad = np.pi / 180.0 -rpmRadSec = 2.0 * (np.pi) / 60.0 -base_R = 63.0 # Actual NREL 5MW radius - - -# Function returns a scaled NREL 5MW rotor object from CC-Blade -def CCrotor( - Rtip=base_R, - Rhub=1.5, - hubHt=90.0, - shearExp=0.2, - rho=1.225, - mu=1.81206e-5, - path_to_af="5MW_AFFiles", -): - - r = (Rtip / base_R) * np.array( - [ - 2.8667, - 5.6000, - 8.3333, - 11.7500, - 15.8500, - 19.9500, - 24.0500, - 28.1500, - 32.2500, - 36.3500, - 40.4500, - 44.5500, - 48.6500, - 52.7500, - 56.1667, - 58.9000, - 61.6333, - ] - ) - chord = (Rtip / base_R) * np.array( - [ - 3.542, - 3.854, - 4.167, - 4.557, - 4.652, - 4.458, - 4.249, - 4.007, - 3.748, - 3.502, - 3.256, - 3.010, - 2.764, - 2.518, - 2.313, - 2.086, - 1.419, - ] - ) - theta = np.array( - [ - 13.308, - 13.308, - 13.308, - 13.308, - 11.480, - 10.162, - 9.011, - 7.795, - 6.544, - 5.361, - 4.188, - 3.125, - 2.319, - 1.526, - 0.863, - 0.370, - 0.106, - ] - ) - B = 3 # number of blades - - # In this initial version, hard-code to be NREL 5MW - afinit = CCAirfoil.initFromAerodynFile # just for shorthand - basepath = path_to_af - - # load all airfoils - airfoil_types = [0] * 8 - airfoil_types[0] = afinit(path.join(basepath, "Cylinder1.dat")) - airfoil_types[1] = afinit(path.join(basepath, "Cylinder2.dat")) - airfoil_types[2] = afinit(path.join(basepath, "DU40_A17.dat")) - airfoil_types[3] = afinit(path.join(basepath, "DU35_A17.dat")) - airfoil_types[4] = afinit(path.join(basepath, "DU30_A17.dat")) - airfoil_types[5] = afinit(path.join(basepath, "DU25_A17.dat")) - airfoil_types[6] = afinit(path.join(basepath, "DU21_A17.dat")) - airfoil_types[7] = afinit(path.join(basepath, "NACA64_A17.dat")) - - # place at appropriate radial stations - af_idx = [0, 0, 1, 2, 3, 3, 4, 5, 5, 6, 6, 7, 7, 7, 7, 7, 7] - - af = [0] * len(r) - for i in range(len(r)): - af[i] = airfoil_types[af_idx[i]] - - tilt = -5.0 - precone = 2.5 - yaw = 0.0 - - nSector = 8 # azimuthal discretization - - rotor = CCBlade( - r, - chord, - theta, - af, - Rhub, - Rtip, - B, - rho, - mu, - precone, - tilt, - yaw, - shearExp, - hubHt, - nSector, - ) - - return rotor - - -# Return the demanded generator torque for a given gen speed -# This is based on the torque controller within SOWFA and using the -# control parameters within the SOWFA example -def trq_cont(turbine_dict, genSpeedF): - """ - Compute the torque control at a given gen speed (based on SOWFA) - """ - # print(genSpeedF,turbine_dict['Region2StartGenSpeed']) - # Region 1. - if genSpeedF < turbine_dict["CutInGenSpeed"]: - # print('in region 1...') - torqueGenCommanded = turbine_dict["CutInGenTorque"] - # # Region 1-1/2. - elif (genSpeedF >= turbine_dict["CutInGenSpeed"]) and ( - genSpeedF < turbine_dict["Region2StartGenSpeed"] - ): - # print('in region 1.5...') - dGenSpeed = genSpeedF - turbine_dict["CutInGenSpeed"] - Region2StartGenTorque = ( - turbine_dict["KGen"] - * turbine_dict["Region2StartGenSpeed"] - * turbine_dict["Region2StartGenSpeed"] - ) - torqueSlope = (Region2StartGenTorque - turbine_dict["CutInGenTorque"]) / ( - turbine_dict["Region2StartGenSpeed"] - turbine_dict["CutInGenSpeed"] - ) - torqueGenCommanded = turbine_dict["CutInGenTorque"] + torqueSlope * dGenSpeed - # # Region 2. - elif (genSpeedF >= turbine_dict["Region2StartGenSpeed"]) and ( - genSpeedF < turbine_dict["Region2EndGenSpeed"] - ): - # print('in region 2...') - torqueGenCommanded = turbine_dict["KGen"] * genSpeedF * genSpeedF - # # Region 2-1/2. - elif (genSpeedF >= turbine_dict["Region2EndGenSpeed"]) and ( - genSpeedF < turbine_dict["RatedGenSpeed"] - ): - # print('in region 2.5...') - dGenSpeed = genSpeedF - turbine_dict["Region2EndGenSpeed"] - Region2EndGenTorque = ( - turbine_dict["KGen"] - * turbine_dict["Region2EndGenSpeed"] - * turbine_dict["Region2EndGenSpeed"] - ) - torqueSlope = (turbine_dict["RatedGenTorque"] - Region2EndGenTorque) / ( - turbine_dict["RatedGenSpeed"] - turbine_dict["Region2EndGenSpeed"] - ) - torqueGenCommanded = Region2EndGenTorque + torqueSlope * dGenSpeed - # # Region 3. - elif genSpeedF >= turbine_dict["RatedGenSpeed"]: - # print('in region 3...') - torqueGenCommanded = turbine_dict["RatedGenTorque"] - - # Limit to the rated torque - torqueGenCommanded = np.min([torqueGenCommanded, turbine_dict["RatedGenTorque"]]) - - return torqueGenCommanded - - -# Update the PI pitch controller -# This is based on the pitch controller within SOWFA and using the control -# parameters within the SOWFA example -def pitch_control(turbine_dict, rotSpeedF, pitch_prev, dt, intSpeedError): - min_pitch = 0.0 - max_pitch = 90.0 - - # Set the gain scheduling variable. - GK = 1.0 / (1.0 + (pitch_prev * degRad) / turbine_dict["PitchK"]) - - # Store the old value of speed error. - # speedErrorLast = sped_prev - - # Compute the low speed shaft speed error. - speedError = rotSpeedF - turbine_dict["RatedRotSpeed"] * rpmRadSec # in rad/s - - # Numerically integrate the speed error over time. - intSpeedError = intSpeedError + speedError * dt - - # Numerically take the deriviative of speed error w.r.t time. - # scalar derivSpeedError = (speedError[i] - speedErrorLast) / dt; - - # Saturate the integrated speed error based on pitch saturation. - intSpeedError = np.max( - [intSpeedError, min_pitch / (GK * turbine_dict["PitchControlKI"])] - ) - intSpeedError = np.min( - [intSpeedError, max_pitch / (GK * turbine_dict["PitchControlKI"])] - ) - - # Compute the pitch components from the proportional, integral, - # and derivative parts and sum them. - pitchP = GK * turbine_dict["PitchControlKP"] * speedError - pitchI = GK * turbine_dict["PitchControlKI"] * intSpeedError - # scalar pitchD = GK * PitchControlKD[j] * derivSpeedError; - pitchCommanded = pitchP + pitchI # + pitchD; - - # Saturate the pitch based on the pitch limits of the pitch - # actuator. - pitchCommanded = np.min([np.max([pitchCommanded, min_pitch]), max_pitch]) - - # print('pitch commanded = ', pitchCommanded,max_pitch) - - # Return the commanded pitch - return pitchCommanded, intSpeedError - - -# Given a controller paramaterized by turbing dict, return a new turbine_dict -# With values scaled according to changes in D and turbine rating (in MW) -def scale_controller_and_rotor(turbine_dict_in, R_In=base_R, turbine_rating=5): - - # Copy the dict - turbine_dict = copy.deepcopy(turbine_dict_in) - - # Save the R value - turbine_dict["TipRad"] = R_In - - # Scale the rotation speed inverse to the new radius - turbine_dict["CutInGenSpeed"] = (base_R / R_In) * turbine_dict["CutInGenSpeed"] - turbine_dict["Region2StartGenSpeed"] = (base_R / R_In) * turbine_dict[ - "Region2StartGenSpeed" - ] - turbine_dict["Region2EndGenSpeed"] = (base_R / R_In) * turbine_dict[ - "Region2EndGenSpeed" - ] - turbine_dict["RatedGenSpeed"] = (base_R / R_In) * turbine_dict["RatedGenSpeed"] - turbine_dict["RatedRotSpeed"] = (base_R / R_In) * turbine_dict["RatedRotSpeed"] - - # Scale the cut in generator torque (not necessary, this is always 0) - # turbine_dict['CutInGenTorque'] = (base_R/R_In) * turbine_dict['CutInGenTorque'] - - # Scale kGen by the 5th power of radius - turbine_dict["KGen"] = (R_In / base_R) ** 5 * turbine_dict["KGen"] - - # Scale the rator torque according to the rated speed and power - turbine_dict["RatedGenTorque"] = (turbine_rating * 1e6) / ( - turbine_dict["RatedRotSpeed"] - * turbine_dict["GBRatio"] - * np.pi - / 30.0 - * turbine_dict["GenEfficiency"] - ) - - # Save rating for conviencce - turbine_dict["RatedMW"] = turbine_rating - - # Get the scaled rotor - rotor = CCrotor(R_In) - - return turbine_dict, rotor - - -# Given a controller paramaterization, show the torque curve -def show_torque_curve(turbine_dict, ax, label="_nolegend_"): - - # Based on the details in SOWFA case, show the torque curve - gen_speed_sweep = np.arange( - 0, turbine_dict["RatedRotSpeed"] * turbine_dict["GBRatio"], 1.0 - ) - gen_torque = np.array([trq_cont(turbine_dict, gf) for gf in gen_speed_sweep]) - # trq_opt = np.array([gf*gf*turbine_dict['KGen'] for gf in gen_speed_sweep]) - - # ax.plot(gen_speed_sweep,trq_opt,'k--',label='Optimal') - ax.plot(gen_speed_sweep, gen_torque, label=label) - ax.set_xlabel("Gen Speed (RPM)") - ax.set_ylabel("Gen Torque (Nm)") - ax.grid(True) - ax.set_title("Torque Curve") - ax.legend() - - -# Generate a set of look-up tables the controller/steady state can use to find a cp/cq/ct -# for a given pitch angle and TSR -def generate_base_lut(rotor, turbine_dict): - - # These dicts (keyed on yaw) - cp_dict = {} - ct_dict = {} - cq_dict = {} - - # for now, assume only one yaw angle, perhaps expand later - yaw = 0.0 - - # Mesh the grid and flatten the arrays - fixed_rpm = 10 # RPM - Rtip = turbine_dict["TipRad"] - TSR_initial = np.arange(0.5, 15, 0.5) - pitch_initial = np.arange(0, 25, 0.5) - ws_array = (fixed_rpm * (np.pi / 30.0) * Rtip) / TSR_initial - ws_mesh, pitch_mesh = np.meshgrid(ws_array, pitch_initial) - ws_flat = ws_mesh.flatten() - pitch_flat = pitch_mesh.flatten() - omega_flat = np.ones_like(pitch_flat) * fixed_rpm - # tsr_flat = (fixed_rpm * (np.pi / 30.0) * Rtip) / ws_flat - - # Get values from cc-blade - outputs, derivs = rotor.evaluate( - [ws_flat], omega_flat, pitch_flat, coefficients=True - ) - CP = outputs["CP"] - CT = outputs["CT"] - CQ = outputs["CQ"] - - # Reshape Cp, Ct and Cq - CP = np.reshape(CP, (len(pitch_initial), len(TSR_initial))) - CT = np.reshape(CT, (len(pitch_initial), len(TSR_initial))) - CQ = np.reshape(CQ, (len(pitch_initial), len(TSR_initial))) - - # # Form the interpolant functions - cp_interp = interpolate.interp2d(TSR_initial, pitch_initial, CP, kind="cubic") - ct_interp = interpolate.interp2d(TSR_initial, pitch_initial, CT, kind="cubic") - cq_interp = interpolate.interp2d(TSR_initial, pitch_initial, CQ, kind="cubic") - - # Add to the dictionaries - cp_dict[yaw] = cp_interp - ct_dict[yaw] = ct_interp - cq_dict[yaw] = cq_interp - - # Save dictionaries - pickle.dump([cp_dict, ct_dict, cq_dict], open("cp_ct_cq_lut.p", "wb")) - - -def get_aero_torque(rotor, ws, rot_speed, fluidDensity, R, pitch_angle=0.0): - outputs, _ = rotor.evaluate( - [ws], [rot_speed / rpmRadSec], [pitch_angle], coefficients=True - ) - - cq = outputs["CQ"] - - # print(cq[0]) - return 0.5 * fluidDensity * (np.pi * R ** 2) * cq[0] * R * ws ** 2 - - -# For a given rotor/controller/wind speed, get the steady state value -def get_steady_state( - turbine_dict, rotor, ws, dt=0.5, sim_time=5, title=None, show_plot=False -): - - # Save some convience terms - fluidDensity = 1.225 # TODO Get from SOWFA - R = turbine_dict["TipRad"] - GBRatio = turbine_dict["GBRatio"] - - # Determine the drivetrain inertia - drivetrain_inertia = ( - turbine_dict["NumBl"] * turbine_dict["BladeIner"] - + turbine_dict["HubIner"] - + turbine_dict["GBRatio"] * turbine_dict["GBRatio"] * turbine_dict["GenIner"] - ) - - # Simulation parameters - # sim_length = sim_time/dt - - # Try to determine a good initial rotor speed - rot_sweep = np.linspace( - turbine_dict["CutInGenSpeed"] * rpmRadSec / GBRatio, - turbine_dict["RatedRotSpeed"] * rpmRadSec, - 15, - ) - gen_sweep = rot_sweep * GBRatio / rpmRadSec - aero_sweep = np.array( - [get_aero_torque(rotor, ws, r_speed, fluidDensity, R) for r_speed in rot_sweep] - ) - gt_sweep = np.array([trq_cont(turbine_dict, gs) for gs in gen_sweep]) - torque_error = np.abs( - aero_sweep * turbine_dict["GBEfficiency"] - GBRatio * gt_sweep - ) - - # If max exceeded, use max - if np.max(aero_sweep * turbine_dict["GBEfficiency"]) > np.max(gt_sweep * GBRatio): - init_rotor = turbine_dict["RatedRotSpeed"] * rpmRadSec - else: # Use the minimum - idx = np.argmin(torque_error) - init_rotor = rot_sweep[idx] - - # Initialize the pitch (if at max RPM) - if (init_rotor == rot_sweep[-1]) or ( - init_rotor == turbine_dict["RatedRotSpeed"] * rpmRadSec - ): - pitch_sweep = np.linspace(0, 20, 50) - aero_sweep = np.array( - [ - get_aero_torque(rotor, ws, init_rotor, fluidDensity, R, pitch_angle=p) - for p in pitch_sweep - ] - ) - gt_sweep = np.array( - [trq_cont(turbine_dict, gen_sweep[-1]) for p in pitch_sweep] - ) - torque_error = np.abs( - aero_sweep * turbine_dict["GBEfficiency"] - GBRatio * gt_sweep - ) - idx = np.argmin(torque_error) - init_pitch = pitch_sweep[idx] - - # And force the intspeed error warm - GK = 1.0 / (1.0 + (init_pitch * degRad) / turbine_dict["PitchK"]) - intSpeedError = init_pitch / (GK * turbine_dict["PitchControlKI"]) - - else: - init_pitch = 0.0 - # Initialize int speed error as 0 - intSpeedError = 0.0 - - # Aero torque assuming pitch is 0 - - # Create the arrays - t_array = np.arange(0, sim_time, dt) - pitch = np.ones_like(t_array) * init_pitch - rot_speed = np.ones_like(t_array) * init_rotor # represent rot speed in rad / s - gen_speed = ( - np.ones_like(t_array) * init_rotor * GBRatio / rpmRadSec - ) # represent gen speed in rpm - aero_torque = np.ones_like(t_array) * 1000.0 - gen_torque = np.ones_like(t_array) * trq_cont(turbine_dict, gen_speed[0]) - gen_power = np.ones_like(t_array) * 0.0 - tsr_array = np.ones_like(t_array) * 0.0 - cq_array = np.ones_like(t_array) * 0.0 - cp_array = np.ones_like(t_array) * 0.0 - ct_array = np.ones_like(t_array) * 0.0 - - # Load the Cp,Ct,Cq tables - cp_dict, ct_dict, cq_dict = pickle.load(open("cp_ct_cq_lut.p", "rb")) - - # Select the 0-yaw LUT - # cq_lut = cq_dict[0] - - # Now loop through and get the values - re_run = True - max_re_run = 5 - num_re_run = 0 - while re_run and (num_re_run < max_re_run): - for i in range(1, len(t_array)): - - # print('Control time step = ', i, 'out of ', len(t_array)) - - # Calculate TSR - tsr = (R * (rot_speed[i - 1] / rpmRadSec) * np.pi / 30.0) / ws - - # Update the aero torque - # cq = cq_lut(tsr,pitch[i-1]) - try: - outputs, _ = rotor.evaluate( - [ws], - [rot_speed[i - 1] / rpmRadSec], - [pitch[i - 1]], - coefficients=True, - ) - # M = outputs["M"] - Cp = outputs["CP"] - Ct = outputs["CT"] - cq = outputs["CQ"] - - except Exception: - print("CC BLADE PROBLEM") - if i > 0: - return gen_power[i - 1], cp_array[i - 1], ct_array[i - 1] - else: - print("...no data") - return np.nan, np.nan, np.nan - - aero_torque[i] = 0.5 * fluidDensity * (np.pi * R ** 2) * cq * R * ws ** 2 - - # Save these values for plotting - cq_array[i] = cq - cp_array[i] = Cp[0] - ct_array[i] = Ct[0] - tsr_array[i] = tsr - - # Update the rotor speed and generator speed - rot_speed[i] = rot_speed[i - 1] + (dt / drivetrain_inertia) * ( - aero_torque[i] * turbine_dict["GBEfficiency"] - - GBRatio * gen_torque[i - 1] - ) - gen_speed[i] = rot_speed[i] * GBRatio / rpmRadSec - - # Update the gen torque - gen_torque[i] = trq_cont(turbine_dict, gen_speed[i]) - - # Update the blade pitch - pitch[i], intSpeedError = pitch_control( - turbine_dict, rot_speed[i], pitch[i - 1], dt, intSpeedError - ) - - # Calculate the power - gen_power[i] = ( - gen_speed[i] - * np.pi - / 30.0 - * gen_torque[i] - * turbine_dict["GenEfficiency"] - ) - # Determine if need to re_run - if (gen_power[-1] <= turbine_dict["RatedMW"] * 1e6 * 1.001) and ( - np.abs(gen_torque[-1] - aero_torque[-1] / GBRatio) < 100 - ): - re_run = False - else: - print("Re Run %s" % title) - re_run = True - num_re_run = num_re_run + 1 - pitch[0] = pitch[-1] - rot_speed[0] = rot_speed[-1] - gen_speed[0] = gen_speed[-1] - aero_torque[0] = aero_torque[-1] - gen_torque[0] = gen_torque[-1] - gen_power[0] = gen_power[-1] - tsr_array[0] = tsr_array[-1] - cq_array[0] = cq_array[-1] - - if show_plot: - fig, axarr = plt.subplots(5, 1, sharex=True) - - if title is not None: - fig.suptitle(title, fontsize=16) - - ax = axarr[0] - ax.plot(t_array[1:], tsr_array[1:], label="TSR") - ax.fill_between(t_array[1:], 6, 8, color="gray", alpha=0.2) - ax.legend() - # ax.set_ylim([5,9]) - ax.grid(True) - - ax = axarr[1] - ax.plot(t_array[1:], pitch[1:], label="Pitch") - ax.grid(True) - ax.legend() - - ax = axarr[2] - ax.plot(t_array[1:], gen_torque[1:], label="Gen Torque") - ax.plot( - t_array[1:], aero_torque[1:] / GBRatio, label="Aero Torque / GB", color="r" - ) - ax.grid(True) - ax.legend() - - ax = axarr[3] - ax.plot(t_array[1:], rot_speed[1:] / rpmRadSec, label="Rotor Speed (RPM)") - ax.axhline(turbine_dict["RatedRotSpeed"], color="r", label="Rated") - ax.grid(True) - ax.legend() - - ax = axarr[4] - ax.plot(t_array[1:], gen_power[1:] / 1e6, label="Power") - ax.axhline(turbine_dict["RatedMW"], color="r") - ax.plot() - ax.grid(True) - ax.legend() - - # Return the steady values - return gen_power[-1], Cp[0], Ct[0] - - -def get_wind_sweep_steady_values(turbine_dict, rotor, ws_array=np.arange(3, 21, 1.0)): - - # Get the steady values - pow_array = [] - cp_array = [] - ct_array = [] - - for ws in ws_array: - print(ws) - p, cp, ct = get_steady_state(turbine_dict, rotor, ws) - pow_array.append(p) - cp_array.append(cp) - ct_array.append(ct) - - return ws_array, np.array(pow_array), np.array(cp_array), np.array(ct_array) diff --git a/floris/tools/cut_plane.py b/floris/tools/cut_plane.py index 4627247fd..ade17b7d7 100644 --- a/floris/tools/cut_plane.py +++ b/floris/tools/cut_plane.py @@ -354,9 +354,9 @@ def calculate_wind_speed(cross_plane, x1_loc, x2_loc, R): Args: cross_plane (:py:class:`floris.tools.cut_plane.CrossPlane`): plane of data. - x1_loc (float): x1-coordinate of point of interst. - x2_loc (float): x2-coordinate of point of interst. - R (float): radius from point of interst to consider + x1_loc (float): x1-coordinate of point of interest. + x2_loc (float): x2-coordinate of point of interest. + R (float): radius from point of interest to consider Returns: (float): effective wind speed @@ -393,8 +393,8 @@ def calculate_power( Args: cross_plane (:py:class:`floris.tools.cut_plane.CrossPlane`): plane of data. - x1_loc (float): x1-coordinate of point of interst. - x2_loc (float): x2-coordinate of point of interst. + x1_loc (float): x1-coordinate of point of interest. + x2_loc (float): x2-coordinate of point of interest. R (float): Radius of wind turbine rotor. ws_array (np.array): reference wind speed for cp curve. cp_array (np.array): cp curve at reference wind speeds. diff --git a/floris/tools/floris_interface.py b/floris/tools/floris_interface.py index c5404d0b2..a466ad583 100644 --- a/floris/tools/floris_interface.py +++ b/floris/tools/floris_interface.py @@ -14,13 +14,13 @@ from __future__ import annotations +import inspect from pathlib import Path import numpy as np import pandas as pd -from scipy.interpolate import LinearNDInterpolator, NearestNDInterpolator -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager from floris.simulation import Floris, State from floris.simulation.turbine import ( average_velocity, @@ -34,7 +34,7 @@ from floris.type_dec import NDArrayFloat -class FlorisInterface(LoggerBase): +class FlorisInterface(LoggingManager): """ FlorisInterface provides a high-level user interface to many of the underlying methods within the FLORIS framework. It is meant to act as a @@ -42,7 +42,7 @@ class FlorisInterface(LoggerBase): methods on objects within FLORIS. Args: - configuration (:py:obj:`dict`): The Floris configuration dictarionary or YAML file. + configuration (:py:obj:`dict`): The Floris configuration dictionary or YAML file. The configuration should have the following inputs specified. - **flow_field**: See `floris.simulation.flow_field.FlowField` for more details. - **farm**: See `floris.simulation.farm.Farm` for more details. @@ -55,7 +55,16 @@ def __init__(self, configuration: dict | str | Path): self.configuration = configuration if isinstance(self.configuration, (str, Path)): - self.floris = Floris.from_file(self.configuration) + try: + self.floris = Floris.from_file(self.configuration) + except FileNotFoundError: + # If the file cannot be found, then attempt the configuration path relative to the + # file location from which FlorisInterface was attempted to be run. If successful, + # update self.configuration to an absolute, working file path and name. + base_fn = Path(inspect.stack()[-1].filename).resolve().parent + config = (base_fn / self.configuration).resolve() + self.floris = Floris.from_file(config) + self.configuration = config elif isinstance(self.configuration, dict): self.floris = Floris.from_dict(self.configuration) @@ -584,7 +593,7 @@ def check_wind_condition_for_viz(self, wd=None, ws=None): ) def get_turbine_powers(self) -> NDArrayFloat: - """Calculates the power at each turbine in the windfarm. + """Calculates the power at each turbine in the wind farm. Returns: NDArrayFloat: Powers at each turbine. @@ -610,7 +619,7 @@ def get_turbine_powers(self) -> NDArrayFloat: return turbine_powers def get_turbine_powers_multidim(self) -> NDArrayFloat: - """Calculates the power at each turbine in the windfarm + """Calculates the power at each turbine in the wind farm when using multi-dimensional Cp/Ct turbine definitions. Returns: @@ -647,7 +656,7 @@ def get_turbine_Cts(self) -> NDArrayFloat: tilt_angle=self.floris.farm.tilt_angles, ref_tilt_cp_ct=self.floris.farm.ref_tilt_cp_cts, fCt=self.floris.farm.turbine_fCts, - tilt_interp=self.floris.farm.turbine_fTilts, + tilt_interp=self.floris.farm.turbine_tilt_interps, correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, turbine_type_map=self.floris.farm.turbine_type_map, average_method=self.floris.grid.average_method, @@ -662,7 +671,7 @@ def get_turbine_ais(self) -> NDArrayFloat: tilt_angle=self.floris.farm.tilt_angles, ref_tilt_cp_ct=self.floris.farm.ref_tilt_cp_cts, fCt=self.floris.farm.turbine_fCts, - tilt_interp=self.floris.farm.turbine_fTilts, + tilt_interp=self.floris.farm.turbine_tilt_interps, correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, turbine_type_map=self.floris.farm.turbine_type_map, average_method=self.floris.grid.average_method, @@ -689,7 +698,7 @@ def turbine_effective_velocities(self) -> NDArrayFloat: ref_tilt_cp_ct=self.floris.farm.ref_tilt_cp_cts, pP=self.floris.farm.pPs, pT=self.floris.farm.pTs, - tilt_interp=self.floris.farm.turbine_fTilts, + tilt_interp=self.floris.farm.turbine_tilt_interps, correct_cp_ct_for_tilt=self.floris.farm.correct_cp_ct_for_tilt, turbine_type_map=self.floris.farm.turbine_type_map, average_method=self.floris.grid.average_method, @@ -986,6 +995,130 @@ def sample_flow_at_points(self, x: NDArrayFloat, y: NDArrayFloat, z: NDArrayFloa return self.floris.solve_for_points(x, y, z) + def sample_velocity_deficit_profiles( + self, + direction: str = 'cross-stream', + downstream_dists: NDArrayFloat | list = None, + profile_range: NDArrayFloat | list = None, + resolution: int = 100, + wind_direction: float = None, + homogeneous_wind_speed: float = None, + ref_rotor_diameter: float = None, + x_start: float = 0.0, + y_start: float = 0.0, + reference_height: float = None, + ) -> list[pd.DataFrame]: + """ + Extract velocity deficit profiles at a set of downstream distances from a starting point + (usually a turbine location). For each downstream distance, a profile is sampled along + a line in either the cross-stream direction (x2) or the vertical direction (x3). + Velocity deficit is here defined as (homogeneous_wind_speed - u)/homogeneous_wind_speed, + where u is the wake velocity obtained when wind_shear = 0.0. + + Args: + direction: At each downstream location, this is the direction in which to sample the + profile. Either `cross-stream` or `vertical`. + downstream_dists: A list/array of streamwise locations for where to sample the profiles. + Default starting point is (0.0, 0.0, reference_height). + profile_range: Determines the extent of the line along which the profiles are sampled. + The range is defined about a point which lies some distance directly downstream of + the starting point. + resolution: Number of sample points in each profile. + wind_direction: A single wind direction. + homogeneous_wind_speed: A single wind speed. It is called homogeneous since 'wind_shear' + is temporarily set to 0.0 in this method. + ref_rotor_diameter: A reference rotor diameter which is used to normalize the + coordinates. + x_start: x-coordinate of starting point. + y_start: y-coordinate of starting point. + reference_height: If `direction` is cross-stream, then `reference_height` defines the + height of the horizontal plane in which the velocity profiles are sampled. + If `direction` is vertical, then the velocity is sampled along the vertical + direction with the `profile_range` being relative to the `reference_height`. + Returns: + A list of pandas DataFrame objects where each DataFrame represents one velocity deficit + profile. + """ + + if direction not in ['cross-stream', 'vertical']: + raise ValueError("`direction` must be either `cross-stream` or `vertical`.") + + if ref_rotor_diameter is None: + unique_rotor_diameters = np.unique(self.floris.farm.rotor_diameters) + if len(unique_rotor_diameters) == 1: + ref_rotor_diameter = unique_rotor_diameters[0] + else: + raise ValueError( + "Please provide a `ref_rotor_diameter`. This is needed to normalize the " + "coordinates. Could not select a value automatically since the number of " + "unique rotor diameters in the turbine layout is not 1. " + f"Found the following rotor diameters: {unique_rotor_diameters}." + ) + + if downstream_dists is None: + downstream_dists = ref_rotor_diameter * np.array([3, 5, 7, 9]) + + if profile_range is None: + profile_range = ref_rotor_diameter * np.array([-2, 2]) + + wind_directions_copy = np.array(self.floris.flow_field.wind_directions, copy=True) + wind_speeds_copy = np.array(self.floris.flow_field.wind_speeds, copy=True) + wind_shear_copy = self.floris.flow_field.wind_shear + + if wind_direction is None: + if len(wind_directions_copy) == 1: + wind_direction = wind_directions_copy[0] + else: + raise ValueError( + "Could not determine a wind direction for which to sample the velocity " + "profiles. Either provide a single `wind_direction` as an argument to this " + "method, or initialize the Floris object with a single wind direction." + ) + + if homogeneous_wind_speed is None: + if len(wind_speeds_copy) == 1: + homogeneous_wind_speed = wind_speeds_copy[0] + self.logger.warning( + "`homogeneous_wind_speed` not provided. Setting it to the following wind speed " + f"found in the current flow field: {wind_speeds_copy[0]} m/s. Note that the " + "inflow is always homogeneous when calculating the velocity deficit profiles. " + "This is done by temporarily setting `wind_shear` to 0.0" + ) + else: + raise ValueError( + "Could not determine a wind speed for which to sample the velocity " + "profiles. Provide a single `homogeneous_wind_speed` to this method." + ) + + if reference_height is None: + reference_height = self.floris.flow_field.reference_wind_height + + self.reinitialize( + wind_directions=[wind_direction], + wind_speeds=[homogeneous_wind_speed], + wind_shear=0.0, + ) + + velocity_deficit_profiles = self.floris.solve_for_velocity_deficit_profiles( + direction, + downstream_dists, + profile_range, + resolution, + homogeneous_wind_speed, + ref_rotor_diameter, + x_start, + y_start, + reference_height, + ) + + self.reinitialize( + wind_directions=wind_directions_copy, + wind_speeds=wind_speeds_copy, + wind_shear=wind_shear_copy, + ) + + return velocity_deficit_profiles + @property def layout_x(self): """ @@ -1018,7 +1151,7 @@ def get_turbine_layout(self, z=False): np.array: lists of x, y, and (optionally) z coordinates of each turbine """ - xcoords, ycoords, zcoords = np.array([c.elements for c in self.floris.farm.coordinates]).T + xcoords, ycoords, zcoords = self.floris.farm.coordinates.T if z: return xcoords, ycoords, zcoords else: diff --git a/floris/tools/flow_data.py b/floris/tools/flow_data.py deleted file mode 100644 index b1ea78a94..000000000 --- a/floris/tools/flow_data.py +++ /dev/null @@ -1,165 +0,0 @@ -# Copyright 2021 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. - -# See https://floris.readthedocs.io for documentation - - -import os - -import numpy as np -from sklearn import neighbors - -from ..utilities import Vec3 - - -class FlowData: - """ - FlowData objects represent a saved 3D flow from a FLORIS simulation - or other data source. - """ - - # TODO handle none case, maybe default values apply like 0 origin and auto - # determine spacing and dimensions - def __init__(self, x, y, z, u, v, w, spacing=None, dimensions=None, origin=None): - """ - Initialize FlowData object with coordinates, velocity fields, - and meta-data. - - Args: - x (np.array): Cartesian coordinate data. - y (np.array): Cartesian coordinate data. - z (np.array): Cartesian coordinate data. - u (np.array): x-component of velocity. - v (np.array): y-component of velocity. - w (np.array): z-component of velocity. - spacing (Vec3, optional): Spatial resolution. - Defaults to None. - dimensions (Vec3, optional): Named dimensions - (e.g. x1, x2, x3). Defaults to None. - origin (Vec3, optional): Coordinates of origin. - Defaults to None. - """ - - self.x = x - self.y = y - self.z = z - self.u = u - self.v = v - self.w = w - - self.spacing = spacing - self.dimensions = dimensions - self.origin = origin - - # Technically resolution is a restating of above, but it is useful to have - self.resolution = Vec3(len(np.unique(x)), len(np.unique(y)), len(np.unique(z))) - - def save_as_vtk(self, filename): - """ - Save FlowData Object to vtk format. - - Args: - filename (str): Write-to path for vtk file. - """ - n_points = self.dimensions.x1 * self.dimensions.x2 * self.dimensions.x3 - - ln = "\n" - vtk_file = open(filename, "w") - vtk_file.write("# vtk DataFile Version 3.0" + ln) - vtk_file.write("array.mean0D" + ln) - vtk_file.write("ASCII" + ln) - vtk_file.write("DATASET STRUCTURED_POINTS" + ln) - vtk_file.write("DIMENSIONS {}".format(self.dimensions) + ln) - vtk_file.write(f"ORIGIN {self.origin.x1} {self.origin.x2} {self.origin.x3}" + ln) - vtk_file.write("SPACING {}".format(self.spacing) + ln) - vtk_file.write("POINT_DATA {}".format(n_points) + ln) - vtk_file.write("FIELD attributes 1" + ln) - vtk_file.write("UAvg 3 {} float".format(n_points) + ln) - for u, v, w in zip(self.u, self.v, self.w): - vtk_file.write("{}".format(Vec3(u, v, w)) + ln) - - @staticmethod - def crop(ff, x_bnds, y_bnds, z_bnds): - """ - Crop FlowData object to within stated bounds. - - Args: - ff (:py:class:`~.tools.flow_data.FlowData`): - FlowData object. - x_bnds (iterable): Min and max of x-coordinate. - y_bnds (iterable): Min and max of y-coordinate. - z_bnds (iterable): Min and max of z-coordinate. - - Returns: - (:py:class:`~.tools.flow_data.FlowData`): - Cropped FlowData object. - """ - - map_values = ( - (ff.x > x_bnds[0]) - & (ff.x < x_bnds[1]) - & (ff.y > y_bnds[0]) - & (ff.y < y_bnds[1]) - & (ff.z > z_bnds[0]) - & (ff.z < z_bnds[1]) - ) - - x = ff.x[map_values] - y = ff.y[map_values] - z = ff.z[map_values] - - # Work out new dimensions - dimensions = Vec3(len(np.unique(x)), len(np.unique(y)), len(np.unique(z))) - - # Work out origin - origin = Vec3( - ff.origin.x1 + np.min(x), - ff.origin.x2 + np.min(y), - ff.origin.x3 + np.min(z), - ) - - return FlowData( - x - np.min(x), - y - np.min(y), - z - np.min(z), - ff.u[map_values], - ff.v[map_values], - ff.w[map_values], - spacing=ff.spacing, # doesn't change - dimensions=dimensions, - origin=origin, - ) - - # Define a quick function for getting arbitrary points from sowfa - - def get_points_from_flow_data(self, x_points, y_points, z_points): - """ - Return the u-value of a set of points from with a FlowData object. - Use a simple nearest neighbor regressor to do internal interpolation. - - Args: - x_points (np.array): Array of x-locations of points. - y_points (np.array): Array of y-locations of points. - z_points (np.array): Array of z-locations of points. - - Returns: - np.array: Array of u-velocity at specified points. - """ - # print(x_points,y_points,z_points) - # X = np.column_stack([self.x, self.y, self.z]) - n_neighbors = 1 - knn = neighbors.KNeighborsRegressor(n_neighbors) - # y_ = knn.fit(X, self.u) # .predict(T) - - # Predict new points - T = np.column_stack([x_points, y_points, z_points]) - return knn.predict(T) diff --git a/floris/tools/interface_utilities.py b/floris/tools/interface_utilities.py index e719aea9f..3a02b6960 100644 --- a/floris/tools/interface_utilities.py +++ b/floris/tools/interface_utilities.py @@ -27,6 +27,7 @@ def show_params( # props = get_props(obj, fi) props = fi.floris.wake._asdict() # props = props["wake_velocity_parameters"][fi.floris.wake.velocity_model.model_string] + # NOTE: _get_model_dict is remove and model.as_dict() should be used instead props = fi.floris.wake.velocity_model._get_model_dict() if verbose: diff --git a/floris/tools/optimization/layout_optimization/layout_optimization_base.py b/floris/tools/optimization/layout_optimization/layout_optimization_base.py index ea160e038..fc67ac021 100644 --- a/floris/tools/optimization/layout_optimization/layout_optimization_base.py +++ b/floris/tools/optimization/layout_optimization/layout_optimization_base.py @@ -20,10 +20,10 @@ YawOptimizationGeometric, ) -from ....logging_manager import LoggerBase +from ....logging_manager import LoggingManager -class LayoutOptimization(LoggerBase): +class LayoutOptimization(LoggingManager): def __init__(self, fi, boundaries, min_dist=None, freq=None, enable_geometric_yaw=False): self.fi = fi.copy() self.boundaries = boundaries diff --git a/floris/tools/optimization/legacy/pyoptsparse/optimization.py b/floris/tools/optimization/legacy/pyoptsparse/optimization.py index 65e6c2a49..d0240c138 100644 --- a/floris/tools/optimization/legacy/pyoptsparse/optimization.py +++ b/floris/tools/optimization/legacy/pyoptsparse/optimization.py @@ -12,10 +12,10 @@ # See https://floris.readthedocs.io for documentation -from ....logging_manager import LoggerBase +from floris.logging_manager import LoggingManager -class Optimization(LoggerBase): +class Optimization(LoggingManager): """ Base optimization class. diff --git a/floris/tools/optimization/legacy/pyoptsparse/yaw.py b/floris/tools/optimization/legacy/pyoptsparse/yaw.py index 99a4b808f..1e90573b0 100644 --- a/floris/tools/optimization/legacy/pyoptsparse/yaw.py +++ b/floris/tools/optimization/legacy/pyoptsparse/yaw.py @@ -17,7 +17,7 @@ import numpy as np from scipy.stats import norm -from ...visualization import visualize_cut_plane +from floris.tools.visualization import visualize_cut_plane class Yaw: diff --git a/floris/tools/optimization/legacy/scipy/optimization.py b/floris/tools/optimization/legacy/scipy/optimization.py index 14e275100..621b1133f 100644 --- a/floris/tools/optimization/legacy/scipy/optimization.py +++ b/floris/tools/optimization/legacy/scipy/optimization.py @@ -12,16 +12,9 @@ # See https://floris.readthedocs.io for documentation -import matplotlib.pyplot as plt import numpy as np -try: - from mpi4py.futures import MPIPoolExecutor -except ImportError: - pass - - class Optimization: """ Optimization is the base optimization class for diff --git a/floris/tools/optimization/legacy/scipy/yaw_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_clustered.py index 36ec451b9..c880bd262 100644 --- a/floris/tools/optimization/legacy/scipy/yaw_clustered.py +++ b/floris/tools/optimization/legacy/scipy/yaw_clustered.py @@ -17,12 +17,13 @@ import numpy as np import pandas as pd -from ....logging_manager import LoggerBase +from floris.logging_manager import LoggingManager + from .cluster_turbines import cluster_turbines from .yaw import YawOptimization -class YawOptimizationClustered(YawOptimization, LoggerBase): +class YawOptimizationClustered(YawOptimization, LoggingManager): """ YawOptimization is a subclass of :py:class:`~.tools.optimizationscipy.YawOptimization` that is used to diff --git a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py index 59f8593db..0c5d5a8e3 100644 --- a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py +++ b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_clustered.py @@ -17,12 +17,13 @@ import numpy as np import pandas as pd -from ....logging_manager import LoggerBase +from floris.logging_manager import LoggingManager + from .cluster_turbines import cluster_turbines from .yaw_wind_rose import YawOptimizationWindRose -class YawOptimizationWindRoseClustered(YawOptimizationWindRose, LoggerBase): +class YawOptimizationWindRoseClustered(YawOptimizationWindRose, LoggingManager): """ YawOptimizationWindRose is a subclass of :py:class:`~.tools.optimizationscipy.YawOptimizationWindRose` that is used diff --git a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py index e96854630..ec46763a5 100644 --- a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py +++ b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel.py @@ -18,11 +18,12 @@ import pandas as pd from scipy.optimize import minimize -from ....logging_manager import LoggerBase +from floris.logging_manager import LoggingManager + from .yaw_wind_rose import YawOptimizationWindRose -class YawOptimizationWindRoseParallel(YawOptimizationWindRose, LoggerBase): +class YawOptimizationWindRoseParallel(YawOptimizationWindRose, LoggingManager): """ YawOptimizationWindRose is a subclass of :py:class:`~.tools.optimizationscipy.YawOptimizationWindRose` that is used diff --git a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py index 4e6891698..caacc0429 100644 --- a/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py +++ b/floris/tools/optimization/legacy/scipy/yaw_wind_rose_parallel_clustered.py @@ -19,11 +19,12 @@ import pandas as pd from scipy.optimize import minimize -from ....logging_manager import LoggerBase +from floris.logging_manager import LoggingManager + from .yaw_wind_rose_clustered import YawOptimizationWindRoseClustered -class YawOptimizationWindRoseParallelClustered(YawOptimizationWindRoseClustered, LoggerBase): +class YawOptimizationWindRoseParallelClustered(YawOptimizationWindRoseClustered, LoggingManager): """ YawOptimizationWindRoseClustered is a subclass of :py:class:`~.tools.optimizationscipy.YawOptimizationWindRoseClustered` that diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py b/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py index b0b9ddeaf..baffb9822 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py +++ b/floris/tools/optimization/yaw_optimization/yaw_optimization_base.py @@ -19,12 +19,12 @@ import numpy as np import pandas as pd -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager from .yaw_optimization_tools import derive_downstream_turbines, find_layout_symmetry -class YawOptimization(LoggerBase): +class YawOptimization(LoggingManager): """ YawOptimization is a subclass of :py:class:`floris.tools.optimization.scipy. Optimization` that is used to optimize the yaw angles of all turbines in a Floris diff --git a/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py b/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py index 82d50ef08..6b0dbc4cf 100644 --- a/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py +++ b/floris/tools/optimization/yaw_optimization/yaw_optimizer_sr.py @@ -20,13 +20,13 @@ import numpy as np import pandas as pd -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager # from .yaw_optimizer_scipy import YawOptimizationScipy from .yaw_optimization_base import YawOptimization -class YawOptimizationSR(YawOptimization, LoggerBase): +class YawOptimizationSR(YawOptimization, LoggingManager): def __init__( self, fi, diff --git a/floris/tools/parallel_computing_interface.py b/floris/tools/parallel_computing_interface.py index b1808ddb5..1192fcfdb 100644 --- a/floris/tools/parallel_computing_interface.py +++ b/floris/tools/parallel_computing_interface.py @@ -6,7 +6,7 @@ import numpy as np import pandas as pd -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR from floris.tools.uncertainty_interface import FlorisInterface, UncertaintyInterface @@ -66,7 +66,7 @@ def _optimize_yaw_angles_serial( return df_opt -class ParallelComputingInterface(LoggerBase): +class ParallelComputingInterface(LoggingManager): def __init__( self, fi, diff --git a/floris/tools/sowfa_utilities.py b/floris/tools/sowfa_utilities.py deleted file mode 100644 index 3abec9691..000000000 --- a/floris/tools/sowfa_utilities.py +++ /dev/null @@ -1,639 +0,0 @@ -# Copyright 2021 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. - -# See https://floris.readthedocs.io for documentation - -import os -import re - -import numpy as np -import pandas as pd - -from ..logging_manager import LoggerBase -from ..utilities import Vec3 -from .cut_plane import CutPlane, get_plane_from_flow_data -from .flow_data import FlowData - - -class SowfaInterface(LoggerBase): - """ - Object to facilitate interaction with flow data output by SOWFA. - - Returns: - :py:class:`floris.tools.sowfa_utilities.SowfaInterface`: object - """ - - def __init__( - self, - case_folder, - flow_data_sub_path="array_mean/array.mean0D_UAvg.vtk", - setup_sub_path="setUp", - turbine_array_sub_path="constant/turbineArrayProperties", - turbine_sub_path="constant/turbineProperties", - controlDict_sub_path="system/controlDict", - turbine_output_sub_path="turbineOutput/20000", - assumed_settling_time=None, - ): - """ - SowfaInterface object init method. - - Args: - case_folder (str): path to folder containing SOWFA data - flow_data_sub_path (str, optional): path to mean data. - Defaults to 'array_mean/array.mean0D_UAvg.vtk'. - setup_sub_path (str, optional): path to setup info. - Defaults to 'setUp'. - turbine_array_sub_path (str, optional): path to wind plant - info. Defaults to 'constant/turbineArrayProperties'. - turbine_sub_path (str, optional): path to wind turbine - info. Defaults to 'constant/turbineProperties'. - controlDict_sub_path (str, optional): path to turbine - controls info. Defaults to 'system/controlDict'. - turbine_output_sub_path (str, optional): path to turbine - operational data. Defaults to 'turbineOutput/20000'. - assumed_settling_time (float, optional): Time to account - for startup transients in simulation. Defaults to None. - """ - self.logger.info(case_folder) - - # Save the case_folder and sub_paths - self.case_folder = case_folder - self.setup_sub_path = setup_sub_path - self.turbine_array_sub_path = turbine_array_sub_path - self.turbine_sub_path = turbine_sub_path - self.controlDict_sub_path = controlDict_sub_path - self.turbine_output_sub_path = turbine_output_sub_path - - # Read in the input files - - # Get control settings from sc input file - # TODO Assuming not dynamic and only one setting applied for each turbine - # TODO If not using the super controller sowfa variant, need alternative - - # Get the turbine name and locations - turbine_array_dict = read_foam_file( - os.path.join(self.case_folder, self.turbine_array_sub_path) - ) - self.turbine_name = turbine_array_dict["turbineType"].replace( - '"', "" - ) # TODO Assuming only one type - self.layout_x, self.layout_y = get_turbine_locations( - os.path.join(self.case_folder, self.turbine_array_sub_path) - ) - - # Save the number of turbines - self.num_turbines = len(self.layout_x) - - # if SC input exists, use it for yaw and pitch as it will over-ride - # if it does not exist, assume the values in turbineArray Properties - if os.path.exists(os.path.join(self.case_folder, "SC_INPUT.txt")): - df_SC = read_sc_input(self.case_folder) - self.yaw_angles = df_SC.yaw.values - self.pitch_angles = df_SC.pitch.values - else: - self.logger.info( - "No SC_INPUT.txt, getting pitch and yaw " + "from turbine array props" - ) - self.yaw_angles = get_turbine_yaw_angles( - os.path.join(self.case_folder, self.turbine_array_sub_path) - ) - self.pitch_angles = get_turbine_pitch_angles( - os.path.join(self.case_folder, self.turbine_array_sub_path) - ) - self.logger.info(self.yaw_angles) - self.logger.info(self.pitch_angles) - - # Get the turbine rotor diameter and hub height - turbine_dict = read_foam_file( - os.path.join(self.case_folder, self.turbine_sub_path, self.turbine_name) - ) - self.D = 2 * turbine_dict["TipRad"] - - # Use the setup file and control file to determine the precursor wind - # speed and the time flow averaging begins (settling time) - setup_dict = read_foam_file(os.path.join(self.case_folder, self.setup_sub_path)) - controlDict_dict = read_foam_file( - os.path.join(self.case_folder, self.controlDict_sub_path) - ) - start_run_time = controlDict_dict["startTime"] - averaging_start_time = setup_dict["meanStartTime"] - if assumed_settling_time is not None: - self.logger.info( - "Using assumed settling time of %.1f s" % assumed_settling_time - ) - self.settling_time = assumed_settling_time - else: - self.settling_time = averaging_start_time - start_run_time - self.precursor_wind_speed = setup_dict["U0Mag"] - - # Get the wind direction - self.precursor_wind_dir = setup_dict["dir"] - - # Get the surface roughness - self.z0 = setup_dict["z0"] - - # Read the outputs - self.turbine_output = read_sowfa_df( - os.path.join(self.case_folder, self.turbine_output_sub_path) - ) - - # Remove the settling time - self.turbine_output = self.turbine_output[ - self.turbine_output.time > self.settling_time - ] - - # Get the sim_time - self.sim_time_length = self.turbine_output.time.max() - - # Read the flow data - try: - self.flow_data = self.read_flow_frame_SOWFA( - os.path.join(case_folder, flow_data_sub_path) - ) - - # Re-set turbine positions to flow_field origin - self.layout_x = self.layout_x - self.flow_data.origin.x1 - self.layout_y = self.layout_y - self.flow_data.origin.x2 - - except FileNotFoundError: - self.logger.info("No flow field found, setting NULL, origin at 0") - self.flow_data = None # TODO might need a null flow-field - - # Try to work out the precursor directory - self.precursor_directory = "unknown" - try: - with open(os.path.join(case_folder, "runscript.preprocess"), "r") as fid: - raw = fid.readlines() - - for i, line in enumerate(raw): - if "precursorDir=" in line: - self.precursor_directory = os.path.basename( - line.replace("precursorDir=", "") - ) - - except FileNotFoundError: - self.logger.info("No preprocess file found") - - def __str__(self): - - self.logger.info("---------------------") - self.logger.info("Case: %s" % self.case_folder) - self.logger.info("==Turbine Info==") - self.logger.info("Turbine: %s" % self.turbine_name) - self.logger.info("Diameter: %dm" % self.D) - self.logger.info("Num Turbines = %d" % self.num_turbines) - self.logger.info("==Control Settings==") - self.logger.info("Yaw Angles, [" + ", ".join(map(str, self.yaw_angles)) + "]") - self.logger.info( - "Pitch Angles, [" + ", ".join(map(str, self.pitch_angles)) + "]" - ) - self.logger.info("==Inflow Info==") - self.logger.info("U0Mag: %.2fm/s" % self.precursor_wind_speed) - self.logger.info("dir: %.1f" % self.precursor_wind_dir) - self.logger.info("==Timing Info==") - self.logger.info("Settling time: %.1fs" % self.settling_time) - self.logger.info("Simulation time: %.1fs" % self.sim_time_length) - self.logger.info("---------------------") - - return " " - - def calculate_horizontal_plane( - self, height, x_resolution=200, y_resolution=200, x_bounds=None, y_bounds=None - ): - """ - Get a horizontal cut through plane at a specific height - - Args: - height (float): height of cut plane, defaults to hub-height - Defaults to Hub-height. - x1_resolution (float, optional): output array resolution. - Defaults to 200. - x2_resolution (float, optional): output array resolution. - Defaults to 200. - x1_bounds (tuple, optional): limits of output array. - Defaults to None. - x2_bounds (tuple, optional): limits of output array. - Defaults to None. - - Returns: - horplane - """ - # Get points from flow data - df = get_plane_from_flow_data( - self.flow_data, normal_vector="z", x3_value=height - ) - - # Compute and return the cutplane - return CutPlane(df) - - def calculate_cross_plane( - self, x_loc, x_resolution=200, y_resolution=200, x_bounds=None, y_bounds=None - ): - """ - Get a horizontal cut through plane at a specific height - - Args: - height (float): height of cut plane, defaults to hub-height - Defaults to Hub-height. - x1_resolution (float, optional): output array resolution. - Defaults to 200. - x2_resolution (float, optional): output array resolution. - Defaults to 200. - x1_bounds (tuple, optional): limits of output array. - Defaults to None. - x2_bounds (tuple, optional): limits of output array. - Defaults to None. - - Returns: - horplane - """ - # Get the points of data in a dataframe - df = get_plane_from_flow_data(self.flow_data, normal_vector="x", x3_value=x_loc) - - # Compute and return the cutplane - return CutPlane(df) - - def calculate_y_plane( - self, y_loc, x_resolution=200, y_resolution=200, x_bounds=None, y_bounds=None - ): - """ - Get a horizontal cut through plane at a specific height - - Args: - height (float): height of cut plane, defaults to hub-height - Defaults to Hub-height. - x1_resolution (float, optional): output array resolution. - Defaults to 200. - x2_resolution (float, optional): output array resolution. - Defaults to 200. - x1_bounds (tuple, optional): limits of output array. - Defaults to None. - x2_bounds (tuple, optional): limits of output array. - Defaults to None. - - Returns: - horplane - """ - # Get the points of data in a dataframe - df = get_plane_from_flow_data(self.flow_data, normal_vector="y", x3_value=y_loc) - - # Compute and return the cutplane - return CutPlane(df) - - def get_average_powers(self): - """ - Return the average power from the simulation per turbine - - Args: - - - Returns: - pow_list (numpy array): an array of powers per turbine - """ - pow_list = [] - for t in range(self.num_turbines): - df_sub = self.turbine_output[self.turbine_output.turbine == t] - pow_list.append(df_sub.powerGenerator.mean()) - return np.array(pow_list) - - def get_time_power_t(self, t): - """ - Return the power over time of a specific turbine t - - Args: - t, turbine number - - Returns: - power - """ - return self.turbine_output[self.turbine_output.turbine == t].powerGenerator - - def get_average_thrust(self): - """ - Return the average thrust from the simulation per turbine - - Args: - - - Returns: - pow_list (numpy array): an array of thrust per turbine - """ - thrust_list = [] - for t in range(self.num_turbines): - df_sub = self.turbine_output[self.turbine_output.turbine == t] - thrust_list.append(df_sub.thrust.mean()) - return np.array(thrust_list) - - def read_flow_frame_SOWFA(self, filename): - """ - Read flow array output from SOWFA - - Args: - filename (str): name of file containing flow data. - - Returns: - FlowData (pd.DataFrame): a pandas table with the columns, - of all relavent flow info (e.g. x, y, z, u, v, w). - """ - # Read the dimension info from the file - with open(filename, "r") as f: - for _ in range(10): - read_data = f.readline() - if "SPACING" in read_data: - splitstring = read_data.rstrip().split(" ") - spacing = Vec3( - float(splitstring[1]), - float(splitstring[2]), - float(splitstring[3]), - ) - if "DIMENSIONS" in read_data: - splitstring = read_data.rstrip().split(" ") - dimensions = Vec3( - int(splitstring[1]), int(splitstring[2]), int(splitstring[3]) - ) - if "ORIGIN" in read_data: - splitstring = read_data.rstrip().split(" ") - origin = Vec3( - float(splitstring[1]), - float(splitstring[2]), - float(splitstring[3]), - ) - - # Set up x, y, z as lists - if dimensions.x1 > 1.0: - xRange = np.arange(0, dimensions.x1 * spacing.x1, spacing.x1) - else: - xRange = np.array([0.0]) - - if dimensions.x2 > 1.0: - yRange = np.arange(0, dimensions.x2 * spacing.x2, spacing.x2) - else: - yRange = np.array([0.0]) - - if dimensions.x3 > 1.0: - zRange = np.arange(0, dimensions.x3 * spacing.x3, spacing.x3) - else: - zRange = np.array([0.0]) - - pts = np.array([(x, y, z) for z in zRange for y in yRange for x in xRange]) - - df = pd.read_csv( - filename, skiprows=10, sep="\t", header=None, names=["u", "v", "w"] - ) - x = pts[:, 0] - y = pts[:, 1] - z = pts[:, 2] - - return FlowData( - x, y, z, df.u.values, df.v.values, df.w.values, spacing, dimensions, origin - ) - - -def read_sc_input(case_folder, wind_direction=270.0): - """ - Read the super controller (SC) input file to get the wind farm - control settings. - - Args: - case_folder (str): path to folder containing SC data. - wind_direction (float, optional): Wind direction. - Defaults to 270.. - - Returns: - df_SC (pd.DataFrame): dataframe containing SC info. - """ - sc_file = os.path.join(case_folder, "SC_INPUT.txt") - - df_SC = pd.read_csv(sc_file, delim_whitespace=True) - - df_SC.columns = ["time", "turbine", "yaw", "pitch"] - - df_SC["yaw"] = wind_direction - df_SC.yaw - - df_SC = df_SC.set_index("turbine") - - return df_SC - - -def read_sowfa_df(folder_name, channels=[]): - """ - New function to use pandas to read in files using pandas - - Args: - folder_name (str): where to find the outputs of ALL channels, - not really used for now, but could be a list of desired - channels to only read. - channels (list, optional): list of specific channels to read. - Defaults to []. - """ - # Get the availble outputs - outputNames = [ - f - for f in os.listdir(folder_name) - if os.path.isfile(os.path.join(folder_name, f)) - ] - - # Remove the harder input files for now (undo someday) - # hardFiles = [ - # "Vtangential", - # "Cl", - # "Cd", - # "Vradial", - # "x", - # "y", - # "z", - # "alpha", - # "axialForce", - # ] - simpleFiles = [ - "nacYaw", - "rotSpeedFiltered", - "rotSpeed", - "thrust", - "torqueGen", - "powerRotor", - "powerGenerator", - "torqueRotor", - "azimuth", - "pitch", - ] - - # Limit to files - if len(channels) == 0: - outputNames = [o for o in outputNames if o in simpleFiles] - else: - outputNames = channels - - # Get the number of channels - num_channels = len(outputNames) - - if num_channels == 0: - raise ValueError("Is %s a data folder?" % folder_name) - - # Now loop through the files - for c_idx, chan in enumerate(outputNames): - - filename = os.path.join(folder_name, chan) - - # Load the file - df_inner = pd.read_csv(filename, sep=" ", header=None, skiprows=1) - - # Rename the columns - df_inner.columns = ["turbine", "time", "dt", chan] - - # Drop dt - df_inner = df_inner[["time", "turbine", chan]].set_index(["time", "turbine"]) - - # On first run declare the new frame - if c_idx == 0: - # Declare the main data frame to return as copy - df = df_inner.copy(deep=True) - - # On other loops just add the new frame - else: - df[chan] = df_inner[chan] - - # Reset the index - df = df.reset_index() - - # Zero the time - df["time"] = df.time - df.time.min() - - return df - - -def read_foam_file(filename): - """ - Method to read scalar and boolean/string inputs from an OpenFOAM - input file. - - Args: - filename (str): path to file to read. - - Returns: - data (dict): dictionary with OpenFOAM inputs - """ - data = {} - - with open(filename, "r") as fid: - raw = fid.readlines() - - bloc_comment_test = False - for i, line in enumerate(raw): - - if raw[i][0:2] == "/*": - bloc_comment_test = True - - if not bloc_comment_test: - - # Check if the string is a comment and skip line - if raw[i].strip()[0:2] == "//" or raw[i].strip()[0:1] == "#": - pass - - elif len(raw[i].strip()) == 0: # Check if the string is empty and skip line - pass - - else: - tmp = raw[i].strip().rstrip().split() - try: - data[tmp[0].replace('"', "")] = np.float(tmp[1][:-1]) - except Exception: - try: - data[tmp[0].replace('"', "")] = tmp[1][:-1] - except Exception: - next - - if raw[i][0:2] == r"\*": - bloc_comment_test = False - - return data - - -def get_turbine_locations(turbine_array_file): - """ - Extract wind turbine locations from SOWFA data. - - Args: - turbine_array_file (str): path to file containing wind plant - layout data. - - Returns: - layout_x (np.array): wind plant layout coodinates (east-west). - layout_y (np.array): wind plant layout coodinates (north-south). - """ - x = [] - y = [] - - with open(turbine_array_file, "r") as f: - for line in f: - if "baseLocation" in line: - # Extract the coordinates - data = re.findall(r"[-+]?\d*\.\d+|\d+", line) - - # Append the data - x.append(float(data[0])) - y.append(float(data[1])) - - layout_x = np.array(x) - layout_y = np.array(y) - - return layout_x, layout_y - - -def get_turbine_pitch_angles(turbine_array_file): - """ - Extract wind turbine blade pitch information from SOWFA data. - - Args: - turbine_array_file (str): path to file containing pitch info. - - Returns: - p (np.array): blade pitch info. - """ - p = [] - - with open(turbine_array_file, "r") as f: - for line in f: - if "Pitch" in line: - # Extract the coordinates - data = re.findall(r"[-+]?\d*\.\d+|\d+", line) - - # Append the data - p.append(float(data[0])) - - return np.array(p) - - -def get_turbine_yaw_angles(turbine_array_file, wind_direction=270.0): - """ - Extract wind turbine yaw angle information from SOWFA data. - - Args: - turbine_array_file (str): path to file containing yaw info. - wind_direction (float, optional): Wind direction. - Defaults to 270.. - - Returns: - y (np.array): wind turbine yaw info. - """ - y = [] - - with open(turbine_array_file, "r") as f: - for line in f: - if "NacYaw" in line: - # Extract the coordinates - data = re.findall(r"[-+]?\d*\.\d+|\d+", line) - - # Append the data - y.append(wind_direction - float(data[0])) - - return np.array(y) diff --git a/floris/tools/uncertainty_interface.py b/floris/tools/uncertainty_interface.py index b57685ac0..b871bd86d 100644 --- a/floris/tools/uncertainty_interface.py +++ b/floris/tools/uncertainty_interface.py @@ -17,12 +17,12 @@ import numpy as np from scipy.stats import norm -from floris.logging_manager import LoggerBase +from floris.logging_manager import LoggingManager from floris.tools import FlorisInterface from floris.utilities import wrap_360 -class UncertaintyInterface(LoggerBase): +class UncertaintyInterface(LoggingManager): def __init__( self, configuration, diff --git a/floris/tools/visualization.py b/floris/tools/visualization.py index aa4d83734..fe01a595b 100644 --- a/floris/tools/visualization.py +++ b/floris/tools/visualization.py @@ -17,17 +17,23 @@ import warnings from typing import Union +import attrs import matplotlib as mpl import matplotlib.colors as mplcolors import matplotlib.pyplot as plt import numpy as np import pandas as pd +from attrs import define, field from matplotlib import rcParams from scipy.spatial import ConvexHull from floris.simulation import Floris from floris.tools.cut_plane import CutPlane from floris.tools.floris_interface import FlorisInterface +from floris.type_dec import ( + floris_array_converter, + NDArrayFloat, +) from floris.utilities import rotate_coordinates_rel_west, wind_delta @@ -306,7 +312,7 @@ def visualize_cut_plane( # Make equal axis ax.set_aspect("equal") - return im + return ax def visualize_heterogeneous_cut_plane( @@ -651,11 +657,20 @@ def calculate_horizontal_plane_with_turbines( # Grab the turbine layout layout_x = copy.deepcopy(fi.layout_x) layout_y = copy.deepcopy(fi.layout_y) + turbine_types = copy.deepcopy(fi.floris.farm.turbine_type) D = fi.floris.farm.rotor_diameters_sorted[0, 0, 0] # Declare a new layout array with an extra turbine layout_x_test = np.append(layout_x,[0]) layout_y_test = np.append(layout_y,[0]) + + # Declare turbine types with an extra turbine in + # case of special one type useage + if len(layout_x) > 1 and len(turbine_types) == 1: + # Convert to list length len(layout_x) + 1 + turbine_types_test = [turbine_types[0] for i in range(len(layout_x))] + ['nrel_5MW'] + else: + turbine_types_test = np.append(turbine_types, 'nrel_5MW').tolist() yaw_angles = np.append(yaw_angles, np.zeros([len(wd), len(ws), 1]), axis=2) # Get a grid of points test test @@ -688,7 +703,11 @@ def calculate_horizontal_plane_with_turbines( # Place the test turbine at this location and calculate wake layout_x_test[-1] = x layout_y_test[-1] = y - fi.reinitialize(layout_x = layout_x_test, layout_y = layout_y_test) + fi.reinitialize( + layout_x=layout_x_test, + layout_y=layout_y_test, + turbine_type=turbine_types_test + ) fi.calculate_wake(yaw_angles=yaw_angles) # Get the velocity of that test turbines central point @@ -712,3 +731,199 @@ def calculate_horizontal_plane_with_turbines( horizontal_plane = CutPlane(df, x_resolution, y_resolution, "z") return horizontal_plane + +@define +class VelocityProfilesFigure(): + """ + Create a figure which displays velocity deficit profiles at several downstream + locations of a turbine. + + Args: + downstream_dists_D: A list/array of streamwise locations at which the velocity deficit + profiles have been sampled. The locations should be normalized by the turbine + diameter D. + layout: A one- or two-element list defining the direction of the profiles and in which + order the directions are plotted. For example, ['cross-stream', 'vertical'] initializes + a figure where cross-stream profiles are expected on the top row of Axes in the figure, + and vertical profiles are expected on the bottom row. + ax_width: Roughly the width of each Axes. + ax_height: Roughly the height of each Axes. + coordinate_labels: A list of labels for the normalized coordinates. + + """ + downstream_dists_D: NDArrayFloat = field(converter=floris_array_converter) + layout: list[str] = field(default=['cross-stream']) + ax_width: float = field(default=2.07) + ax_height: float = field(default=3.0) + coordinate_labels: list[str] = field(default=['x_1/D', 'x_2/D', 'x_3/D']) + + n_rows: int = field(init=False) + n_cols: int = field(init=False) + fig: plt.Figure = field(init=False) + axs: np.ndarray = field(init=False) + deficit_max: float = field(init=False, default=0.0) + + def __attrs_post_init__(self) -> None: + self.n_rows = len(self.layout) + self.n_cols = len(self.downstream_dists_D) + figsize = [0.7 + self.ax_width * self.n_cols, 1.0 + self.ax_height * self.n_rows] + self.fig, self.axs = plt.subplots( + self.n_rows, + self.n_cols, + figsize=figsize, + layout='tight', + sharex='col', + sharey='row', + squeeze=False, + ) + + for ax in self.axs[-1]: + ax.set_xlabel(r'$\Delta U / U_\infty$', fontsize=14) + ax.tick_params('x', labelsize=14) + + for ax, x1_D in zip(self.axs[0], self.downstream_dists_D): + ax.set_title(f'${self.coordinate_labels[0]} = {x1_D:.1f}$', fontsize=14) + + for ax, profile_direction in zip(self.axs[:,0], self.layout): + if profile_direction == 'cross-stream': + ylabel = f'${self.coordinate_labels[1]}$' + elif profile_direction == 'vertical': + ylabel = f'${self.coordinate_labels[2]}$' + ax.set_ylabel(ylabel, fontsize=14) + ax.tick_params('y', labelsize=14) + + @layout.validator + def layout_validator(self, instance : attrs.Attribute, value : list[str]) -> None: + allowed_layouts = [ + ['cross-stream'], + ['vertical'], + ['cross-stream', 'vertical'], + ['vertical', 'cross-stream'], + ] + if value not in allowed_layouts: + raise ValueError(f"'layout' must be one of the following: {allowed_layouts}.") + + def add_profiles( + self, + velocity_deficit_profiles: list[pd.DataFrame], + **kwargs + ) -> None: + """ + Add a list of velocity deficit profiles to the figure. Each profile is represented + as a pandas DataFrame. `kwargs` are passed to `ax.plot`. + """ + for df in velocity_deficit_profiles: + ax, profile_direction = self.match_profile_to_axes(df) + profile_direction_D = f'{profile_direction}/D' + ax.plot(df['velocity_deficit'], df[profile_direction_D], **kwargs) + self.deficit_max = max(self.deficit_max, df['velocity_deficit'].max()) + + margin = 0.05 + self.set_xlim([0.0 - margin, self.deficit_max + margin]) + + def match_profile_to_axes( + self, + df: pd.DataFrame, + ) -> tuple[plt.Axes, str]: + x1_D = np.unique(df['x1/D']) + if len(x1_D) == 1: + x1_D = x1_D[0] + else: + raise ValueError( + "The streamwise location x1/D must be constant for each velocity profile." + ) + + unique_x2 = np.unique(df['x2/D']) + unique_x3 = np.unique(df['x3/D']) + if len(unique_x2) == 1: + profile_direction = 'x3' + profile_direction_name = 'vertical' + elif len(unique_x3) == 1: + profile_direction = 'x2' + profile_direction_name = 'cross-stream' + else: + raise ValueError( + f"Velocity deficit profile at x1/D = {x1_D} is neither in the cross-stream (x2) " + "nor the vertical (x3) direction." + ) + row = self.layout.index(profile_direction_name) + + col = None + for i in range(self.n_cols): + if np.abs(x1_D - self.downstream_dists_D[i]) < 0.001: + col = i + break + if col is None: + raise ValueError( + "Could not add a velocity deficit profile at downstream distance " + f"x1/D = {x1_D}. The downstream distance must be one of the following " + "values with which this VelocityProfilesFigure object was initialized: " + f"{self.downstream_dists_D}." + ) + return self.axs[row,col], profile_direction + + def set_xlim( + self, + xlim: list[float] | NDArrayFloat, + ) -> None: + for ax in self.axs[-1]: + ax.set_xlim(xlim) + + def add_ref_lines_x2( + self, + ref_lines_x2_D: list[float] | NDArrayFloat, + **kwargs + ) -> None: + """ + Add reference lines to the VelocityProfilesFigure which go along the XAxis. + Commonly used to show the extent of the turbine. + Args: + ref_lines_x2_D: A list of x2-coordinates normalized by the turbine diameter D. + One coordinate per reference line. + **kwargs: Additional parameters to pass to `ax.plot`. + """ + if 'cross-stream' not in self.layout: + raise Exception( + "Could not add reference lines to cross-stream (x2) velocity profiles. No " + "such profiles exist in the figure." + ) + row_x2 = self.layout.index('cross-stream') + self.add_ref_lines(ref_lines_x2_D, row_x2, **kwargs) + + def add_ref_lines_x3( + self, + ref_lines_x3_D: list[float] | NDArrayFloat, + **kwargs + ) -> None: + """ + Add reference lines to the VelocityProfilesFigure which go along the XAxis. + Commonly used to show the extent of the turbine. + Args: + ref_lines_x3_D: A list of x3-coordinates normalized by the turbine diameter D. + One coordinate per reference line. + **kwargs: Additional parameters to pass to `ax.plot`. + """ + if 'vertical' not in self.layout: + raise Exception( + "Could not add reference lines to vertical (x3) velocity profiles. No " + "such profiles exist in the figure." + ) + row_x3 = self.layout.index('vertical') + self.add_ref_lines(ref_lines_x3_D, row_x3, **kwargs) + + def add_ref_lines( + self, + ref_lines_D: list[float] | NDArrayFloat, + row: int, + **kwargs + ) -> None: + default_params = { + 'linestyle': (0, (4, 2)), + 'color': 'k', + 'linewidth': 1.1 + } + kwargs = default_params | kwargs + + for ax in self.axs[row]: + for coordinate in ref_lines_D: + ax.plot([0.0, 1.0], [coordinate, coordinate], **kwargs) diff --git a/floris/turbine_library/__init__.py b/floris/turbine_library/__init__.py index 828c50eb2..933615b0c 100644 --- a/floris/turbine_library/__init__.py +++ b/floris/turbine_library/__init__.py @@ -1 +1,2 @@ from floris.turbine_library.turbine_previewer import TurbineInterface, TurbineLibrary +from floris.turbine_library.turbine_utilities import build_turbine_dict diff --git a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml index ea8623eee..58b2b3a1f 100644 --- a/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml +++ b/floris/turbine_library/iea_15MW_floating_multi_dim_cp_ct.yaml @@ -8,7 +8,7 @@ TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 6.0 multi_dimensional_cp_ct: True -power_thrust_data_file: '../floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv' +power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv' floating_tilt_table: tilt: - 5.747296314800103 @@ -18,7 +18,7 @@ floating_tilt_table: - 8.795649572299896 - 8.089078308325314 - 7.7229584934943614 - wind_speeds: + wind_speed: - 4.0 - 6.0 - 8.0 @@ -26,4 +26,4 @@ floating_tilt_table: - 12.0 - 14.0 - 16.0 -floating_correct_cp_ct_for_tilt: True +correct_cp_ct_for_tilt: True diff --git a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml index 51e0a83f6..d01e52633 100644 --- a/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml +++ b/floris/turbine_library/iea_15MW_multi_dim_cp_ct.yaml @@ -8,4 +8,4 @@ TSR: 8.0 ref_density_cp_ct: 1.225 ref_tilt_cp_ct: 6.0 multi_dimensional_cp_ct: True -power_thrust_data_file: '../floris/turbine_library/iea_15MW_multi_dim_Tp_Hs.csv' +power_thrust_data_file: 'iea_15MW_multi_dim_Tp_Hs.csv' diff --git a/floris/turbine_library/turbine_previewer.py b/floris/turbine_library/turbine_previewer.py index 3d8374460..bb1ab0cd7 100644 --- a/floris/turbine_library/turbine_previewer.py +++ b/floris/turbine_library/turbine_previewer.py @@ -12,6 +12,8 @@ # See https://floris.readthedocs.io for documentation +from __future__ import annotations + from pathlib import Path import attrs @@ -19,12 +21,19 @@ import numpy as np from attrs import define, field -from floris.simulation import ( +from floris.simulation.turbine import ( Ct, power, Turbine, ) -from floris.type_dec import NDArrayFloat +from floris.simulation.turbine_multi_dim import ( + Ct_multidim, + multidim_Ct_down_select, + multidim_power_down_select, + power_multidim, + TurbineMultiDimensional, +) +from floris.type_dec import convert_to_path, NDArrayFloat from floris.utilities import ( load_yaml, round_nearest, @@ -38,21 +47,35 @@ @define(auto_attribs=True) class TurbineInterface: - turbine: Turbine = field(validator=attrs.validators.instance_of(Turbine)) + turbine: Turbine | TurbineMultiDimensional = field( + validator=attrs.validators.instance_of((Turbine, TurbineMultiDimensional)) + ) @classmethod - def from_internal_library(cls, file_name: str): - """Loads the turbine definition from a YAML configuration file located in - ``floris/floris/turbine_library/``. + def from_library(cls, library_path: str | Path, file_name: str): + """Loads the turbine definition from a YAML configuration file located in either the + internal turbine library ``floris/floris/turbine_library/``, or a user-specified location. Args: - file_`name : str | Path - T`he file name of the turbine configuration file. + library_path (:obj:`str` | :obj:`pathlib.Path`): The location of the turbine library; + use "internal" to use the FLORIS-provided library. + file_name (:obj:`str` | :obj:`pathlib.Path`): The name of the configuration file. Returns: (TurbineInterface): Creates a new ``TurbineInterface`` object. """ - return cls(turbine=Turbine.from_dict(load_yaml(INTERNAL_LIBRARY / file_name))) + # Use the pre-mapped internal turbine library or validate the user's library + if library_path == "internal": + library_path = INTERNAL_LIBRARY + else: + library_path = convert_to_path(library_path) + + # Add in the library specification if needed, and load from dict + turb_dict = load_yaml(library_path / file_name) + if turb_dict.get("multi_dimensional_cp_ct", False): + turb_dict.setdefault("turbine_library_path", library_path) + return cls(turbine=TurbineMultiDimensional.from_dict(turb_dict)) + return cls(turbine=Turbine.from_dict(turb_dict)) @classmethod def from_yaml(cls, file_path: str | Path): @@ -65,7 +88,14 @@ def from_yaml(cls, file_path: str | Path): Returns: (TurbineInterface): Creates a new ``TurbineInterface`` object. """ - return cls(turbine=Turbine.from_dict(load_yaml(file_path))) + file_path = Path(file_path).resolve() + + # Add in the library specification if needed, and load from dict + turb_dict = load_yaml(file_path) + if turb_dict.get("multi_dimensional_cp_ct", False): + turb_dict.setdefault("turbine_library_path", file_path.parent) + return cls(turbine=TurbineMultiDimensional.from_dict(turb_dict)) + return cls(turbine=Turbine.from_dict(turb_dict)) @classmethod def from_turbine_dict(cls, config_dict: dict): @@ -78,12 +108,14 @@ def from_turbine_dict(cls, config_dict: dict): Returns: (`TurbineInterface`): Returns a ``TurbineInterface`` object. """ + if config_dict.get("multi_dimensional_cp_ct", False): + return cls(turbine=TurbineMultiDimensional.from_dict(config_dict)) return cls(turbine=Turbine.from_dict(config_dict)) - def power_curve( + def power_curve( self, wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - ) -> tuple[NDArrayFloat, NDArrayFloat]: + ) -> tuple[NDArrayFloat, NDArrayFloat] | tuple[NDArrayFloat, dict[tuple, NDArrayFloat]]: """Produces a plot-ready power curve for the turbine for wind speed vs power (MW), assuming no tilt or yaw effects. @@ -92,36 +124,36 @@ def power_curve( 0 m/s -> 40 m/s, every 0.5 m/s. Returns: - (tuple[NDArrayFloat, NDArrayFloat]): Returns the wind speed array and the power array. + (tuple[NDArrayFloat, NDArrayFloat] | tuple[NDArrayFloat, dict[tuple, NDArrayFloat]]): + Returns the wind speed array and the power array, or the wind speed array and a + dictionary of the multidimensional parameters and their associated power arrays. """ shape = (1, wind_speeds.size, 1) - power_mw = power( - ref_density_cp_ct=np.full(shape, self.turbine.ref_density_cp_ct), - rotor_effective_velocities=wind_speeds.reshape(shape), - power_interp={self.turbine.turbine_type: self.turbine.power_interp}, - turbine_type_map=np.full(shape, self.turbine.turbine_type) - ).flatten() / 1e6 + if self.turbine.multi_dimensional_cp_ct: + power_interps = { + k: multidim_power_down_select( + np.full(shape, self.turbine.power_interp), + dict(zip(self.turbine.condition_keys, k)), + ) + for k in self.turbine.power_interp + } + power_mw = { + k: power_multidim( + ref_density_cp_ct=np.full(shape, self.turbine.ref_density_cp_ct), + rotor_effective_velocities=wind_speeds.reshape(shape), + power_interp=power_interps[k], + ).flatten() / 1e6 + for k in self.turbine.power_interp + } + else: + power_mw = power( + ref_density_cp_ct=np.full(shape, self.turbine.ref_density_cp_ct), + rotor_effective_velocities=wind_speeds.reshape(shape), + power_interp={self.turbine.turbine_type: self.turbine.power_interp}, + turbine_type_map=np.full(shape, self.turbine.turbine_type) + ).flatten() / 1e6 return wind_speeds, power_mw - def Cp_curve( - self, - wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - ) -> tuple[NDArrayFloat, NDArrayFloat]: - """Produces a plot-ready thrust curve for the turbine for wind speed vs power coefficient - assuming no tilt or yaw effects. - - Args: - wind_speeds : NDArrayFloat, optional - The wind speed conditions to produce the power curve for, by default 0 m/s -> 40 m/s, - every 0.5 m/s. - - Returns: - tuple[NDArrayFloat, NDArrayFloat] - Returns the wind speed array and the power coefficient array. - """ - cp_curve = self.turbine.fCp_interp(wind_speeds) - return wind_speeds, cp_curve - def Ct_curve( self, wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, @@ -138,23 +170,47 @@ def Ct_curve( Returns the wind speed array and the thrust coefficient array. """ shape = (1, wind_speeds.size, 1) - ct_curve = Ct( - velocities=wind_speeds.reshape(shape), - yaw_angle=np.zeros(shape), - tilt_angle=np.full(shape, self.turbine.ref_tilt_cp_ct), - ref_tilt_cp_ct=np.full(shape, self.turbine.ref_tilt_cp_ct), - fCt={self.turbine.turbine_type: self.turbine.fCt_interp}, - tilt_interp=[(self.turbine.turbine_type, self.turbine.fTilt_interp)], - correct_cp_ct_for_tilt=np.zeros(shape, dtype=bool), - turbine_type_map=np.full(shape, self.turbine.turbine_type), - ).flatten() + shape_single = (1, 1, 1) + if self.turbine.multi_dimensional_cp_ct: + fCt_interps = { + k: multidim_Ct_down_select( + np.full(shape, self.turbine.fCt_interp), + dict(zip(self.turbine.condition_keys, k)), + ) + for k in self.turbine.fCt_interp + } + ct_curve = { + k: Ct_multidim( + velocities=wind_speeds.reshape(shape), + yaw_angle=np.zeros(shape), + tilt_angle=np.full(shape, self.turbine.ref_tilt_cp_ct), + ref_tilt_cp_ct=np.full(shape_single, self.turbine.ref_tilt_cp_ct), + fCt=fCt_interps[k], + tilt_interp={self.turbine.turbine_type: self.turbine.tilt_interp}, + correct_cp_ct_for_tilt=np.zeros(shape_single, dtype=bool), + turbine_type_map=np.full(shape_single, self.turbine.turbine_type) + ).flatten() + for k in self.turbine.fCt_interp + } + else: + ct_curve = Ct( + velocities=wind_speeds.reshape(shape), + yaw_angle=np.zeros(shape), + tilt_angle=np.full(shape, self.turbine.ref_tilt_cp_ct), + ref_tilt_cp_ct=np.full(shape, self.turbine.ref_tilt_cp_ct), + fCt={self.turbine.turbine_type: self.turbine.fCt_interp}, + tilt_interp={self.turbine.turbine_type: self.turbine.tilt_interp}, + correct_cp_ct_for_tilt=np.zeros(shape, dtype=bool), + turbine_type_map=np.full(shape, self.turbine.turbine_type), + ).flatten() return wind_speeds, ct_curve def plot_power_curve( self, wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, + fig_kwargs: dict | None = None, + plot_kwargs: dict | None = None, + legend_kwargs: dict | None = None, return_fig: bool = False ) -> None | tuple[plt.Figure, plt.Axes]: """Plots the power curve for a given set of wind speeds. @@ -163,9 +219,11 @@ def plot_power_curve( wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to 0 m/s -> 40 m/s, every 0.5 m/s. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. + Defaults to None. plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + legend_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.legend()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. @@ -175,6 +233,11 @@ def plot_power_curve( """ wind_speeds, power_mw = self.power_curve(wind_speeds=wind_speeds) + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + plot_kwargs = {} if plot_kwargs is None else plot_kwargs + legend_kwargs = {} if legend_kwargs is None else legend_kwargs + # Set the figure defaults if none are provided fig_kwargs.setdefault("dpi", 200) fig_kwargs.setdefault("figsize", (4, 3)) @@ -185,12 +248,20 @@ def plot_power_curve( min_windspeed = 0 max_windspeed = max(wind_speeds) min_power = 0 - max_power = max(power_mw) - ax.plot(wind_speeds, power_mw, label=self.turbine.turbine_type, **plot_kwargs) + max_power = 0 + if isinstance(power_mw, dict): + for key, _power_mw in power_mw.items(): + max_power = max(max_power, *_power_mw) + _cond = "; ".join((f"{c}: {k}" for c, k in zip(self.turbine.condition_keys, key))) + label = f"{self.turbine.turbine_type} - {_cond}" + ax.plot(wind_speeds, _power_mw, label=label, **plot_kwargs) + else: + max_power = max(power_mw) + ax.plot(wind_speeds, power_mw, label=self.turbine.turbine_type, **plot_kwargs) ax.grid() ax.set_axisbelow(True) - ax.legend() + ax.legend(**legend_kwargs) max_power = round_nearest_2_or_5(max_power) ax.set_xlim(min_windspeed, max_windspeed) @@ -204,62 +275,12 @@ def plot_power_curve( fig.tight_layout() - def plot_Cp_curve( - self, - wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, - return_fig: bool = False - ) -> None | tuple[plt.Figure, plt.Axes]: - """Plots the power coefficient curve for a given set of wind speeds. - - Args: - wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to - 0 m/s -> 40 m/s, every 0.5 m/s. - fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. - plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. - return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be - returned. Defaults to False. - - Returns: - None | tuple[plt.Figure, plt.Axes]: None, if :py:attr:`return_fig` is False, otherwise - a tuple of the Figure and Axes objects are returned. - """ - wind_speeds, power_c = self.Cp_curve(wind_speeds=wind_speeds) - - # Set the figure defaults if none are provided - fig_kwargs.setdefault("dpi", 200) - fig_kwargs.setdefault("figsize", (4, 3)) - - fig = plt.figure(**fig_kwargs) - ax = fig.add_subplot(111) - - min_windspeed = 0 - max_windspeed = max(wind_speeds) - ax.plot(wind_speeds, power_c, label=self.turbine.turbine_type, **plot_kwargs) - - ax.grid() - ax.set_axisbelow(True) - ax.legend() - - ax.set_xlim(min_windspeed, max_windspeed) - ax.set_ylim(0, round_nearest(max(power_c) * 100, base=10) / 100) - - ax.set_xlabel("Wind Speed (m/s)") - ax.set_ylabel("Power Coefficient") - - if return_fig: - return fig, ax - - fig.tight_layout() - def plot_Ct_curve( self, wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, + fig_kwargs: dict | None = None, + plot_kwargs: dict | None = None, + legend_kwargs: dict | None = None, return_fig: bool = False ) -> None | tuple[plt.Figure, plt.Axes]: """Plots the thrust coefficient curve for a given set of wind speeds. @@ -268,9 +289,11 @@ def plot_Ct_curve( wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to 0 m/s -> 40 m/s, every 0.5 m/s. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. + Defaults to None. plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + legend_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.legend()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. @@ -280,6 +303,11 @@ def plot_Ct_curve( """ wind_speeds, thrust = self.Ct_curve(wind_speeds=wind_speeds) + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + plot_kwargs = {} if plot_kwargs is None else plot_kwargs + legend_kwargs = {} if legend_kwargs is None else legend_kwargs + # Set the figure defaults if none are provided fig_kwargs.setdefault("dpi", 200) fig_kwargs.setdefault("figsize", (4, 3)) @@ -288,15 +316,24 @@ def plot_Ct_curve( ax = fig.add_subplot(111) min_windspeed = 0 + max_thrust = 0 max_windspeed = max(wind_speeds) - ax.plot(wind_speeds, thrust, label=self.turbine.turbine_type, **plot_kwargs) + if isinstance(thrust, dict): + for key, _thrust in thrust.items(): + max_thrust = max(max_thrust, *_thrust) + _cond = "; ".join((f"{c}: {k}" for c, k in zip(self.turbine.condition_keys, key))) + label = f"{self.turbine.turbine_type} - {_cond}" + ax.plot(wind_speeds, _thrust, label=label, **plot_kwargs) + else: + max_thrust = max(thrust) + ax.plot(wind_speeds, thrust, label=self.turbine.turbine_type, **plot_kwargs) ax.grid() ax.set_axisbelow(True) - ax.legend() + ax.legend(**legend_kwargs) ax.set_xlim(min_windspeed, max_windspeed) - ax.set_ylim(0, round_nearest(max(thrust) * 100, base=10) / 100) + ax.set_ylim(0, round_nearest(max_thrust * 100, base=10) / 100) ax.set_xlabel("Wind Speed (m/s)") ax.set_ylabel("Thrust Coefficient") @@ -378,21 +415,6 @@ def compute_power_curves( name: t.power_curve(wind_speeds) for name, t in self.turbine_map.items() } - def compute_Cp_curves( - self, - wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - ) -> None: - """Computes the power coefficient curves for each turbine in ``turbine_map`` and sets the - ``Ct_curves`` attribute. - - Args: - wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to - 0 m/s -> 40 m/s, every 0.5 m/s. - """ - self.Cp_curves = { - name: t.Cp_curve(wind_speeds) for name, t in self.turbine_map.items() - } - def compute_Ct_curves( self, wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, @@ -415,8 +437,9 @@ def plot_power_curves( which: list[str] = [], exclude: list[str] = [], wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, + fig_kwargs: dict | None = None, + plot_kwargs: dict | None = None, + legend_kwargs: dict | None = None, return_fig: bool = False, show: bool = False, ) -> None | tuple[plt.Figure, plt.Axes]: @@ -432,9 +455,11 @@ def plot_power_curves( wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to 0 m/s -> 40 m/s, every 0.5 m/s. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. + Defaults to None. plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + legend_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.legend()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. show (bool, optional): Indicator if the figure should be automatically displayed. @@ -449,6 +474,11 @@ def plot_power_curves( which = [*self.turbine_map] if which == [] else which + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + plot_kwargs = {} if plot_kwargs is None else plot_kwargs + legend_kwargs = {} if legend_kwargs is None else legend_kwargs + # Set the figure defaults if none are provided if fig is None: fig_kwargs.setdefault("dpi", 200) @@ -465,13 +495,20 @@ def plot_power_curves( for name, (ws, p) in self.power_curves.items(): if name in exclude or name not in which: continue - max_power = max(p.max(), max_power) - max_windspeed = max(ws.max(), max_windspeed) - ax.plot(ws, p, label=name, **plot_kwargs) + if isinstance(p, dict): + max_windspeed = max(ws.max(), max_windspeed) + for k, _p in p.items(): + max_power = max(_p.max(), max_power) + label = f"{name} - {k}" + ax.plot(ws, _p, label=label, linestyle="--", **plot_kwargs) + else: + max_power = max(p.max(), max_power) + max_windspeed = max(ws.max(), max_windspeed) + ax.plot(ws, p, label=name, **plot_kwargs) ax.grid() ax.set_axisbelow(True) - ax.legend() + ax.legend(**legend_kwargs) max_power = round_nearest(max_power, base=5) ax.set_xlim(min_windspeed, max_windspeed) @@ -486,82 +523,6 @@ def plot_power_curves( if show: fig.tight_layout() - def plot_Cp_curves( - self, - fig: plt.Figure | None = None, - ax: plt.Axes | None = None, - which: list[str] = [], - exclude: list[str] = [], - wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, - return_fig: bool = False, - show: bool = False, - ) -> None | tuple[plt.Figure, plt.Axes]: - """Plots each power coefficient curve in ``turbine_map`` in a single plot. - - Args: - fig (plt.figure, optional): A pre-made figure where the plot should exist. - ax (plt.Axes, optional): A pre-initialized axes object that should be used for the plot. - which (list[str], optional): A list of which turbine types/names to include. Defaults to - []. - exclude (list[str], optional): A list of turbine types/names names to exclude. Defaults - to []. - wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to - 0 m/s -> 40 m/s, every 0.5 m/s. - fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. - plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. - return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be - returned. Defaults to False. - show (bool, optional): Indicator if the figure should be automatically displayed. - Defaults to False. - - Returns: - None | tuple[plt.Figure, plt.Axes]: None, if :py:attr:`return_fig` is False, otherwise - a tuple of the Figure and Axes objects are returned. - """ - if self.Cp_curves == {} or wind_speeds is None: - self.compute_Cp_curves(wind_speeds=wind_speeds) - - which = [*self.turbine_map] if which == [] else which - - # Set the figure defaults if none are provided - if fig is None: - fig_kwargs.setdefault("dpi", 200) - fig_kwargs.setdefault("figsize", (4, 3)) - - fig = plt.figure(**fig_kwargs) - if ax is None: - ax = fig.add_subplot(111) - - min_windspeed = 0 - max_windspeed = 0 - max_power = 0 - for name, (ws, p) in self.Cp_curves.items(): - if name in exclude or name not in which: - continue - max_windspeed = max(ws.max(), max_windspeed) - max_power = max(p.max(), max_power) - ax.plot(ws, p, label=name, **plot_kwargs) - - ax.grid() - ax.set_axisbelow(True) - ax.legend() - - ax.set_xlim(min_windspeed, max_windspeed) - ax.set_ylim(0, round_nearest(max_power * 100, base=10) / 100) - - ax.set_xlabel("Wind Speed (m/s)") - ax.set_ylabel("Power Coefficient") - - if return_fig: - return fig, ax - - if show: - fig.tight_layout() - def plot_Ct_curves( self, fig: plt.Figure | None = None, @@ -569,8 +530,9 @@ def plot_Ct_curves( which: list[str] = [], exclude: list[str] = [], wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, + fig_kwargs: dict | None = None, + plot_kwargs: dict | None = None, + legend_kwargs: dict | None = None, return_fig: bool = False, show: bool = False, ) -> None | tuple[plt.Figure, plt.Axes]: @@ -586,9 +548,11 @@ def plot_Ct_curves( wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to 0 m/s -> 40 m/s, every 0.5 m/s. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. + Defaults to None. plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.legend()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. show (bool, optional): Indicator if the figure should be automatically displayed. @@ -603,6 +567,12 @@ def plot_Ct_curves( which = [*self.turbine_map] if which == [] else which + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + plot_kwargs = {} if plot_kwargs is None else plot_kwargs + legend_kwargs = {} if legend_kwargs is None else legend_kwargs + + # Set the figure defaults if none are provided if fig is None: fig_kwargs.setdefault("dpi", 200) @@ -618,13 +588,20 @@ def plot_Ct_curves( for name, (ws, t) in self.Ct_curves.items(): if name in exclude or name not in which: continue - max_windspeed = max(ws.max(), max_windspeed) - max_thrust = max(t.max(), max_thrust) - ax.plot(ws, t, label=name, **plot_kwargs) + if isinstance(t, dict): + max_windspeed = max(ws.max(), max_windspeed) + for k, _t in t.items(): + max_thrust = max(_t.max(), max_thrust) + label = f"{name} - {k}" + ax.plot(ws, _t, label=label, linestyle="--", **plot_kwargs) + else: + max_windspeed = max(ws.max(), max_windspeed) + max_thrust = max(t.max(), max_thrust) + ax.plot(ws, t, label=name, **plot_kwargs) ax.grid() ax.set_axisbelow(True) - ax.legend() + ax.legend(**legend_kwargs) ax.set_xlim(min_windspeed, max_windspeed) ax.set_ylim(0, round_nearest(max_thrust * 100, base=10) / 100) @@ -644,8 +621,8 @@ def plot_rotor_diameters( ax: plt.Axes | None = None, which: list[str] = [], exclude: list[str] = [], - fig_kwargs: dict = {}, - bar_kwargs = {}, + fig_kwargs: dict | None = None, + bar_kwargs: dict | None = None, return_fig: bool = False, show: bool = False, ) -> None | tuple[plt.Figure, plt.Axes]: @@ -659,9 +636,9 @@ def plot_rotor_diameters( exclude (list[str], optional): A list of turbine types/names names to exclude. Defaults to []. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. - bar_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + bar_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.bar()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. show (bool, optional): Indicator if the figure should be automatically displayed. @@ -673,6 +650,10 @@ def plot_rotor_diameters( """ which = [*self.turbine_map] if which == [] else which + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + bar_kwargs = {} if bar_kwargs is None else bar_kwargs + # Set the figure defaults if none are provided if fig is None: fig_kwargs.setdefault("dpi", 200) @@ -699,7 +680,7 @@ def plot_rotor_diameters( ax.set_ylim(0, round_nearest(max(y) / 10, base=5) * 10) ax.set_xticks(x) - ax.set_xticklabels(np.array([*subset_map])[ix_sort]) + ax.set_xticklabels(np.array([*subset_map])[ix_sort], rotation=30, ha="right") ax.set_ylabel("Rotor Diameter (m)") if return_fig: @@ -714,8 +695,8 @@ def plot_hub_heights( ax: plt.Axes | None = None, which: list[str] = [], exclude: list[str] = [], - fig_kwargs: dict = {}, - bar_kwargs = {}, + fig_kwargs: dict | None = None, + bar_kwargs: dict | None = None, return_fig: bool = False, show: bool = False, ) -> None | tuple[plt.Figure, plt.Axes]: @@ -729,9 +710,9 @@ def plot_hub_heights( exclude (list[str], optional): A list of turbine types/names names to exclude. Defaults to []. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. - bar_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. + bar_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.bar()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. show (bool, optional): Indicator if the figure should be automatically displayed. @@ -743,6 +724,10 @@ def plot_hub_heights( """ which = [*self.turbine_map] if which == [] else which + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + bar_kwargs = {} if bar_kwargs is None else bar_kwargs + # Set the figure defaults if none are provided if fig is None: fig_kwargs.setdefault("dpi", 200) @@ -769,7 +754,7 @@ def plot_hub_heights( ax.set_ylim(0, round_nearest(max(y) / 10, base=5) * 10) ax.set_xticks(x) - ax.set_xticklabels(np.array([*subset_map])[ix_sort]) + ax.set_xticklabels(np.array([*subset_map])[ix_sort], rotation=30, ha="right") ax.set_ylabel("Hub Height (m)") if return_fig: @@ -783,9 +768,10 @@ def plot_comparison( which: list[str] = [], exclude: list[str] = [], wind_speeds: NDArrayFloat = DEFAULT_WIND_SPEEDS, - fig_kwargs: dict = {}, - plot_kwargs = {}, - bar_kwargs = {}, + fig_kwargs: dict | None = None, + plot_kwargs: dict | None = None, + bar_kwargs: dict | None = None, + legend_kwargs: dict | None = None, return_fig: bool = False ) -> None | tuple[plt.Figure, list[plt.Axes]]: """Plots each thrust curve in ``turbine_map`` in a single plot. @@ -798,11 +784,13 @@ def plot_comparison( wind_speeds (NDArrayFloat, optional): A 1-D array of wind speeds, in m/s. Defaults to 0 m/s -> 40 m/s, every 0.5 m/s. fig_kwargs (dict, optional): Any keywords arguments to be passed to ``plt.Figure()``. - Defaults to {}. + Defaults to None. plot_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.plot()``. - Defaults to {}. + Defaults to None. bar_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.bar()``. - Defaults to {}. + Defaults to None. + legend_kwargs (dict, optional): Any keyword arguments to be passed to ``plt.legend()``. + Defaults to None. return_fig (bool, optional): Indicator if the ``Figure`` and ``Axes`` objects should be returned. Defaults to False. @@ -810,17 +798,23 @@ def plot_comparison( None | tuple[plt.Figure, list[plt.Axes]]: None, if :py:attr:`return_fig` is False, otherwise a tuple of the Figure and Axes objects are returned. """ + # Initialize kwargs if None + fig_kwargs = {} if fig_kwargs is None else fig_kwargs + plot_kwargs = {} if plot_kwargs is None else plot_kwargs + bar_kwargs = {} if bar_kwargs is None else bar_kwargs + legend_kwargs = {} if legend_kwargs is None else legend_kwargs + # Set the figure defaults if none are provided fig_kwargs.setdefault("dpi", 200) fig_kwargs.setdefault("figsize", (6, 5)) + legend_kwargs.setdefault("fontsize", 6) fig = plt.figure(**fig_kwargs) ax1 = fig.add_subplot(321) ax2 = fig.add_subplot(322) ax3 = fig.add_subplot(323) ax4 = fig.add_subplot(324) - ax5 = fig.add_subplot(325) - ax_list = [ax1, ax2, ax3, ax4, ax5] + ax_list = [ax1, ax2, ax3, ax4] self.plot_power_curves( fig, @@ -830,17 +824,9 @@ def plot_comparison( wind_speeds=wind_speeds, plot_kwargs=plot_kwargs, ) - self.plot_Cp_curves( - fig, - ax3, - which=which, - exclude=exclude, - wind_speeds=wind_speeds, - plot_kwargs=plot_kwargs, - ) self.plot_Ct_curves( fig, - ax5, + ax3, which=which, exclude=exclude, wind_speeds=wind_speeds, @@ -854,8 +840,8 @@ def plot_comparison( ax.xaxis.label.set_size(7) ax.yaxis.label.set_size(8) - for ax in (ax1, ax3, ax5): - ax.legend(fontsize=6) + for ax in (ax1, ax3): + ax.legend(**legend_kwargs) if return_fig: return fig, ax_list diff --git a/floris/turbine_library/turbine_utilities.py b/floris/turbine_library/turbine_utilities.py new file mode 100644 index 000000000..c862c21bd --- /dev/null +++ b/floris/turbine_library/turbine_utilities.py @@ -0,0 +1,166 @@ +import os.path + +import numpy as np +import yaml + + +def build_turbine_dict( + turbine_data_dict, + turbine_name, + file_path=None, + generator_efficiency=1.0, + hub_height=90.0, + pP=1.88, + pT=1.88, + rotor_diameter=126.0, + TSR=8.0, + air_density=1.225, + ref_tilt_cp_ct=5.0 +): + """ + Tool for formatting a full turbine dict from data formatted as a + dictionary. + + Default value for turbine physical parameters are from the NREL 5MW reference + wind turbine. + + Returns a turbine dictionary object as expected by FLORIS. Optionally, + prints the dictionary to a yaml to be included in a FLORIS wake model yaml. + + turbine_data is a dictionary that contains keys specifying the + turbine power and thrust as a function of wind speed. The following keys + are possible: + - wind_speed [m/s] + - power_absolute [kW] + - power_coefficient [-] + - thrust_absolute [kN] + - thrust_coefficient [-] + Of these, wind_speed is required. One of power_absolute and power_coefficient + must be specified; and one of thrust_absolute and thrust_coefficient must be + specified. If both _absolute and _coefficient versions are specified, the + _coefficient entry will be used and the _absolute entry ignored. + + Args: + turbine_data_dict (dict): Dictionary containing performance of the wind + turbine as a function of wind speed. Described in more detail above. + turbine_name (string): Name of the turbine, which will be used for the + turbine_type field as well as the filename. + file_path (): Path for placement of the produced yaml. Defaults to None, + in which case no yaml is written. + generator_efficiency (float): Generator efficiency [-]. Defaults to 1.0. + hub_height (float): Hub height [m]. Defaults to 90.0. + pP (float): Cosine exponent for power loss to yaw [-]. Defaults to 1.88. + pT (float): Cosine exponent for thrust loss to yaw [-]. Defaults to 1.88. + rotor_diameter (float). Rotor diameter [m]. Defaults to 126.0. + TSR (float). Turbine optimal tip-speed ratio [-]. Defaults to 8.0. + air_density (float). Air density used to specify power and thrust + curves [kg/m^3]. Defaults to 1.225. + ref_tilt_cp_ct (float). Rotor tilt (due to shaft tilt and/or platform + tilt) used when defining the power and thrust curves [deg]. Defaults + to 5.0. + + Returns: + turbine_dict (dict): Formatted turbine dictionary as expected by FLORIS. + """ + + # Check that necessary columns are specified + if "wind_speed" not in turbine_data_dict: + raise KeyError("wind_speed column must be specified.") + u = np.array(turbine_data_dict["wind_speed"]) + A = np.pi * rotor_diameter**2/4 + + # Construct the Cp curve + if "power_coefficient" in turbine_data_dict: + if "power_absolute" in turbine_data_dict: + print( + "Found both power_absolute and power_coefficient." + "Ignoring power_absolute." + ) + Cp = np.array(turbine_data_dict["power_coefficient"]) + + elif "power_absolute" in turbine_data_dict: + P = np.array(turbine_data_dict["power_absolute"]) + if _find_nearest_value_for_wind_speed(P, u, 10) > 20000 or \ + _find_nearest_value_for_wind_speed(P, u, 10) < 1000: + print( + "Unusual power value detected. Please check that power_absolute", + "is specified in kW." + ) + + validity_mask = (P != 0) | (u != 0) + Cp = np.zeros_like(P, dtype=float) + + Cp[validity_mask] = (P[validity_mask]*1000) / \ + (0.5*air_density*A*u[validity_mask]**3) + + else: + raise KeyError( + "Either power_absolute or power_coefficient must be specified." + ) + + # Construct Ct curve + if "thrust_coefficient" in turbine_data_dict: + if "thrust_absolute" in turbine_data_dict: + print( + "Found both thrust_absolute and thrust_coefficient." + "Ignoring thrust_absolute." + ) + Ct = np.array(turbine_data_dict["thrust_coefficient"]) + + elif "thrust_absolute" in turbine_data_dict: + T = np.array(turbine_data_dict["thrust_absolute"]) + if _find_nearest_value_for_wind_speed(T, u, 10) > 3000 or \ + _find_nearest_value_for_wind_speed(T, u, 10) < 100: + print( + "Unusual thrust value detected. Please check that thrust_absolute", + "is specified in kN." + ) + + validity_mask = (T != 0) | (u != 0) + Ct = np.zeros_like(T) + + Ct[validity_mask] = (T[validity_mask]*1000)/\ + (0.5*air_density*A*u[validity_mask]**2) + + else: + raise KeyError( + "Either thrust_absolute or thrust_coefficient must be specified." + ) + + # Build the turbine dict + power_thrust_dict = { + "wind_speed": u.tolist(), + "power": Cp.tolist(), + "thrust": Ct.tolist() + } + + turbine_dict = { + "turbine_type": turbine_name, + "generator_efficiency": generator_efficiency, + "hub_height": hub_height, + "pP": pP, + "pT": pT, + "rotor_diameter": rotor_diameter, + "TSR": TSR, + "ref_density_cp_ct": air_density, + "ref_tilt_cp_ct": ref_tilt_cp_ct, + "power_thrust_table": power_thrust_dict + } + + # Create yaml file + if file_path is not None: + full_name = os.path.join(file_path, turbine_name+".yaml") + yaml.dump( + turbine_dict, + open(full_name, "w"), + sort_keys=False + ) + + print(full_name, "created.") + + return turbine_dict + +def _find_nearest_value_for_wind_speed(test_vals, ws_vals, ws): + errs = np.absolute(ws_vals-ws) + idx = errs.argmin() + return test_vals[idx] diff --git a/floris/type_dec.py b/floris/type_dec.py index b67cd8681..ebbb3178a 100644 --- a/floris/type_dec.py +++ b/floris/type_dec.py @@ -15,6 +15,7 @@ from __future__ import annotations import copy +import inspect from pathlib import Path from typing import ( Any, @@ -50,6 +51,24 @@ def floris_array_converter(data: Iterable) -> np.ndarray: raise TypeError(e.args[0] + f". Data given: {data}") return a +def floris_numeric_dict_converter(data: dict) -> dict: + try: + return {k: floris_array_converter(v) for k, v in data.items()} + except TypeError as e: + raise TypeError(e.args[0] + f". Data given: {data}") + +# def array_field(**kwargs) -> Callable: +# """ +# A wrapper for the :py:func:`attr.field` function that converts the input to a Numpy array, +# adds a comparison function specific to Numpy arrays, and passes through all additional +# keyword arguments. +# """ +# return field( +# converter=floris_array_converter, +# eq=cmp_using(eq=np.array_equal), +# **kwargs +# ) + def _attr_serializer(inst: type, field: Attribute, value: Any): if isinstance(value, np.ndarray): return value.tolist() @@ -66,20 +85,16 @@ def _attr_floris_filter(inst: Attribute, value: Any) -> bool: return True def iter_validator(iter_type, item_types: Union[Any, Tuple[Any]]) -> Callable: - """Helper function to generate iterable validators that will reduce the amount of + """ + Helper function to generate iterable validators that will reduce the amount of boilerplate code. - Parameters - ---------- - iter_type : any iterable - The type of iterable object that should be validated. - item_types : Union[Any, Tuple[Any]] - The type or types of acceptable item types. - - Returns - ------- - Callable - The attr.validators.deep_iterable iterable and instance validator. + Args: + iter_type (iterable): The type of iterable object that should be validated. + item_types (Union[Any, Tuple[Any]]): The type or types of acceptable item types. + + Returns: + Callable: The attr.validators.deep_iterable iterable and instance validator. """ validator = attrs.validators.deep_iterable( member_validator=attrs.validators.instance_of(item_types), @@ -88,13 +103,19 @@ def iter_validator(iter_type, item_types: Union[Any, Tuple[Any]]) -> Callable: return validator def convert_to_path(fn: str | Path) -> Path: - """Converts an input string or pathlib.Path object to a fully resolved ``pathlib.Path`` - object. + """ + Converts an input string or ``pathlib.Path`` object to a fully resolved ``pathlib.Path`` + object. If the input is a string, it is converted to a pathlib.Path object. + The function then checks if the path exists as an absolute path, a relative path from + the script, or a relative path from the system location. If the path does not exist in + any of these locations, a FileExistsError is raised. Args: fn (str | Path): The user input file path or file name. Raises: + FileExistsError: Raised if :py:attr:`fn` is not able to be found as an absolute path, nor as + a relative path. TypeError: Raised if :py:attr:`fn` is neither a :py:obj:`str`, nor a :py:obj:`pathlib.Path`. Returns: @@ -103,11 +124,30 @@ def convert_to_path(fn: str | Path) -> Path: if isinstance(fn, str): fn = Path(fn) + # Get the base path from where the analysis script was run to determine the relative + # path from which `fn` might be based. [1] is where a direct call to this function will be + # located (e.g., testing via pytest), and [-1] is where a direct call to the function via an + # analysis script will be located (e.g., running an example). + base_fn_script = Path(inspect.stack()[-1].filename).resolve().parent + base_fn_sys = Path(inspect.stack()[1].filename).resolve().parent + if isinstance(fn, Path): - fn.resolve() - else: - raise TypeError(f"The passed input: {fn} could not be converted to a pathlib.Path object") - return fn + absolute_fn = fn.resolve() + relative_fn_script = (base_fn_script / fn).resolve() + relative_fn_sys = (base_fn_sys / fn).resolve() + if absolute_fn.exists(): + return absolute_fn + if relative_fn_script.exists(): + return relative_fn_script + if relative_fn_sys.exists(): + return relative_fn_sys + raise FileExistsError( + f"{fn} could not be found as either a\n" + f" - relative file path from a script: {relative_fn_script}\n" + f" - relative file path from a system location: {relative_fn_sys}\n" + f" - or absolute file path: {absolute_fn}" + ) + raise TypeError(f"The passed input: {fn} could not be converted to a pathlib.Path object") @define diff --git a/floris/utilities.py b/floris/utilities.py index 6e565d225..e9f457945 100644 --- a/floris/utilities.py +++ b/floris/utilities.py @@ -29,101 +29,6 @@ def pshape(array: np.ndarray, label: str = ""): print(label, np.shape(array)) -@define -class Vec3: - """ - Contains 3-component vector information. All arithmetic operators are - set so that Vec3 objects can operate on and with each other directly. - - Args: - components (list(numeric, numeric, numeric), numeric): All three vector - components. - string_format (str, optional): Format to use in the - overloaded __str__ function. Defaults to None. - """ - components: NDArrayFloat = field(converter=floris_array_converter) - # NOTE: this does not convert elements to float if they are given as int. Is this ok? - - @components.validator - def _check_components(self, attribute, value) -> None: - if np.ndim(value) > 1: - raise ValueError( - f"Vec3 must contain exactly 1 dimension, {np.ndim(value)} were given." - ) - if np.size(value) != 3: - raise ValueError( - f"Vec3 must contain exactly 3 components, {np.size(value)} were given." - ) - - def __add__(self, arg): - if type(arg) is Vec3: - return Vec3(self.components + arg.components) - elif type(arg) is int or type(arg) is float: - return Vec3(self.components + arg) - else: - raise ValueError - - def __sub__(self, arg): - if type(arg) is Vec3: - return Vec3(self.components - arg.components) - elif type(arg) is int or type(arg) is float: - return Vec3(self.components - arg) - else: - raise ValueError - - def __mul__(self, arg): - if type(arg) is Vec3: - return Vec3(self.components * arg.components) - elif type(arg) is int or type(arg) is float: - return Vec3(self.components * arg) - else: - raise ValueError - - def __truediv__(self, arg): - if type(arg) is Vec3: - return Vec3(self.components / arg.components) - elif type(arg) is int or type(arg) is float: - return Vec3(self.components / arg) - else: - raise ValueError - - def __eq__(self, arg): - return False not in np.isclose([self.x1, self.x2, self.x3], [arg.x1, arg.x2, arg.x3]) - - def __hash__(self): - return hash((self.x1, self.x2, self.x3)) - - @property - def x1(self): - return self.components[0] - - @x1.setter - def x1(self, value): - self.components[0] = float(value) - - @property - def x2(self): - return self.components[1] - - @x2.setter - def x2(self, value): - self.components[1] = float(value) - - @property - def x3(self): - return self.components[2] - - @x3.setter - def x3(self, value): - self.components[2] = float(value) - - @property - def elements(self) -> Tuple[float, float, float]: - # TODO: replace references to elements with components - # and remove this @property - return self.components - - def cosd(angle): """ Cosine of an angle with the angle given in degrees. @@ -303,9 +208,9 @@ def reverse_rotate_coordinates_rel_west( grid_y_reversed = np.zeros_like(grid_x) grid_z_reversed = np.zeros_like(grid_x) for wii, angle_rotation in enumerate(wind_deviation_from_west): - x_rot = grid_x[wii, :, :, :, :] - y_rot = grid_y[wii, :, :, :, :] - z_rot = grid_z[wii, :, :, :, :] + x_rot = grid_x[wii] + y_rot = grid_y[wii] + z_rot = grid_z[wii] # Rotate turbine coordinates about the center x_rot_offset = x_rot - x_center_of_rotation @@ -322,9 +227,9 @@ def reverse_rotate_coordinates_rel_west( ) z = z_rot # Nothing changed in this rotation - grid_x_reversed[wii, :, :, :, :] = x - grid_y_reversed[wii, :, :, :, :] = y - grid_z_reversed[wii, :, :, :, :] = z + grid_x_reversed[wii] = x + grid_y_reversed[wii] = y + grid_z_reversed[wii] = z return grid_x_reversed, grid_y_reversed, grid_z_reversed diff --git a/floris/version.py b/floris/version.py index 5a958026d..d70c8f8d8 100644 --- a/floris/version.py +++ b/floris/version.py @@ -1 +1 @@ -3.5 +3.6 diff --git a/setup.py b/setup.py index 0bab76eb1..6e08029ae 100644 --- a/setup.py +++ b/setup.py @@ -30,19 +30,19 @@ REQUIRED = [ # simulation "attrs", - "pyyaml", - "numexpr", - "numpy>=1.20", - "scipy>=1.1", + "pyyaml~=6.0", + "numexpr~=2.0", + "numpy~=1.20", + "scipy~=1.1", # tools - "matplotlib>=3", - "pandas", - "shapely", + "matplotlib~=3.0", + "pandas~=2.0", + "shapely~=2.0", # utilities - "coloredlogs>=10.0", - "flatten_dict", + "coloredlogs~=10.0", + "flatten_dict~=0.0", ] # What packages are optional? @@ -52,7 +52,7 @@ # pip install "floris[develop]" installs developer packages in non-editable install EXTRAS = { "docs": { - "jupyter-book<=0.13.3", + "jupyter-book", "sphinx-book-theme", "sphinx-autodoc-typehints", "sphinxcontrib-autoyaml", diff --git a/tests/base_test.py b/tests/base_test.py index fcaa6ae1c..3be2e8710 100644 --- a/tests/base_test.py +++ b/tests/base_test.py @@ -15,6 +15,7 @@ import pytest from attr import define, field +from attrs.exceptions import FrozenAttributeError from floris.simulation import BaseClass, BaseModel @@ -31,16 +32,22 @@ def function() -> None: return None -def test_get_model_defaults(): - defaults = ClassTest.get_model_defaults() - assert len(defaults) == 2 - assert defaults["x"] == 1 - assert defaults["a_string"] == "abc" - - def test_get_model_values(): + """ + BaseClass and therefore BaseModel previously had a method `get_model_values` that + returned the values of the model parameters. This was removed because it was redundant + but this test was refactored to test the as_dict method from FromDictMixin. + This tests that the parameters are changed when set by the user. + """ cls = ClassTest(x=4, a_string="xyz") - values = cls._get_model_dict() + values = cls.as_dict() assert len(values) == 2 assert values["x"] == 4 assert values["a_string"] == "xyz" + +def test_NUM_EPS(): + cls = ClassTest(x=4, a_string="xyz") + assert cls.NUM_EPS == 0.001 + + with pytest.raises(FrozenAttributeError): + cls.NUM_EPS = 2 diff --git a/tests/conftest.py b/tests/conftest.py index ab04fbde3..edbb2b863 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -25,7 +25,6 @@ PointsGrid, TurbineGrid, ) -from floris.utilities import Vec3 def turbines_to_array(turbine_list: list): @@ -108,10 +107,6 @@ def print_test_values( ## Unit test fixtures -@pytest.fixture -def vec3_fixture(): - return Vec3([4, 4, 0]) - @pytest.fixture def flow_field_fixture(sample_inputs_fixture): flow_field_dict = sample_inputs_fixture.flow_field @@ -119,16 +114,11 @@ def flow_field_fixture(sample_inputs_fixture): @pytest.fixture def turbine_grid_fixture(sample_inputs_fixture) -> TurbineGrid: - turbine_coordinates = [Vec3(c) for c in list(zip(X_COORDS, Y_COORDS, Z_COORDS))] - - # TODO: The TurbineGrid requires that the rotor diameters be 1d but the - # Farm constructs them as 3d - # Can we make this consistent? - + turbine_coordinates = np.array(list(zip(X_COORDS, Y_COORDS, Z_COORDS))) rotor_diameters = ROTOR_DIAMETER * np.ones( (N_TURBINES) ) return TurbineGrid( turbine_coordinates=turbine_coordinates, - reference_turbine_diameter=rotor_diameters, + turbine_diameters=rotor_diameters, wind_directions=np.array(WIND_DIRECTIONS), wind_speeds=np.array(WIND_SPEEDS), grid_resolution=TURBINE_GRID_RESOLUTION, @@ -137,11 +127,11 @@ def turbine_grid_fixture(sample_inputs_fixture) -> TurbineGrid: @pytest.fixture def flow_field_grid_fixture(sample_inputs_fixture) -> FlowFieldGrid: - turbine_coordinates = [Vec3(c) for c in list(zip(X_COORDS, Y_COORDS, Z_COORDS))] + turbine_coordinates = np.array(list(zip(X_COORDS, Y_COORDS, Z_COORDS))) rotor_diameters = ROTOR_DIAMETER * np.ones( (N_WIND_DIRECTIONS, N_WIND_SPEEDS, N_TURBINES) ) return FlowFieldGrid( turbine_coordinates=turbine_coordinates, - reference_turbine_diameter=rotor_diameters, + turbine_diameters=rotor_diameters, wind_directions=np.array(WIND_DIRECTIONS), wind_speeds=np.array(WIND_SPEEDS), grid_resolution=[3,2,2] @@ -149,14 +139,14 @@ def flow_field_grid_fixture(sample_inputs_fixture) -> FlowFieldGrid: @pytest.fixture def points_grid_fixture(sample_inputs_fixture) -> PointsGrid: - turbine_coordinates = [Vec3(c) for c in list(zip(X_COORDS, Y_COORDS, Z_COORDS))] + turbine_coordinates = np.array(list(zip(X_COORDS, Y_COORDS, Z_COORDS))) rotor_diameters = ROTOR_DIAMETER * np.ones( (N_WIND_DIRECTIONS, N_WIND_SPEEDS, N_TURBINES) ) points_x = [0.0, 10.0] points_y = [0.0, 0.0] points_z = [1.0, 2.0] return PointsGrid( turbine_coordinates=turbine_coordinates, - reference_turbine_diameter=rotor_diameters, + turbine_diameters=rotor_diameters, wind_directions=np.array(WIND_DIRECTIONS), wind_speeds=np.array(WIND_SPEEDS), grid_resolution=None, @@ -353,13 +343,13 @@ def __init__(self): 5.0, 5.0, ], - "wind_speeds": [ + "wind_speed": [ 0.0, 25.0, 50.0, ], } - self.turbine_floating["floating_correct_cp_ct_for_tilt"] = True + self.turbine_floating["correct_cp_ct_for_tilt"] = True self.turbine_multi_dim = copy.deepcopy(self.turbine) del self.turbine_multi_dim['power_thrust_table'] diff --git a/tests/farm_unit_test.py b/tests/farm_unit_test.py index 53c340a20..64d1d405e 100644 --- a/tests/farm_unit_test.py +++ b/tests/farm_unit_test.py @@ -19,7 +19,7 @@ import pytest from floris.simulation import Farm -from floris.utilities import load_yaml, Vec3 +from floris.utilities import load_yaml from tests.conftest import ( N_TURBINES, N_WIND_DIRECTIONS, @@ -35,7 +35,7 @@ def test_farm_init_homogenous_turbines(): layout_x = farm_data["layout_x"] layout_y = farm_data["layout_y"] coordinates = np.array([ - Vec3([x, y, turbine_data["hub_height"]]) + np.array([x, y, turbine_data["hub_height"]]) for x, y in zip(layout_x, layout_y) ]) @@ -49,7 +49,6 @@ def test_farm_init_homogenous_turbines(): # turbine_type=[turbine_data["turbine_type"]] farm.construct_hub_heights() - farm.construct_coordinates() farm.set_yaw_angles(N_WIND_DIRECTIONS, N_WIND_SPEEDS) # Check initial values @@ -61,7 +60,6 @@ def test_farm_init_homogenous_turbines(): def test_asdict(sample_inputs_fixture: SampleInputs): farm = Farm.from_dict(sample_inputs_fixture.farm) farm.construct_hub_heights() - farm.construct_coordinates() farm.construct_turbine_ref_tilt_cp_cts() farm.set_yaw_angles(N_WIND_DIRECTIONS, N_WIND_SPEEDS) farm.set_tilt_to_ref_tilt(N_WIND_DIRECTIONS, N_WIND_SPEEDS) @@ -69,7 +67,6 @@ def test_asdict(sample_inputs_fixture: SampleInputs): new_farm = farm.from_dict(dict1) new_farm.construct_hub_heights() - new_farm.construct_coordinates() new_farm.construct_turbine_ref_tilt_cp_cts() new_farm.set_yaw_angles(N_WIND_DIRECTIONS, N_WIND_SPEEDS) new_farm.set_tilt_to_ref_tilt(N_WIND_DIRECTIONS, N_WIND_SPEEDS) diff --git a/tests/grid_unit_test.py b/tests/grid_unit_test.py deleted file mode 100644 index b9938233b..000000000 --- a/tests/grid_unit_test.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright 2021 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. - -# See https://floris.readthedocs.io for documentation - - -import numpy as np -import pytest - -from floris.utilities import Vec3 -from tests.conftest import ( - N_TURBINES, - N_WIND_DIRECTIONS, - N_WIND_SPEEDS, - TURBINE_GRID_RESOLUTION, -) - - -# TODO: test the dimension expansion - - -def test_turbinegrid_set_grid(turbine_grid_fixture): - expected_x_grid = [ - [[0.0, 0.0], [0.0, 0.0]], - [[630.0, 630.0], [630.0, 630.0]], - [[1260.0, 1260.0], [1260.0, 1260.0]] - ] - expected_y_grid = [ - [[-31.5, -31.5], [31.5, 31.5]], - [[-31.5, -31.5], [31.5, 31.5]], - [[-31.5, -31.5], [31.5, 31.5]] - ] - expected_z_grid = [ - [[58.5, 121.5], [58.5, 121.5]], - [[58.5, 121.5], [58.5, 121.5]], - [[58.5, 121.5], [58.5, 121.5]] - ] - - # subtract the test and expected values which should result in 0's - # then, search for any elements that are true and negate the results - # if an element is zero, the not will return true - # if an element is non-zero, the not will return false - assert not np.any(turbine_grid_fixture.x_sorted[0, 0] - expected_x_grid) - assert not np.any(turbine_grid_fixture.y_sorted[0, 0] - expected_y_grid) - assert not np.any(turbine_grid_fixture.z_sorted[0, 0] - expected_z_grid) - - -def test_turbinegrid_dimensions(turbine_grid_fixture): - assert np.shape(turbine_grid_fixture.x_sorted) == ( - N_WIND_DIRECTIONS, - N_WIND_SPEEDS, - N_TURBINES, - TURBINE_GRID_RESOLUTION, - TURBINE_GRID_RESOLUTION - ) - assert np.shape(turbine_grid_fixture.y_sorted) == ( - N_WIND_DIRECTIONS, - N_WIND_SPEEDS, - N_TURBINES, - TURBINE_GRID_RESOLUTION, - TURBINE_GRID_RESOLUTION - ) - assert np.shape(turbine_grid_fixture.z_sorted) == ( - N_WIND_DIRECTIONS, - N_WIND_SPEEDS, - N_TURBINES, - TURBINE_GRID_RESOLUTION, - TURBINE_GRID_RESOLUTION - ) - - -def test_turbinegrid_dynamic_properties(turbine_grid_fixture): - assert turbine_grid_fixture.n_turbines == N_TURBINES - assert turbine_grid_fixture.n_wind_speeds == N_WIND_SPEEDS - assert turbine_grid_fixture.n_wind_directions == N_WIND_DIRECTIONS - - # TODO: @Rob @Chris This breaks n_turbines since the validator is not run. - # Is this case ok? Do we enforce that turbine_coordinates must be set by =? - # turbine_grid_fixture.turbine_coordinates.append(Vec3([100.0, 200.0, 300.0])) - # assert turbine_grid_fixture.n_turbines == N_TURBINES + 1 - - turbine_grid_fixture.turbine_coordinates = [ - *turbine_grid_fixture.turbine_coordinates, Vec3([100.0, 200.0, 300.0]) - ] - assert turbine_grid_fixture.n_turbines == N_TURBINES + 1 - - turbine_grid_fixture.wind_speeds = [*turbine_grid_fixture.wind_speeds, 0.0] - assert turbine_grid_fixture.n_wind_speeds == N_WIND_SPEEDS + 1 - - turbine_grid_fixture.wind_directions = [*turbine_grid_fixture.wind_directions, 0.0] - assert turbine_grid_fixture.n_wind_directions == N_WIND_DIRECTIONS + 1 - - - -# def test_flow_field_set_bounds(flow_field_grid_fixture): -# assert flow_field_grid_fixture.xmin == -252.0 -# assert flow_field_grid_fixture.xmax == 2520.0 -# assert flow_field_grid_fixture.ymin == -252.0 -# assert flow_field_grid_fixture.ymax == 252.0 -# assert flow_field_grid_fixture.zmin == 0.1 -# assert flow_field_grid_fixture.zmax == 540 - - -# def test_flow_field_set_grid(flow_field_grid_fixture): -# assert ( -# [ -# flow_field_grid_fixture.x[0][0][0], -# flow_field_grid_fixture.y[0][0][0], -# flow_field_grid_fixture.z[0][0][0] -# ] -# == [ -252.0, -252.0, 0.1] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[1][0][0], -# flow_field_grid_fixture.y[0][0][0], -# flow_field_grid_fixture.z[0][0][0] -# ] -# == [ 2520.0, -252.0, 0.1] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[0][0][0], -# flow_field_grid_fixture.y[0][1][0], -# flow_field_grid_fixture.z[0][0][0] -# ] -# == [ -252.0, 252.0, 0.1] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[1][0][0], -# flow_field_grid_fixture.y[0][1][0], -# flow_field_grid_fixture.z[0][0][0] -# ] -# == [ 2520.0, 252.0, 0.1] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[0][0][0], -# flow_field_grid_fixture.y[0][0][0], -# flow_field_grid_fixture.z[0][0][1] -# ] -# == [ -252.0, -252.0, 540.0] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[1][0][0], -# flow_field_grid_fixture.y[0][0][0], -# flow_field_grid_fixture.z[0][0][1] -# ] -# == [ 2520.0, -252.0, 540.0] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[0][0][0], -# flow_field_grid_fixture.y[0][1][0], -# flow_field_grid_fixture.z[0][0][1] -# ] -# == [ -252.0, 252.0, 540.0] -# ) -# assert ( -# [ -# flow_field_grid_fixture.x[1][0][0], -# flow_field_grid_fixture.y[0][1][0], -# flow_field_grid_fixture.z[0][0][1] -# ] -# == [ 2520.0, 252.0, 540.0] -# ) diff --git a/tests/reg_tests/cumulative_curl_regression_test.py b/tests/reg_tests/cumulative_curl_regression_test.py index d0fea7a01..7cbffc561 100644 --- a/tests/reg_tests/cumulative_curl_regression_test.py +++ b/tests/reg_tests/cumulative_curl_regression_test.py @@ -193,7 +193,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -203,7 +203,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -219,7 +219,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -361,7 +361,7 @@ def test_regression_yaw(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -371,7 +371,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -387,7 +387,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -457,7 +457,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -467,7 +467,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -483,7 +483,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -552,7 +552,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -562,7 +562,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -578,7 +578,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -648,7 +648,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/empirical_gauss_regression_test.py b/tests/reg_tests/empirical_gauss_regression_test.py index 4dc28ef2e..2a7c49127 100644 --- a/tests/reg_tests/empirical_gauss_regression_test.py +++ b/tests/reg_tests/empirical_gauss_regression_test.py @@ -137,7 +137,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -147,7 +147,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -163,7 +163,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -307,7 +307,7 @@ def test_regression_yaw(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -317,7 +317,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -333,7 +333,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -404,7 +404,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/floris_interface_regression_test.py b/tests/reg_tests/floris_interface_regression_test.py index 3e8286c3e..ccf62350e 100644 --- a/tests/reg_tests/floris_interface_regression_test.py +++ b/tests/reg_tests/floris_interface_regression_test.py @@ -106,7 +106,7 @@ def test_calculate_no_wake(sample_inputs_fixture): ref_tilt_cp_cts, fi.floris.farm.pPs, fi.floris.farm.pTs, - fi.floris.farm.turbine_fTilts, + fi.floris.farm.turbine_tilt_interps, fi.floris.farm.correct_cp_ct_for_tilt, fi.floris.farm.turbine_type_map, ) @@ -116,7 +116,7 @@ def test_calculate_no_wake(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, fi.floris.farm.turbine_fCts, - fi.floris.farm.turbine_fTilts, + fi.floris.farm.turbine_tilt_interps, fi.floris.farm.correct_cp_ct_for_tilt, fi.floris.farm.turbine_type_map, ) @@ -132,7 +132,7 @@ def test_calculate_no_wake(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, fi.floris.farm.turbine_fCts, - fi.floris.farm.turbine_fTilts, + fi.floris.farm.turbine_tilt_interps, fi.floris.farm.correct_cp_ct_for_tilt, fi.floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/gauss_regression_test.py b/tests/reg_tests/gauss_regression_test.py index 20e71dc71..a6a3dd5e7 100644 --- a/tests/reg_tests/gauss_regression_test.py +++ b/tests/reg_tests/gauss_regression_test.py @@ -285,7 +285,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -295,7 +295,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -311,7 +311,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -453,7 +453,7 @@ def test_regression_yaw(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -463,7 +463,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -479,7 +479,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -546,7 +546,7 @@ def test_regression_gch(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -556,7 +556,7 @@ def test_regression_gch(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -572,7 +572,7 @@ def test_regression_gch(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -635,7 +635,7 @@ def test_regression_gch(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -645,7 +645,7 @@ def test_regression_gch(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -661,7 +661,7 @@ def test_regression_gch(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -731,7 +731,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -741,7 +741,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -757,7 +757,7 @@ def test_regression_yaw_added_recovery(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -826,7 +826,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -836,7 +836,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -852,7 +852,7 @@ def test_regression_secondary_steering(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -922,7 +922,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/jensen_jimenez_regression_test.py b/tests/reg_tests/jensen_jimenez_regression_test.py index 3e720edab..7d0f633ce 100644 --- a/tests/reg_tests/jensen_jimenez_regression_test.py +++ b/tests/reg_tests/jensen_jimenez_regression_test.py @@ -135,7 +135,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -145,7 +145,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -161,7 +161,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -303,7 +303,7 @@ def test_regression_yaw(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -313,7 +313,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -329,7 +329,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -399,7 +399,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/none_regression_test.py b/tests/reg_tests/none_regression_test.py index 3a1b37d5e..787685c0e 100644 --- a/tests/reg_tests/none_regression_test.py +++ b/tests/reg_tests/none_regression_test.py @@ -136,7 +136,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -146,7 +146,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -162,7 +162,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -328,7 +328,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/reg_tests/turbopark_regression_test.py b/tests/reg_tests/turbopark_regression_test.py index d7726f519..acecaa6bc 100644 --- a/tests/reg_tests/turbopark_regression_test.py +++ b/tests/reg_tests/turbopark_regression_test.py @@ -137,7 +137,7 @@ def test_regression_tandem(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -147,7 +147,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -163,7 +163,7 @@ def test_regression_tandem(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -306,7 +306,7 @@ def test_regression_yaw(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -316,7 +316,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -332,7 +332,7 @@ def test_regression_yaw(sample_inputs_fixture): tilt_angles, ref_tilt_cp_cts, floris.farm.turbine_fCts, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) @@ -403,7 +403,7 @@ def test_regression_small_grid_rotation(sample_inputs_fixture): ref_tilt_cp_cts, floris.farm.pPs, floris.farm.pTs, - floris.farm.turbine_fTilts, + floris.farm.turbine_tilt_interps, floris.farm.correct_cp_ct_for_tilt, floris.farm.turbine_type_map, ) diff --git a/tests/turbine_grid_unit_test.py b/tests/turbine_grid_unit_test.py new file mode 100644 index 000000000..08c7371bd --- /dev/null +++ b/tests/turbine_grid_unit_test.py @@ -0,0 +1,99 @@ +# Copyright 2021 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. + +# See https://floris.readthedocs.io for documentation + + +import numpy as np + +from floris.simulation import TurbineGrid +from tests.conftest import ( + N_TURBINES, + N_WIND_DIRECTIONS, + N_WIND_SPEEDS, + TURBINE_GRID_RESOLUTION, +) + + +# def test_from_dict_as_dict(turbine_grid_fixture): +# grid_dict = turbine_grid_fixture.as_dict() +# new_grid = TurbineGrid.from_dict(grid_dict) +# assert new_grid == turbine_grid_fixture + + +def test_set_grid(turbine_grid_fixture): + expected_x_grid = [ + [[0.0, 0.0], [0.0, 0.0]], + [[630.0, 630.0], [630.0, 630.0]], + [[1260.0, 1260.0], [1260.0, 1260.0]] + ] + expected_y_grid = [ + [[-31.5, -31.5], [31.5, 31.5]], + [[-31.5, -31.5], [31.5, 31.5]], + [[-31.5, -31.5], [31.5, 31.5]] + ] + expected_z_grid = [ + [[58.5, 121.5], [58.5, 121.5]], + [[58.5, 121.5], [58.5, 121.5]], + [[58.5, 121.5], [58.5, 121.5]] + ] + + # subtract the test and expected values which should result in 0's + # then, search for any elements that are true and negate the results + # if an element is zero, the not will return true + # if an element is non-zero, the not will return false + np.testing.assert_array_equal(turbine_grid_fixture.x_sorted[0, 0], expected_x_grid) + np.testing.assert_array_equal(turbine_grid_fixture.y_sorted[0, 0], expected_y_grid) + np.testing.assert_array_equal(turbine_grid_fixture.z_sorted[0, 0], expected_z_grid) + + +def test_dimensions(turbine_grid_fixture): + assert np.shape(turbine_grid_fixture.x_sorted) == ( + N_WIND_DIRECTIONS, + N_WIND_SPEEDS, + N_TURBINES, + TURBINE_GRID_RESOLUTION, + TURBINE_GRID_RESOLUTION + ) + assert np.shape(turbine_grid_fixture.y_sorted) == ( + N_WIND_DIRECTIONS, + N_WIND_SPEEDS, + N_TURBINES, + TURBINE_GRID_RESOLUTION, + TURBINE_GRID_RESOLUTION + ) + assert np.shape(turbine_grid_fixture.z_sorted) == ( + N_WIND_DIRECTIONS, + N_WIND_SPEEDS, + N_TURBINES, + TURBINE_GRID_RESOLUTION, + TURBINE_GRID_RESOLUTION + ) + + +def test_dynamic_properties(turbine_grid_fixture): + assert turbine_grid_fixture.n_turbines == N_TURBINES + assert turbine_grid_fixture.n_wind_speeds == N_WIND_SPEEDS + assert turbine_grid_fixture.n_wind_directions == N_WIND_DIRECTIONS + + turbine_grid_fixture.turbine_coordinates = np.append( + turbine_grid_fixture.turbine_coordinates, + np.array([[100.0, 200.0, 300.0]]), + axis=0 + ) + assert turbine_grid_fixture.n_turbines == N_TURBINES + 1 + + turbine_grid_fixture.wind_speeds = [*turbine_grid_fixture.wind_speeds, 0.0] + assert turbine_grid_fixture.n_wind_speeds == N_WIND_SPEEDS + 1 + + turbine_grid_fixture.wind_directions = [*turbine_grid_fixture.wind_directions, 0.0] + assert turbine_grid_fixture.n_wind_directions == N_WIND_DIRECTIONS + 1 diff --git a/tests/turbine_multi_dim_unit_test.py b/tests/turbine_multi_dim_unit_test.py index fd6cdacce..05c91ebc3 100644 --- a/tests/turbine_multi_dim_unit_test.py +++ b/tests/turbine_multi_dim_unit_test.py @@ -18,7 +18,6 @@ import numpy as np import pandas as pd import pytest -from scipy.interpolate import interp1d from floris.simulation import ( Turbine, @@ -111,7 +110,6 @@ def test_turbine_init(): assert turbine.generator_efficiency == turbine_data["generator_efficiency"] assert isinstance(turbine.power_thrust_data, dict) - assert isinstance(turbine.fCp_interp, interp1d) assert isinstance(turbine.fCt_interp, dict) assert isinstance(turbine.power_interp, dict) assert turbine.rotor_radius == turbine_data["rotor_diameter"] / 2.0 @@ -135,12 +133,11 @@ def test_ct(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt=np.array([[[turbine.fCt_interp[(2, 1)]]]]), - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False]]]), turbine_type_map=turbine_type_map[:,:,0] ) - print(thrust) np.testing.assert_allclose(thrust, np.array([[[0.77853469]]])) # Multiple turbines with index filter @@ -158,15 +155,13 @@ def test_ct(): N_TURBINES, ) ), - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False] * N_TURBINES]]), turbine_type_map=turbine_type_map, ix_filter=INDEX_FILTER, ) assert len(thrusts[0, 0]) == len(INDEX_FILTER) - print(thrusts) - thrusts_truth = [ [ [0.77853469, 0.77853469], @@ -275,7 +270,7 @@ def test_axial_induction(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt=np.array([[[turbine.fCt_interp[(2, 1)]]]]), - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False]]]), turbine_type_map=turbine_type_map[0,0,0], ) @@ -295,7 +290,7 @@ def test_axial_induction(): N_TURBINES, ) ), - tilt_interp=np.array([(turbine.turbine_type, None)] * N_TURBINES), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False] * N_TURBINES]]), turbine_type_map=turbine_type_map, ix_filter=INDEX_FILTER, diff --git a/tests/turbine_unit_test.py b/tests/turbine_unit_test.py index c832bd594..a3f03e674 100644 --- a/tests/turbine_unit_test.py +++ b/tests/turbine_unit_test.py @@ -13,9 +13,13 @@ # See https://floris.readthedocs.io for documentation +import os +from pathlib import Path + import attr import numpy as np import pytest +import yaml from scipy.interpolate import interp1d from floris.simulation import ( @@ -26,12 +30,11 @@ Turbine, ) from floris.simulation.turbine import ( - _filter_convert, _rotor_velocity_tilt_correction, _rotor_velocity_yaw_correction, compute_tilt_angles_for_floating_turbines, - PowerThrustTable, ) +from floris.turbine_library import build_turbine_dict from tests.conftest import SampleInputs, WIND_SPEEDS @@ -47,56 +50,41 @@ INDEX_FILTER = [0, 2] -def test_power_thrust_table(): - turbine_data = SampleInputs().turbine - table = PowerThrustTable.from_dict(turbine_data["power_thrust_table"]) - - # Test data conversion is correct - assert isinstance(table.power, np.ndarray) - assert isinstance(table.thrust, np.ndarray) - assert isinstance(table.wind_speed, np.ndarray) - - # Test for initialization errors - for el in ("power", "thrust", "wind_speed"): - pt_table = SampleInputs().turbine["power_thrust_table"] - pt_table[el] = pt_table[el][:-1] - with pytest.raises(ValueError): - PowerThrustTable.from_dict(pt_table) - - pt_table = SampleInputs().turbine["power_thrust_table"] - pt_table[el] = np.array(pt_table[el]).reshape(2, -1) - with pytest.raises(ValueError): - PowerThrustTable.from_dict(pt_table) - - def test_turbine_init(): turbine_data = SampleInputs().turbine turbine = Turbine.from_dict(turbine_data) + assert turbine.turbine_type == turbine_data["turbine_type"] assert turbine.rotor_diameter == turbine_data["rotor_diameter"] assert turbine.hub_height == turbine_data["hub_height"] assert turbine.pP == turbine_data["pP"] assert turbine.pT == turbine_data["pT"] + assert turbine.TSR == turbine_data["TSR"] assert turbine.generator_efficiency == turbine_data["generator_efficiency"] - - pt_data = turbine_data["power_thrust_table"] - assert isinstance(turbine.power_thrust_table, PowerThrustTable) - np.testing.assert_allclose( - turbine.power_thrust_table.power, - np.array(pt_data["power"]) + assert turbine.ref_density_cp_ct == turbine_data["ref_density_cp_ct"] + assert turbine.ref_tilt_cp_ct == turbine_data["ref_tilt_cp_ct"] + assert np.array_equal( + turbine.power_thrust_table["wind_speed"], + turbine_data["power_thrust_table"]["wind_speed"] ) - np.testing.assert_allclose( - turbine.power_thrust_table.thrust, - np.array(pt_data["thrust"]) + assert np.array_equal( + turbine.power_thrust_table["power"], + turbine_data["power_thrust_table"]["power"] ) - np.testing.assert_allclose( - turbine.power_thrust_table.wind_speed, - np.array(pt_data["wind_speed"]) + assert np.array_equal( + turbine.power_thrust_table["thrust"], + turbine_data["power_thrust_table"]["thrust"] ) + assert turbine.rotor_radius == turbine.rotor_diameter / 2.0 + assert turbine.rotor_area == np.pi * turbine.rotor_radius ** 2.0 + + # TODO: test these explicitly. + # Test create a simpler interpolator and test that you get the values you expect + # fCt_interp: interp1d = field(init=False) + # power_interp: interp1d = field(init=False) + # tilt_interp: interp1d = field(init=False, default=None) - assert isinstance(turbine.fCp_interp, interp1d) assert isinstance(turbine.fCt_interp, interp1d) assert isinstance(turbine.power_interp, interp1d) - assert turbine.rotor_radius == turbine_data["rotor_diameter"] / 2.0 def test_rotor_radius(): @@ -132,50 +120,6 @@ def test_rotor_area(): assert turbine.rotor_area == np.pi -def test_filter_convert(): - N = 4 - - # When the index filter is not None or a Numpy array, - # the function should return None - ix_filter = 1 - sample_arg = np.arange(N) - with pytest.raises(TypeError): - _filter_convert(ix_filter, sample_arg) - - # When the sample_arg is not a Numpy array, the function - # should return None - ix_filter = None - sample_arg = [1, 2, 3] - with pytest.raises(TypeError): - _filter_convert(ix_filter, sample_arg) - - # When the sample_arg is a Numpy array and the index filter - # is None, a boolean array containing all True should be - # returned with the same shape as the sample_arg. - ix_filter = None - sample_arg = np.arange(N) - ix_filter = _filter_convert(ix_filter, sample_arg) - assert ix_filter.sum() == N - assert ix_filter.shape == (N,) - - # When the index filter is given as a Python list, the - # function should return the values cast to a Numpy array - ix_filter = [1, 2] - sample_arg = np.arange(N).reshape(1, 1, N) - ix_filter = _filter_convert(ix_filter, sample_arg) - np.testing.assert_array_equal(ix_filter, np.array([1, 2])) - - # Test that a 1-D boolean truth array is returned - # When the index filter is None and the sample_arg - # is a Numpy array of values, the returned filter indices - # should be all True and have the shape of the turbine-dimension - ix_filter = None - sample_arg = np.arange(N).reshape(1, 1, N) - ix_filter = _filter_convert(ix_filter, sample_arg) - assert ix_filter.sum() == N - assert ix_filter.shape == (N,) - - def test_average_velocity(): # TODO: why do we use cube root - mean - cube (like rms) instead of a simple average (np.mean)? # Dimensions are (n wind directions, n wind speeds, n turbines, grid x, grid y) @@ -254,7 +198,7 @@ def test_ct(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt={turbine.turbine_type: turbine.fCt_interp}, - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False]]]), turbine_type_map=turbine_type_map[:,:,0] ) @@ -270,7 +214,7 @@ def test_ct(): tilt_angle=np.ones((1, 1, N_TURBINES)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, N_TURBINES)) * 5.0, fCt={turbine.turbine_type: turbine.fCt_interp}, - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False] * N_TURBINES]]), turbine_type_map=turbine_type_map, ix_filter=INDEX_FILTER, @@ -291,7 +235,7 @@ def test_ct(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt={turbine.turbine_type: turbine_floating.fCt_interp}, - tilt_interp=np.array([(turbine_floating.turbine_type, turbine_floating.fTilt_interp)]), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[True]]]), turbine_type_map=turbine_type_map[:,:,0] ) @@ -304,62 +248,90 @@ def test_ct(): def test_power(): - N_TURBINES = 4 AIR_DENSITY = 1.225 + # Test that power is computed as expected for a single turbine + n_turbines = 1 + wind_speed = 10.0 turbine_data = SampleInputs().turbine turbine = Turbine.from_dict(turbine_data) - turbine_type_map = np.array(N_TURBINES * [turbine.turbine_type]) + turbine_type_map = np.array(n_turbines * [turbine.turbine_type]) turbine_type_map = turbine_type_map[None, None, :] - - # Single turbine - wind_speed = 10.0 - p = power( + test_power = power( ref_density_cp_ct=AIR_DENSITY, - rotor_effective_velocities=wind_speed * np.ones((1, 1, 1, 3, 3)), - power_interp={turbine.turbine_type: turbine.fCp_interp}, + rotor_effective_velocities=wind_speed * np.ones((1, 1, 1)), + power_interp={turbine.turbine_type: turbine.power_interp}, turbine_type_map=turbine_type_map[:,:,0] ) - # calculate power again + # Recompute using the provided Cp table truth_index = turbine_data["power_thrust_table"]["wind_speed"].index(wind_speed) cp_truth = turbine_data["power_thrust_table"]["power"][truth_index] - power_truth = ( + baseline_power = ( 0.5 - * turbine.rotor_area * cp_truth - * turbine.generator_efficiency + * AIR_DENSITY + * turbine.rotor_area * wind_speed ** 3 + * turbine.generator_efficiency + ) + assert np.allclose(baseline_power, test_power) + + + # At rated, the power calculated should be 5MW since the test data is the NREL 5MW turbine + wind_speed = 18.0 + rated_power = power( + ref_density_cp_ct=AIR_DENSITY, + rotor_effective_velocities=wind_speed * np.ones((1, 1, 1)), + power_interp={turbine.turbine_type: turbine.power_interp}, + turbine_type_map=turbine_type_map[:,:,0] + ) + assert np.allclose(rated_power, 5e6) + + + # At wind speed = 0.0, the power should be 0 based on the provided Cp curve + wind_speed = 0.0 + zero_power = power( + ref_density_cp_ct=AIR_DENSITY, + rotor_effective_velocities=wind_speed * np.ones((1, 1, 1)), + power_interp={turbine.turbine_type: turbine.power_interp}, + turbine_type_map=turbine_type_map[:,:,0] + ) + assert np.allclose(zero_power, 0.0) + + + # Test 4-turbine velocities array + n_turbines = 4 + wind_speed = 10.0 + turbine_data = SampleInputs().turbine + turbine = Turbine.from_dict(turbine_data) + turbine_type_map = np.array(n_turbines * [turbine.turbine_type]) + turbine_type_map = turbine_type_map[None, None, :] + test_4_power = power( + ref_density_cp_ct=AIR_DENSITY, + rotor_effective_velocities=wind_speed * np.ones((1, 1, n_turbines)), + power_interp={turbine.turbine_type: turbine.power_interp}, + turbine_type_map=turbine_type_map ) - np.testing.assert_allclose(p,cp_truth,power_truth ) - - # # Multiple turbines with ix filter - # p = power( - # air_density=AIR_DENSITY, - # velocities=np.ones((N_TURBINES, 3, 3)) * WIND_CONDITION_BROADCAST, # 3 x 4 x 4 x 3 x 3 - # yaw_angle=np.zeros((1, 1, N_TURBINES)), - # pP=turbine.pP * np.ones((3, 4, N_TURBINES)), - # power_interp={turbine.turbine_type: turbine.fCp_interp}, - # turbine_type_map=turbine_type_map, - # ix_filter=INDEX_FILTER, - # ) - # assert len(p[0, 0]) == len(INDEX_FILTER) - - # for i in range(len(INDEX_FILTER)): - # effective_velocity_trurth = ((AIR_DENSITY/1.225)**(1/3)) * WIND_SPEEDS[0] - # truth_index = turbine_data["power_thrust_table"]["wind_speed"].index( - # effective_velocity_trurth - # ) - # cp_truth = turbine_data["power_thrust_table"]["power"][truth_index] - # power_truth = ( - # 0.5 - # * turbine.rotor_area - # * cp_truth - # * turbine.generator_efficiency - # * effective_velocity_trurth ** 3 - # ) - # print(i,WIND_SPEEDS, effective_velocity_trurth, cp_truth, p[0, 0, i], power_truth) - # np.testing.assert_allclose(p[0, 0, i], power_truth) + baseline_4_power = baseline_power * np.ones((1, 1, n_turbines)) + assert np.allclose(baseline_4_power, test_4_power) + assert np.shape(baseline_4_power) == np.shape(test_4_power) + + + # Same as above but with the grid expanded in the velocities array + turbine_data = SampleInputs().turbine + turbine = Turbine.from_dict(turbine_data) + turbine_type_map = np.array(n_turbines * [turbine.turbine_type]) + turbine_type_map = turbine_type_map[None, None, :] + test_grid_power = power( + ref_density_cp_ct=AIR_DENSITY, + rotor_effective_velocities=wind_speed * np.ones((1, 1, n_turbines, 3, 3)), + power_interp={turbine.turbine_type: turbine.power_interp}, + turbine_type_map=turbine_type_map[:,:,0] + ) + baseline_grid_power = baseline_power * np.ones((1, 1, n_turbines, 3, 3)) + assert np.allclose(baseline_grid_power, test_grid_power) + assert np.shape(baseline_grid_power) == np.shape(test_grid_power) def test_axial_induction(): @@ -383,7 +355,7 @@ def test_axial_induction(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt={turbine.turbine_type: turbine.fCt_interp}, - tilt_interp=np.array([(turbine.turbine_type, None)]), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False]]]), turbine_type_map=turbine_type_map[0,0,0], ) @@ -396,7 +368,7 @@ def test_axial_induction(): tilt_angle=np.ones((1, 1, N_TURBINES)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, N_TURBINES)) * 5.0, fCt={turbine.turbine_type: turbine.fCt_interp}, - tilt_interp=np.array([(turbine.turbine_type, None)] * N_TURBINES), + tilt_interp={turbine.turbine_type: None}, correct_cp_ct_for_tilt=np.array([[[False] * N_TURBINES]]), turbine_type_map=turbine_type_map, ix_filter=INDEX_FILTER, @@ -414,7 +386,7 @@ def test_axial_induction(): tilt_angle=np.ones((1, 1, 1)) * 5.0, ref_tilt_cp_ct=np.ones((1, 1, 1)) * 5.0, fCt={turbine.turbine_type: turbine_floating.fCt_interp}, - tilt_interp=np.array([(turbine_floating.turbine_type, turbine_floating.fTilt_interp)]), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[True]]]), turbine_type_map=turbine_type_map[0,0,0], ) @@ -479,7 +451,7 @@ def test_rotor_velocity_tilt_correction(): tilt_angle=5.0*np.ones((1, 1, 1)), ref_tilt_cp_ct=np.array([turbine.ref_tilt_cp_ct]), pT=np.array([turbine.pT]), - tilt_interp=np.array([(turbine.turbine_type, turbine.fTilt_interp)]), + tilt_interp={turbine.turbine_type: turbine.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[False]]]), rotor_effective_velocities=wind_speed, ) @@ -492,7 +464,7 @@ def test_rotor_velocity_tilt_correction(): tilt_angle=5.0*np.ones((1, 1, N_TURBINES)), ref_tilt_cp_ct=np.array([turbine.ref_tilt_cp_ct] * N_TURBINES), pT=np.array([turbine.pT] * N_TURBINES), - tilt_interp=np.array([(turbine.turbine_type, turbine.fTilt_interp)] * N_TURBINES), + tilt_interp={turbine.turbine_type: turbine.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[False] * N_TURBINES]]), rotor_effective_velocities=wind_speed_N_TURBINES, ) @@ -505,7 +477,7 @@ def test_rotor_velocity_tilt_correction(): tilt_angle=5.0*np.ones((1, 1, 1)), ref_tilt_cp_ct=np.array([turbine_floating.ref_tilt_cp_ct]), pT=np.array([turbine_floating.pT]), - tilt_interp=np.array([(turbine_floating.turbine_type, turbine_floating.fTilt_interp)]), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[True]]]), rotor_effective_velocities=wind_speed, ) @@ -518,9 +490,7 @@ def test_rotor_velocity_tilt_correction(): tilt_angle=5.0*np.ones((1, 1, N_TURBINES)), ref_tilt_cp_ct=np.array([turbine_floating.ref_tilt_cp_ct] * N_TURBINES), pT=np.array([turbine_floating.pT] * N_TURBINES), - tilt_interp=np.array( - [(turbine_floating.turbine_type, turbine_floating.fTilt_interp)] * N_TURBINES - ), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, correct_cp_ct_for_tilt=np.array([[[True] * N_TURBINES]]), rotor_effective_velocities=wind_speed_N_TURBINES, ) @@ -546,27 +516,25 @@ def test_compute_tilt_angles_for_floating_turbines(): tilt = compute_tilt_angles_for_floating_turbines( turbine_type_map=np.array([turbine_type_map[:, :, 0]]), tilt_angle=5.0*np.ones((1, 1, 1)), - tilt_interp=np.array([(turbine_floating.turbine_type, turbine_floating.fTilt_interp)]), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, rotor_effective_velocities=rotor_effective_velocities, ) # calculate tilt again - truth_index = turbine_floating_data["floating_tilt_table"]["wind_speeds"].index(wind_speed) + truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed) tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index] np.testing.assert_allclose(tilt, tilt_truth) - # Mulitple turbines + # Multiple turbines tilt_N_turbines = compute_tilt_angles_for_floating_turbines( turbine_type_map=np.array(turbine_type_map), tilt_angle=5.0*np.ones((1, 1, N_TURBINES)), - tilt_interp=np.array( - [(turbine_floating.turbine_type, turbine_floating.fTilt_interp)] * N_TURBINES - ), + tilt_interp={turbine_floating.turbine_type: turbine_floating.tilt_interp}, rotor_effective_velocities=rotor_effective_velocities_N_TURBINES, ) # calculate tilt again - truth_index = turbine_floating_data["floating_tilt_table"]["wind_speeds"].index(wind_speed) + truth_index = turbine_floating_data["floating_tilt_table"]["wind_speed"].index(wind_speed) tilt_truth = turbine_floating_data["floating_tilt_table"]["tilt"][truth_index] np.testing.assert_allclose(tilt_N_turbines, [[[tilt_truth] * N_TURBINES]]) @@ -580,3 +548,88 @@ def test_asdict(sample_inputs_fixture: SampleInputs): dict2 = new_turb.as_dict() assert dict1 == dict2 + +def test_build_turbine_dict(): + + orig_file_path = Path(__file__).resolve().parent / "data" / "nrel_5MW_custom.yaml" + test_turb_name = "test_turbine_export" + test_file_path = "." + + in_dict = yaml.safe_load( open(orig_file_path, "r") ) + + # Mocked up turbine data + turbine_data_dict = { + "wind_speed":in_dict["power_thrust_table"]["wind_speed"], + "power_coefficient":in_dict["power_thrust_table"]["power"], + "thrust_coefficient":in_dict["power_thrust_table"]["thrust"] + } + + build_turbine_dict( + turbine_data_dict, + test_turb_name, + file_path=test_file_path, + generator_efficiency=in_dict["generator_efficiency"], + hub_height=in_dict["hub_height"], + pP=in_dict["pP"], + pT=in_dict["pT"], + rotor_diameter=in_dict["rotor_diameter"], + TSR=in_dict["TSR"], + air_density=in_dict["ref_density_cp_ct"], + ref_tilt_cp_ct=in_dict["ref_tilt_cp_ct"] + ) + + test_dict = yaml.safe_load( + open(os.path.join(test_file_path, test_turb_name+".yaml"), "r") + ) + + # Correct intended difference for test; assert equal + test_dict["turbine_type"] = in_dict["turbine_type"] + assert list(in_dict.keys()) == list(test_dict.keys()) + assert in_dict == test_dict + + # Now, in absolute values + Cp = np.array(in_dict["power_thrust_table"]["power"]) + Ct = np.array(in_dict["power_thrust_table"]["thrust"]) + ws = np.array(in_dict["power_thrust_table"]["wind_speed"]) + + P = 0.5 * in_dict["ref_density_cp_ct"] * (np.pi * in_dict["rotor_diameter"]**2/4) \ + * Cp * ws**3 + T = 0.5 * in_dict["ref_density_cp_ct"] * (np.pi * in_dict["rotor_diameter"]**2/4) \ + * Ct * ws**2 + + turbine_data_dict = { + "wind_speed":in_dict["power_thrust_table"]["wind_speed"], + "power_absolute": P/1000, + "thrust_absolute": T/1000 + } + + build_turbine_dict( + turbine_data_dict, + test_turb_name, + file_path=test_file_path, + generator_efficiency=in_dict["generator_efficiency"], + hub_height=in_dict["hub_height"], + pP=in_dict["pP"], + pT=in_dict["pT"], + rotor_diameter=in_dict["rotor_diameter"], + TSR=in_dict["TSR"], + air_density=in_dict["ref_density_cp_ct"], + ref_tilt_cp_ct=in_dict["ref_tilt_cp_ct"] + ) + + test_dict = yaml.safe_load( + open(os.path.join(test_file_path, test_turb_name+".yaml"), "r") + ) + + test_dict["turbine_type"] = in_dict["turbine_type"] + assert list(in_dict.keys()) == list(test_dict.keys()) + for k in in_dict.keys(): + if type(in_dict[k]) is dict: + for k2 in in_dict[k].keys(): + assert np.allclose(in_dict[k][k2], test_dict[k][k2]) + elif type(in_dict[k]) is str: + assert in_dict[k] == test_dict[k] + else: + assert np.allclose(in_dict[k], test_dict[k]) + + os.remove( os.path.join(test_file_path, test_turb_name+".yaml") ) diff --git a/tests/type_dec_unit_test.py b/tests/type_dec_unit_test.py index 43bf5bc3a..641f207dc 100644 --- a/tests/type_dec_unit_test.py +++ b/tests/type_dec_unit_test.py @@ -39,11 +39,11 @@ def __attrs_post_init__(self): self.non_initd = 1.1 liststr: List[str] = field( - default=["qwerty", "asdf"], + factory=lambda:["qwerty", "asdf"], validator=iter_validator(list, str) ) array: np.ndarray = field( - default=[1.0, 2.0], + factory=lambda:[1.0, 2.0], converter=floris_array_converter, # validator=iter_validator(np.ndarray, floris_float_type) ) @@ -63,8 +63,8 @@ def test_FromDictMixin_defaults(): defaults = {a.name: a.default for a in AttrsDemoClass.__attrs_attrs__ if a.default} assert cls.y == defaults["y"] assert cls.z == defaults["z"] - np.testing.assert_array_equal(cls.liststr, defaults["liststr"]) - np.testing.assert_array_equal(cls.array, defaults["array"]) + np.testing.assert_array_equal(cls.liststr, defaults["liststr"].factory()) + np.testing.assert_array_equal(cls.array, defaults["array"].factory()) # Test that defaults can be overwritten inputs = {"w": 0, "x": 1, "y": 4.5} @@ -130,23 +130,36 @@ def test_attrs_array_converter(): def test_convert_to_path(): - # Test that a string works str_input = "../tests" + expected_path = (Path(__file__).parent / str_input).resolve() + + # Test that a string works test_str_input = convert_to_path(str_input) - assert isinstance(test_str_input, Path) + assert test_str_input == expected_path # Test that a pathlib.Path works - path_input = Path("../tests") + path_input = Path(str_input) test_path_input = convert_to_path(path_input) - assert isinstance(test_path_input, Path) + assert test_path_input == expected_path # Test that both of those inputs are the same + # NOTE These first three asserts tests the relative path search assert test_str_input == test_path_input - # Test that a non-existent folder also works even though it's a valid data type - str_input = "tests" - test_str_input = convert_to_path(str_input) - assert isinstance(test_str_input, Path) + # Test absolute path + abs_path = expected_path + test_abs_path = convert_to_path(abs_path) + assert test_abs_path == expected_path + + # Test a file + file_input = Path(__file__) + test_file = convert_to_path(file_input) + assert test_file == file_input + + # Test that a non-existent folder fails, now that the conversion has a multi-pronged search + str_input = str(Path(__file__).parent / "bad_path") + with pytest.raises(FileExistsError): + convert_to_path(str_input) # Test that invalid data types fail with pytest.raises(TypeError): diff --git a/tests/vec3_unit_test.py b/tests/vec3_unit_test.py deleted file mode 100644 index dc1ac091c..000000000 --- a/tests/vec3_unit_test.py +++ /dev/null @@ -1,132 +0,0 @@ -# Copyright 2021 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. - -# See https://floris.readthedocs.io for documentation - - -import numpy as np -import pytest - -from floris.utilities import Vec3 - - -def test_instantiation_with_list(): - """ - The class should initialize with a list of length 3. - The class should raise an exception if the length of - points is not 3. - """ - vec3 = Vec3([1, 2, 3]) - assert vec3.x1 == 1.0 - assert vec3.x2 == 2.0 - assert vec3.x3 == 3.0 - - with pytest.raises(Exception): - vec3 = Vec3([1, 2, 3, 4]) - - with pytest.raises(Exception): - vec3 = Vec3([1, 2]) - - -def test_add(vec3_fixture): - """ - The overloaded operator should accept a scalar value and apply it to - all components. - It should also accept a Vec3 value and perform an element-wise operation. - """ - scalar = vec3_fixture + 1 - assert scalar.x1 == vec3_fixture.x1 + 1 - assert scalar.x2 == vec3_fixture.x2 + 1 - assert scalar.x3 == vec3_fixture.x3 + 1 - - vector = vec3_fixture + Vec3([2, 3, 4]) - assert vector.x1 == vec3_fixture.x1 + 2 - assert vector.x2 == vec3_fixture.x2 + 3 - assert vector.x3 == vec3_fixture.x3 + 4 - - -def test_subtract(vec3_fixture): - """ - The overloaded operator should accept a scalar value and apply it to - all components. - It should also accept a Vec3 value and perform an element-wise operation. - """ - scalar = vec3_fixture - 1 - assert scalar.x1 == vec3_fixture.x1 - 1 - assert scalar.x2 == vec3_fixture.x2 - 1 - assert scalar.x3 == vec3_fixture.x3 - 1 - - vector = vec3_fixture - Vec3([2, 3, 4]) - assert vector.x1 == vec3_fixture.x1 - 2 - assert vector.x2 == vec3_fixture.x2 - 3 - assert vector.x3 == vec3_fixture.x3 - 4 - - -def test_multiply(vec3_fixture): - """ - The overloaded operator should accept a scalar value and apply it to - all components. - It should also accept a Vec3 value and perform an element-wise operation. - """ - scalar = vec3_fixture * 10 - assert scalar.x1 == vec3_fixture.x1 * 10 - assert scalar.x2 == vec3_fixture.x2 * 10 - assert scalar.x3 == vec3_fixture.x3 * 10 - - vector = vec3_fixture * Vec3([2, 3, 4]) - assert vector.x1 == vec3_fixture.x1 * 2 - assert vector.x2 == vec3_fixture.x2 * 3 - assert vector.x3 == vec3_fixture.x3 * 4 - - -def test_divide(vec3_fixture): - """ - The overloaded operator should accept a scalar value and apply it to - all components. - It should also accept a Vec3 value and perform an element-wise operation. - """ - scalar = vec3_fixture / 10.0 - np.testing.assert_allclose(scalar.x1, vec3_fixture.x1 / 10.0) - np.testing.assert_allclose(scalar.x2, vec3_fixture.x2 / 10.0) - np.testing.assert_allclose(scalar.x3, vec3_fixture.x3 / 10.0) - - vector = vec3_fixture / Vec3([10, 100, 1000]) - np.testing.assert_allclose(vector.x1, vec3_fixture.x1 / 10.0) - np.testing.assert_allclose(vector.x2, vec3_fixture.x2 / 100.0) - np.testing.assert_allclose(vector.x3, vec3_fixture.x3 / 1000.0) - - -def test_equality(vec3_fixture): - """ - The overloaded equality operator should compare each component to the - same components of the right-hand-side value. - """ - rhs = Vec3([vec3_fixture.x1, vec3_fixture.x2, vec3_fixture.x3]) - assert vec3_fixture == rhs - - rhs = Vec3([vec3_fixture.x1 + 1, vec3_fixture.x2, vec3_fixture.x3]) - assert vec3_fixture != rhs - - rhs = Vec3([vec3_fixture.x1, vec3_fixture.x2 + 1, vec3_fixture.x3]) - assert vec3_fixture != rhs - - rhs = Vec3([vec3_fixture.x1, vec3_fixture.x2, vec3_fixture.x3 + 1]) - assert vec3_fixture != rhs - - -def test_elements_property(vec3_fixture): - """Ensure that the x1, x2, and x3 elements match the expected values. - """ - x1, x2, x3 = vec3_fixture.elements - assert 4.0 == x1 - assert 4.0 == x2 - assert 0.0 == x3