diff --git a/.gitignore b/.gitignore index e6c3648d..1b8af0f9 100644 --- a/.gitignore +++ b/.gitignore @@ -32,4 +32,4 @@ SMARTEOLE_WakeSteering_ReadMe.xlsx SMARTEOLE_WakeSteering_Map.pdf SMARTEOLE-WFC-open-dataset.zip examples_artificial_data/03_energy_ratio/heterogeneity_layouts.pdf -docs/examples +*.pkl diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 091d376f..e32bfd99 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,7 +2,7 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v5.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer diff --git a/docs/_toc.yml b/docs/_toc.yml index 8edeb012..5cc4a051 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -6,23 +6,17 @@ root: index parts: - caption: Getting Started chapters: - - file: overview + - file: introduction - file: installation - - file: getting_started - caption: User Reference chapters: - # - file: data_processing - file: flasc_data_format - file: energy_ratio - file: energy_change + - file: model_fit - file: licensing - # - caption: Developer Reference - # chapters: - # - file: contributing - # - file: development - # - file: testing - caption: Examples Data Processing chapters: - file: examples/01_raw_data_processing/03_northing_calibration_hoger diff --git a/docs/getting_started.md b/docs/getting_started.md deleted file mode 100644 index e4806576..00000000 --- a/docs/getting_started.md +++ /dev/null @@ -1,30 +0,0 @@ -# Getting started - -The easiest way to get started is to install FLASC and -then follow the examples. The correct order is: - -## Install FLASC -Install the repository following the instructions in -[installation](installation). - -## FLASC examples -You can generate a demo dataset by following the examples in -``examples_smarteole/``. The notebook ``02_download_and_format_dataset.ipynb`` -downloads data from a wake steering experiment conducted in 2019. We encourage -users to step through the notebooks in ``examples_smarteole/`` in order to -develop an understanding of FLASC's capabilities using a dataset from a real -field experiment. - -Additional useful examples can be found in ``examples_artificial_data/``, where -we intentionally introduce "challenges" for the FLASC tools to solve using -artificially-generated data. This provides a good way for users to get to know -the FLASC tools in more depth. Again, we recommend stepping through the -examples in the subdirectories in their numerical order. - -Roughly speaking, the examples in both ``examples_smarteole/`` and -``examples_artificial_data`` demonstrate the FLASC modules in the order: -- `flasc.data_processing` -- `flasc.analysis` -- `flasc.model_fitting` - -and use `flasc.utilities` throughout. diff --git a/docs/index.md b/docs/index.md index 9d9b1be1..39055bf6 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,6 +1,5 @@ -# FLASC documentation - +# FLASC Welcome to the documentation of the NREL FLASC repository! @@ -8,15 +7,42 @@ Welcome to the documentation of the NREL FLASC repository! FLASC v2.3 now requires `numpy` version 2, following the update in FLORIS v4.3. See the [numpy documentation for details](https://numpy.org/doc/stable/numpy_2_0_migration_guide.html). ``` -FLASC provides a rich suite of analysis tools for SCADA data filtering & -analysis, wind farm model validation, field experiment design, and field -experiment monitoring. The repository is centrally built around NRELs -in-house [FLORIS](https://github.com/NREL/floris/discussions/) - wake modeling utility. +FLASC provides a comprehensive toolkit for wind farm analysis, combining SCADA data processing with advanced wake modeling capabilities. The repository is intended as a community-driven toolbox, available on its [GitHub Repository](https://github.com/NREL/flasc). + +## What is FLASC? + +FLASC offers analysis tools for SCADA data filtering & analysis, wind farm model validation, field experiment design, and field experiment monitoring. Built around NREL's [FLORIS](https://github.com/NREL/floris/discussions/) wake modeling utility, FLASC enables researchers and practitioners to: + +- **Process and filter SCADA data** with robust outlier detection and quality control +- **Analyze energy production patterns** using energy ratio methodology for wake quantification +- **Calibrate wake models** automatically to match observed turbine performance +- **Evaluate field experiments** with comprehensive uplift analysis tools + +## Documentation Structure + +This documentation is organized to guide you from basic concepts to advanced applications: + +### Getting Started +- **[Introduction](introduction)**: Overview of FLASC capabilities and package structure +- **[Installation](installation)**: Setup instructions and requirements + +### Core Concepts +- **[FLASC Data Format](flasc_data_format)**: Understanding FLASC's data structures and conventions +- **[Energy Ratio Analysis](energy_ratio)**: Quantifying wake effects and turbine performance +- **[Energy Change Analysis](energy_change)**: Methods for calculating production changes +- **[Model Fitting](model_fit)**: Automated FLORIS model calibration to SCADA data + +### Practical Applications +The documentation includes extensive examples demonstrating real-world applications using both synthetic data (`examples_artificial_data/`) and field experiment data (`examples_smarteole/`). These examples follow a typical FLASC workflow: data processing → analysis → model calibration. + +## Key Features + +FLASC's modular design supports the complete wind farm analysis workflow: -FLASC also largely relies on the energy ratio to, among other things, quantify wake -losses in synthetic and historical data, to perform turbine northing -calibrations, and for model parameter estimation. +- **Data Processing**: Import, filter, and quality-control SCADA data with specialized tools for wind measurements +- **Wake Analysis**: Quantify wake effects using energy ratios and validate against physics-based models +- **Model Calibration**: Automatically tune FLORIS parameters to match observed performance +- **Experiment Analysis**: Evaluate control strategies and technology impacts with statistical rigor The FLASC repository is intended as a community driven toolbox, available on its [GitHub Repository](https://github.com/NREL/flasc). diff --git a/docs/installation.md b/docs/installation.md index 086de17f..47f07ae6 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -1,44 +1,3 @@ - - # Installation FLASC is available as a package on PyPI. We strongly recommend installing FLASC diff --git a/docs/introduction.md b/docs/introduction.md new file mode 100644 index 00000000..ae2f87a4 --- /dev/null +++ b/docs/introduction.md @@ -0,0 +1,77 @@ +# Introduction to FLASC + +FLASC provides a rich suite of analysis tools for SCADA data filtering & analysis, wind farm model validation, field experiment design, and field experiment monitoring. The repository is centrally built around NREL's in-house [FLORIS](https://github.com/nrel/floris) wake modeling utility. FLASC also largely relies on the "energy ratio" to quantify wake losses in synthetic and historical data, perform turbine northing calibrations, and for model parameter estimation. + +## Getting Started + +The easiest way to get started is to install FLASC and then follow the examples. The recommended approach is: + +### 1. Install FLASC +Install the repository following the instructions in [installation](installation). + +### 2. Explore Examples +You can generate a demo dataset by following the examples in `examples_smarteole/`. The notebook `02_download_and_format_dataset.ipynb` downloads data from a wake steering experiment conducted in 2019. We encourage users to step through the notebooks in `examples_smarteole/` in order to develop an understanding of FLASC's capabilities using a dataset from a real field experiment. + +Additional useful examples can be found in `examples_artificial_data/`, where we intentionally introduce "challenges" for the FLASC tools to solve using artificially-generated data. This provides a good way for users to get to know the FLASC tools in more depth. Again, we recommend stepping through the examples in the subdirectories in their numerical order. + +### 3. Workflow Overview +Roughly speaking, the examples in both `examples_smarteole/` and `examples_artificial_data` demonstrate the FLASC modules in the order: +- `flasc.data_processing` - Import and filter SCADA data +- `flasc.analysis` - Energy ratio analysis and uplift calculations +- `flasc.model_fitting` - Calibrate FLORIS models to SCADA data + +and use `flasc.utilities` throughout for supporting functions. + +## FLASC Package Structure + +FLASC consists of multiple modules, each serving specific analysis needs: + +### flasc.data_processing +This module contains functions that support importing and processing raw SCADA data files. Data is saved in feather format for optimal balance of storage size and load and write speed. + +Functions include filtering data by wind direction, wind speed and/or TI, deriving the ambient conditions from the upstream turbines, all the while dealing with angle wrapping for angular variables. Outliers can be detected and removed at the turbine level. Filtering methods include sensor-stuck type of fault detection and analysis of the turbine wind speed-power curve. + +Also included are functions to downsample, upsample and calculate moving averages of a data frame with SCADA and/or FLORIS data. These functions allow the user to specify which columns contain angular variables, and consequently 360 deg wrapping is taken care of. It also allows the user to calculate the min, max, std and median for downsampled data frames. It leverages efficient functions inherent in pandas and polars to maximize performance. + +Finally, functions are provided to detect northing bias (caused by miscalibrated yaw encoders) in turbine data. + +### flasc.analysis +This module contains classes to calculate and visualize the energy ratio as defined by Fleming et al. (2019). The energy ratio is a very useful quantity in SCADA data analysis and related model validation. It represents the amount of energy produced by a turbine relative to what that turbine would have produced if no wakes were present. See [energy ratio](energy_ratio) for more details. Also included are methods for calculating the total power uplift in a comparative field experiment. + +### flasc.model_fitting +This module provides automated calibration of FLORIS wake models to SCADA data through the ModelFit framework. It includes modular cost functions, optimization algorithms, and tools for parameter sensitivity analysis. See [model fitting](model_fit) for comprehensive documentation. + +### flasc.utilities +This module contains utilities that support the other modules within FLASC. These utilities help to interface with FLORIS and calculate a large set of floris simulations for different atmospheric conditions, yaw misalignments and/or model parameters. It also includes two functions to precalculate and respectively interpolate from a large set of model solutions to speed up further postprocessing. + +Also included are functions to estimate the timeshift between two sources of data, for example, to synchronize measurements from a met mast with measurements from SCADA data. The module also includes a function to estimate the offset between two timeseries of wind direction measurements. This is useful to determine the northing bias of a turbine if you know the correct calibration of at least one other wind turbine. Finally, this module also contains a function to estimate the atmospheric turbulence intensity based on the power measurements of the turbines inside a wind farm. + +Additionally, visualization tools can be found in `flasc.visualization` and `flasc.yaw_optimizer_visualization`. + +## Literature + +See {cite:p}`Doekemeijer2022a` and {cite:p}`Bay2022a` for practical examples of how the flasc repository is used for processing and analyses of historical SCADA data of three offshore wind farms. + +```{bibliography} +``` + +## Citation + +If FLASC played a role in your research, please cite it. This software can be cited as: + + FLASC. Version 2.0.1 (2024). Available at https://github.com/NREL/flasc. + +For LaTeX users: + + @misc{flasc2024, + author = {NREL}, + title = {FLASC. Version 2.0.1}, + year = {2024}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/NREL/flasc}, + } + +## Questions + +For technical questions regarding FLASC usage, please post your questions to [GitHub Discussions](https://github.com/NREL/flasc/discussions) on the FLASC repository. Alternatively, email the NREL FLASC team at `paul.fleming@nrel.gov `_ or `michael.sinner@nrel.gov `_. diff --git a/docs/model_fit.md b/docs/model_fit.md new file mode 100644 index 00000000..ecaa1f1e --- /dev/null +++ b/docs/model_fit.md @@ -0,0 +1,219 @@ +# FLORIS model calibration with ModelFit + +FLASC's ModelFit capability provides a modular framework for calibrating FLORIS engineering wake models to SCADA data. This automated calibration process optimizes model parameters to minimize differences between predicted and observed turbine performance, improving the accuracy of wake modeling for wind farm analysis. + +## Overview + +ModelFit implements an optimization-based approach to model calibration that links modular cost functions with optimization algorithms. The system is designed to: + +- **Automatically calibrate FLORIS parameters** to match SCADA measurements +- **Support multiple cost functions** for different optimization objectives +- **Provide flexible optimization algorithms** including grid search and Bayesian optimization +- **Handle various FLORIS model types** including standard (`FlorisModel`), parallel (`ParallelFlorisModel`), and uncertain models (`UncertainFlorisModel`) +- **Enable custom cost function development** through a base class interface + +The ModelFit framework replaces and deprecates older calibration methods, providing a more robust and extensible approach to model tuning. + +## ModelFit Class + +The `ModelFit` class serves as the central component that coordinates FLORIS simulations, cost function evaluation, and parameter optimization. It manages the interface between SCADA data, FLORIS models, and optimization routines. + +### Basic Usage + +```python +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.opt_library import opt_optuna + +# Define parameters to optimize +parameter_list = [("wake", "wake_velocity_parameters", "jensen", "we")] +parameter_name_list = ["wake_expansion"] # Single string name +parameter_range_list = [(0.01, 0.07)] + +# Create ModelFit instance +mf = ModelFit( + df_scada, # SCADA data in FLASC format + floris_model, # FLORIS model instance + TurbinePowerMeanAbsoluteError(), # Cost function + parameter_list=parameter_list, + parameter_name_list=parameter_name_list, + parameter_range_list=parameter_range_list +) + +# Run optimization +result = opt_optuna(mf, n_trials=100) +``` + +For multi-parameter optimization, you can tune multiple elements of parameter arrays simultaneously. In this example code +the first array entries of the `wake_expansion_rates` array in the `empirical_gauss` model are calibrated together. + +```python +# Define multiple parameters to optimize (first two wake expansion rates) +parameter_list = [ + ("wake", "wake_velocity_parameters", "empirical_gauss", "wake_expansion_rates"), + ("wake", "wake_velocity_parameters", "empirical_gauss", "wake_expansion_rates") +] +parameter_name_list = ["we_1", "we_2"] # Names for each parameter +parameter_range_list = [(0.0, 0.05), (0.0, 0.08)] # Ranges for each parameter +parameter_index_list = [0, 1] # Array indices to optimize + +# Create ModelFit instance for multi-parameter optimization +mf_multi = ModelFit( + df_scada, + floris_model, + TurbinePowerMeanAbsoluteError(), + parameter_list=parameter_list, + parameter_name_list=parameter_name_list, + parameter_range_list=parameter_range_list, + parameter_index_list=parameter_index_list +) + +# Run optimization +result = opt_optuna(mf_multi, n_trials=200) +``` + +## Cost Functions and Cost Library + +The cost library provides a number of pre-built cost functions and base classes for developing custom cost functions. +Each cost function is implemented as a subclass of `CostFunctionBase` and must implement the `cost()` method that computes the cost given a FLORIS simulation dataframe. This design allows for easy extension and customization of cost metrics, with the ability to add define extra parameters of the cost function on instantiation of the cost function object. + +### Recommended Cost Function + +While a number of cost functions are included, we recommend using **`TurbinePowerMeanAbsoluteError`** for most applications because it avoids several critical issues found in other cost functions: + +- **Avoids skewing toward outliers**: Unlike squared error metrics, mean absolute error is not dominated by extreme values +- **Prevents turbine error cancellation**: Turbine-level errors are computed individually before averaging, preventing positive and negative errors from different turbines from canceling out +- **Eliminates time cancellation**: Absolute errors at each time step are preserved, preventing temporal error cancellation effects +- **Physical interpretability**: Errors are expressed in power units (kW) that are meaningful to engineers +- **Computational efficiency**: Simple calculation that scales well with large datasets + +See also [Modeling and analysis of offshore wind farm wake effects on wind turbine components and power production](https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/3178958) by Diederik van Binsbergen for further analysis on choice of cost function. + +### Available Cost Functions + +The library includes turbine-level, farm-level, and wake-specific cost functions, as well as base classes for custom development. For specialized applications, custom cost functions can be created by inheriting from `CostFunctionBase`: + +```python +from flasc.model_fitting.cost_library import CostFunctionBase + +class CustomCostFunction(CostFunctionBase): + """Custom cost function example.""" + + def cost(self, df_floris): + """Implement custom cost calculation.""" + error = self.df_scada["pow_004"] - df_floris["pow_004"] + return error.abs().mean() +``` + +Prepackaged cost functions include: +- `TurbinePowerMeanAbsoluteError`: The mean absolute error in power at each turbine and averages across turbines and time steps. Our recommended cost function. +- `TurbinePowerRootMeanSquaredError`: The square root of the mean of the squared error in power at each turbine at each time step. +- `FarmPowerMeanAbsoluteError`: The mean absolute error in total farm power at each time step. +- `FarmPowerRootMeanSquaredError`: The square root of the mean of the squared error in total farm power at each time step. +- `WakeLossRootMeanSquaredError`: The root mean squared error in wake loss at each turbine at each time step, where the wake loss is defined as the difference between the free stream power and the "waked" power at the turbine. + +## Optimization Functions and Optimization Library + +The optimization library provides algorithms for parameter optimization. It currently includes a simple grid search and a Bayesian optimization (optuna). The output of each optimization function is a dictionary containing the optimal parameter values (under the `"optimized_parameter_values"` key), the minimum cost achieved (`"optimized_cost"`), and possible additional metadata about the optimization process under optimizer-specific keys. + +### Grid Search Optimization + +Grid search evaluates all parameter combinations across a defined grid: + +```python +from flasc.model_fitting.opt_library import opt_sweep + +# Simple grid search +result = opt_sweep(mf, n_grid=10) + +# Different grid sizes per parameter +result = opt_sweep(mf, n_grid=[10, 15, 8]) +``` + +As well as the standard `"optimized_parameter_values"` and `"optimized_cost"` keys, the `results` dictionary returned by `opt_sweep` also includes `"all_parameter_combinations"` and `"all_costs"` keys that contain the full set of parameter combinations evaluated and their corresponding costs. + +### Bayesian Optimization with Optuna + +Optuna provides efficient Bayesian optimization for larger parameter spaces: + +```python +from flasc.model_fitting.opt_library import opt_optuna + +# Basic Optuna optimization +# n_trials limits the number of trials to 100 +result = opt_optuna(mf, n_trials=100) + +# With timeout and n_trials limited +result = opt_optuna(mf, n_trials=200, timeout=3600) +``` + +As well as the standard `"optimized_parameter_values"` and `"optimized_cost"` keys, the `results` dictionary returned by `opt_optuna` also includes an `"optuna_study"` key that contains the full Optuna study object. This can be used for further analysis and visualization of the optimization process. + +#### Optuna Visualization and Analysis + +Optuna provides analysis tools that can be used directly with ModelFit results: + +```python +from optuna.visualization.matplotlib import plot_optimization_history, plot_slice, plot_contour + +# Extract study object from results +study = result["optuna_study"] + +# Visualization options +plot_optimization_history(study) # Progress over trials +plot_slice(study) # Parameter sensitivity +plot_contour(study) # Parameter interactions (2D) +``` + + +### Optimizers with wd_std + +Both implementations of optimizations included have versions that include optimization of the +standard deviation of wind direction (`wd_std`) within the `UncertainFlorisModel` as an additional +component of the optimization. + +```python +from flasc.model_fitting.opt_library import opt_optuna_with_wd_std + +# Optimize parameters + wind direction uncertainty +result = opt_optuna_with_wd_std(uncertain_model_fit, n_trials=100) +``` + +## Examples + +### Artificial Data Examples + +The artificial data examples in `examples_artificial_data/05_model_fit/` demonstrate fundamental ModelFit usage with synthetic two-turbine datasets: + +- **`00_generate_data.py`**: Creates synthetic SCADA data using FLORIS simulations with known parameter values. + +- **`01a_evaluate_costs.py`**: Shows how to evaluate different cost functions across parameter ranges. + +- **`01b_evaluate_costs_uncertain.py`**: Demonstrates cost function evaluation using UncertainFlorisModel, showing how wind direction uncertainty affects parameter sensitivity and cost landscapes. + +- **`02a_optimize_parameter_optsweep.py`**: Demonstrates grid search optimization to find optimal wake expansion parameters, ideal for understanding parameter spaces with few dimensions. + +- **`02b_optimize_parameter_optuna.py`**: Illustrates Bayesian optimization using Optuna, including visualization tools like `plot_optimization_history` and `plot_slice` for analyzing optimization progress and parameter importance. + +- **`02c_optimize_parameter_optuna_wd_std.py`**: Demonstrates optimization including wind direction standard deviation (`wd_std`) as an additional parameter using `opt_optuna_with_wd_std`, showing how to tune both model parameters and uncertainty parameters simultaneously. + +### Real-World Application Example + +The SMARTEOLE example in `examples_smarteole/11_model_tuning_with_model_fit.ipynb` demonstrates using ModelFit with real world data. + +- **Sequential parameter tuning**: First calibrates wake expansion parameters using baseline data, then optimizes deflection gain using wake steering data. + +- **Atmospheric condition analysis**: Demonstrates separate tuning for day and night conditions, revealing how optimal parameters vary with atmospheric stability. + +- **Multi-parameter optimization**: Shows simultaneous optimization of multiple parameters and compares results with sequential tuning approaches. + + + +## Deprecated Code + +ModelFit replaces several older calibration methods. The following items are deprecated and will be removed in a future release. They remain available for the time being in keeping with semantic versioning principles. + +### Deprecated Modules +- **`flasc.model_fitting.floris_tuning`**: Original tuning implementation. This package is considered deprecated as of FLASC v2.4, and will be removed in a future release. Use `flasc.model_fitting.model_fit` instead. +- **Examples 07 and 08**: SMARTEOLE tuning examples using old methods. See `examples_smarteole/11_model_tuning_with_model_fit.ipynb` for the replacement. + diff --git a/docs/overview.md b/docs/overview.md deleted file mode 100644 index 3bc9a380..00000000 --- a/docs/overview.md +++ /dev/null @@ -1,107 +0,0 @@ -# Overview - -FLASC provides a rich suite of analysis tools for SCADA data filtering & -analysis, wind farm model validation, field experiment design, and field -experiment monitoring. The repository is centrally built around NREL's -in-house [FLORIS](https://github.com/nrel/floris) wake modeling utility. -FLASC also largely relies on the "energy ratio" to quantify wake -losses in synthetic and historical data, perform turbine northing -calibrations, and for model parameter estimation. - -# FLASC package - -FLASC consists of multiple modules, including: - - -## flasc.data_processing - -This module contains functions that supports importing and processing raw -SCADA data files. Data is saved in feather -format for optimal balance of storage size and load and write speed. - -Functions include filtering data by wind direction, wind speed an/or TI, -deriving the ambient conditions from the upstream turbines, all the while -dealing with angle wrapping for angular variables. Outliers -can be detected and removed at the turbine level. -Filtering methods include sensor-stuck type of -fault detection and analysis of the turbine wind speed-power curve. - -Also included are functions to downsample, upsample and calculate -moving averages of a data frame with SCADA and/or FLORIS data. These functions -allow the user to specify which columns contain angular variables, and -consequently 360 deg wrapping is taken care of. It also allows the user -to calculate the min, max, std and median for downsampled data frames. It -leverages efficient functions inherent in pandas and polars to maximize -performance. - -Finally, functions are provided to detect northing bias (caused by -miscalibrated yaw encoders) in turbine data. - - -## flasc.analysis - -This module contains classes to calculate and visualize the energy ratio as -defined by Fleming et al. (2019). The energy ratio is a very useful quantity -in SCADA data analysis and related model validation. It represents the amount -of energy produced by a turbine relative to what that turbine would have -produced if no wakes were present. See [energy ratio](energy_ratio) for more -details. Also included are methods for calculating the total power uplift in a -comparative field experiment. - -## flasc.utilities - -This module contains utilities that support the other modules within FLASC. -These utilities help to interface with FLORIS and calculate a large set of -floris simulations for different atmospheric conditions, yaw misalignments -and/or model parameters. It also includes two functions to precalculate and -respectively interpolate from a large set of model solutions to speed up -further postprocessing. - -Also included are functions to estimate the timeshift between -two sources of data, for example, to sychronize measurements from a met mast -with measurements from SCADA data. The module also includes a function to -estimate the offset between two timeseries of wind direction measurements. -This is useful to determine the northing bias of a turbine if you know the -correct calibration of at least one other wind turbine. Finally, this module -also contains a function to estimate the atmospheric turbulence intensity -based on the power measurements of the turbines inside a wind farm. - -Additionally, visualization tools can be found in `flasc.visualization` and `flasc.yaw_optimizer_visualization.` - -# Literature - -See {cite:p}`Doekemeijer2022a` and {cite:p}`Bay2022a`for practical -examples of how the flasc repository is used or processing and analyses of -historical SCADA data of three offshore wind farms. - - ```{bibliography} - ``` - -# Citation - - -If FLASC played a role in your research, please cite it. This software can be -cited as: - - FLASC. Version 2.0.1 (2024). Available at https://github.com/NREL/flasc. - -For LaTeX users: - - - @misc{flasc2024, - author = {NREL}, - title = {FLASC. Version 2.0.1}, - year = {2024}, - publisher = {GitHub}, - journal = {GitHub repository}, - url = {https://github.com/NREL/flasc}, - } - - -# Questions - -For technical questions regarding FLASC usage, please post your questions to -[GitHub Discussions](https://github.com/NREL/flasc/discussions) on the -FLASC repository. Alternatively, email the NREL FLASC team at -`paul.fleming@nrel.gov `_ or -`michael.sinner@nrel.gov `_. diff --git a/examples_artificial_data/01_raw_data_processing/02_flasc_data_frame.ipynb b/examples_artificial_data/01_raw_data_processing/02_flasc_data_frame.ipynb new file mode 100644 index 00000000..09f9fe07 --- /dev/null +++ b/examples_artificial_data/01_raw_data_processing/02_flasc_data_frame.ipynb @@ -0,0 +1,443 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FlascDataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FlascDataFrame...." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from flasc.flasc_dataframe import FlascDataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate synthetic data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Suppose the we have a 3 turbine farm with turbines names 'TB01', 'TB02', 'TB03'\n", + "# For each turbine we have power, wind speed and wind direction data\n", + "# Assume that in the native data collection system,\n", + "# the signal names for each channel are given below\n", + "\n", + "N = 20 # Number of data points\n", + "\n", + "# Wind speeds\n", + "wind_speed_TB01 = np.random.rand(N) + 8.0\n", + "wind_speed_TB02 = np.random.rand(N) + 7.5\n", + "wind_speed_TB03 = np.random.rand(N) + 8.5\n", + "\n", + "# Wind directions\n", + "wind_dir_TB01 = 10 * np.random.rand(N) + 270.0\n", + "wind_dir_TB02 = 10 * np.random.rand(N) + 270.0\n", + "wind_dir_TB03 = 10 * np.random.rand(N) + 270.0\n", + "\n", + "# Power\n", + "power_TB01 = wind_speed_TB01**3\n", + "power_TB02 = wind_speed_TB02**3\n", + "power_TB03 = wind_speed_TB03**3\n", + "\n", + "# Time\n", + "time = np.arange(N)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Add this data to a pandas dataframe\n", + "df = pd.DataFrame(\n", + " {\n", + " \"time\": time,\n", + " \"wind_speed_TB01\": wind_speed_TB01,\n", + " \"wind_speed_TB02\": wind_speed_TB02,\n", + " \"wind_speed_TB03\": wind_speed_TB03,\n", + " \"wind_dir_TB01\": wind_dir_TB01,\n", + " \"wind_dir_TB02\": wind_dir_TB02,\n", + " \"wind_dir_TB03\": wind_dir_TB03,\n", + " \"power_TB01\": power_TB01,\n", + " \"power_TB02\": power_TB02,\n", + " \"power_TB03\": power_TB03,\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add to FlascDataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Declare a name_map dictionary to map the signal names to the turbine names\n", + "name_map = {\n", + " \"time\": \"time\",\n", + " \"wind_speed_TB01\": \"ws_000\",\n", + " \"wind_speed_TB02\": \"ws_001\",\n", + " \"wind_speed_TB03\": \"ws_002\",\n", + " \"wind_dir_TB01\": \"wd_000\",\n", + " \"wind_dir_TB02\": \"wd_001\",\n", + " \"wind_dir_TB03\": \"wd_002\",\n", + " \"power_TB01\": \"pow_000\",\n", + " \"power_TB02\": \"pow_001\",\n", + " \"power_TB03\": \"pow_002\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timews_000ws_001ws_002wd_000wd_001wd_002pow_000pow_001pow_002
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448.0697367.8154179.014511273.859713279.687027276.033408525.506343477.371464732.531900
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" + ], + "text/plain": [ + " time ws_000 ws_001 ws_002 wd_000 wd_001 wd_002 \\\n", + "0 0 8.764502 8.157309 9.307117 275.810165 276.755093 272.444566 \n", + "1 1 8.450410 8.452772 8.535425 275.299285 274.063929 271.070424 \n", + "2 2 8.668215 7.654708 8.733668 275.228593 275.422351 273.983654 \n", + "3 3 8.030583 8.434888 8.868566 277.248916 275.195408 278.022614 \n", + "4 4 8.069736 7.815417 9.014511 273.859713 279.687027 276.033408 \n", + "\n", + " pow_000 pow_001 pow_002 \n", + "0 673.258291 542.801206 806.204968 \n", + "1 603.438938 603.945117 621.835407 \n", + "2 651.311896 448.524130 666.177672 \n", + "3 517.894491 600.119784 697.525638 \n", + "4 525.506343 477.371464 732.531900 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Declare an instance of FlascDataFrame\n", + "fdf = FlascDataFrame(df, name_map=name_map)\n", + "\n", + "fdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timewind_speed_TB01wind_speed_TB02wind_speed_TB03wind_dir_TB01wind_dir_TB02wind_dir_TB03power_TB01power_TB02power_TB03
008.7645028.1573099.307117275.810165276.755093272.444566673.258291542.801206806.204968
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338.0305838.4348888.868566277.248916275.195408278.022614517.894491600.119784697.525638
448.0697367.8154179.014511273.859713279.687027276.033408525.506343477.371464732.531900
\n", + "
" + ], + "text/plain": [ + " time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \\\n", + "0 0 8.764502 8.157309 9.307117 275.810165 \n", + "1 1 8.450410 8.452772 8.535425 275.299285 \n", + "2 2 8.668215 7.654708 8.733668 275.228593 \n", + "3 3 8.030583 8.434888 8.868566 277.248916 \n", + "4 4 8.069736 7.815417 9.014511 273.859713 \n", + "\n", + " wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03 \n", + "0 276.755093 272.444566 673.258291 542.801206 806.204968 \n", + "1 274.063929 271.070424 603.438938 603.945117 621.835407 \n", + "2 275.422351 273.983654 651.311896 448.524130 666.177672 \n", + "3 275.195408 278.022614 517.894491 600.119784 697.525638 \n", + "4 279.687027 276.033408 525.506343 477.371464 732.531900 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "## Illustrate transformation back to user names\n", + "fdf.convert_to_user_format().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FlascDataFrame in FLASC format\n", + " time variable value\n", + "0 0 ws_000 8.764502\n", + "1 1 ws_000 8.450410\n", + "2 2 ws_000 8.668215\n", + "3 3 ws_000 8.030583\n", + "4 4 ws_000 8.069736\n", + "FlascDataFrame in FLASC format\n", + " time variable value\n", + "175 15 pow_002 812.743716\n", + "176 16 pow_002 690.231480\n", + "177 17 pow_002 770.042469\n", + "178 18 pow_002 843.600158\n", + "179 19 pow_002 739.761434\n" + ] + } + ], + "source": [ + "## Illustrate wide to long transformation\n", + "print(fdf._convert_wide_to_long().head())\n", + "print(fdf._convert_wide_to_long().tail())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "flasc", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb b/examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb deleted file mode 100644 index 3b175c7b..00000000 --- a/examples_artificial_data/04_floris_tuning/00_tune_floris_to_artificial_data.ipynb +++ /dev/null @@ -1,1432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" - ] - } - ], - "source": [ - "# Suppress warnings\n", - "import warnings\n", - "from pathlib import Path\n", - "\n", - "import floris.layout_visualization as layoutviz\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from floris import FlorisModel\n", - "\n", - "import flasc.model_fitting.floris_tuning as ft\n", - "\n", - "# from flasc.model_tuning.floris_tuner import FlorisTuner\n", - "import flasc.utilities.floris_tools as ftools\n", - "from flasc import FlascDataFrame\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model_to_tune_to = \"emgauss\" # gch, turbopark, emgauss\n", - "n_row_x = 3 # Define the number of turbine rows in the x direction\n", - "n_row_y = 3 # Define the number of turbine rows in the y direction (should be odd)\n", - "D_between_turbines_x = 7 # Distance between turbines\n", - "D_between_turbines_y = 10 # Distance between turbines\n", - "\n", - "if n_row_y % 2 == 0:\n", - " warnings.warn(\"Even number will be weird\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Parameters about timing and grouping\n", - "points_per_group = 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load FLORIS" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the FLORIS models for GCH and EMG\n", - "file_path = Path.cwd()\n", - "\n", - "# Load the model to tune to (fi_benchmark)\n", - "fm_path = file_path / f\"../floris_input_artificial/{model_to_tune_to}.yaml\"\n", - "fm_benchmark = FlorisModel(fm_path)\n", - "\n", - "# Load the emgauss model\n", - "fm_path = file_path / \"../floris_input_artificial/emgauss.yaml\"\n", - "fm_emg = FlorisModel(fm_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generate data to tune to" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0, 0, 0, 882, 882, 882, 1764, 1764, 1764]\n", - "[0, 1260, 2520, 0, 1260, 2520, 0, 1260, 2520]\n" - ] - } - ], - "source": [ - "# Define a layout of num_turbines turbines arranged in a grid\n", - "num_turbines = n_row_x * n_row_y\n", - "D = 126\n", - "\n", - "layout_x = []\n", - "layout_y = []\n", - "\n", - "for i in range(n_row_x):\n", - " for j in range(n_row_y):\n", - " layout_x.append(i * D * D_between_turbines_x)\n", - " layout_y.append(j * D * D_between_turbines_y)\n", - "\n", - "\n", - "print(layout_x)\n", - "print(layout_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Identify the indexes of the reference turbine, the control turbine,\n", - "# the single_wake turbine and the deep_wake turbine\n", - "ref_idx = 0\n", - "control_idx = int(np.floor(n_row_y / 2))\n", - "single_wake_idx = int(np.floor(n_row_y / 2) + n_row_y)\n", - "deep_wake_idx = int(num_turbines - (np.floor(n_row_y / 2)) - 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set the layout and show the locations of special turbines\n", - "fm_benchmark.set(layout_x=layout_x, layout_y=layout_y)\n", - "fm_emg.set(layout_x=layout_x, layout_y=layout_y)\n", - "\n", - "fig, ax = plt.subplots(figsize=(9, 8))\n", - "layoutviz.plot_turbine_points(fm_benchmark, ax=ax)\n", - "layoutviz.plot_turbine_labels(fm_benchmark, ax=ax)\n", - "layoutviz.plot_waking_directions(fm_benchmark, limit_dist_D=D_between_turbines_y * 1.2, ax=ax)\n", - "ax.grid()\n", - "ax.set_xlabel(\"x coordinate [m]\")\n", - "ax.set_ylabel(\"y coordinate [m]\")\n", - "\n", - "# Show the special turbine locations\n", - "ax.scatter(\n", - " layout_x[ref_idx], layout_y[ref_idx], color=\"r\", marker=\"o\", s=100, label=\"Reference Turbine\"\n", - ")\n", - "ax.scatter(\n", - " layout_x[control_idx],\n", - " layout_y[control_idx],\n", - " color=\"g\",\n", - " marker=\"o\",\n", - " s=100,\n", - " label=\"Control Turbine\",\n", - ")\n", - "ax.scatter(\n", - " layout_x[single_wake_idx],\n", - " layout_y[single_wake_idx],\n", - " color=\"b\",\n", - " marker=\"o\",\n", - " s=100,\n", - " label=\"Single Wake Turbine\",\n", - ")\n", - "ax.scatter(\n", - " layout_x[deep_wake_idx],\n", - " layout_y[deep_wake_idx],\n", - " color=\"m\",\n", - " marker=\"o\",\n", - " s=100,\n", - " label=\"Deep Wake Turbine\",\n", - ")\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate a time history of wind speed conditions to generate around a sector of west winds" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Num Points 10000\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a time history of points where the wind speed and wind\n", - "# direction step different combinations\n", - "ws_points = np.arange(5.0, 10.0, 1.0)\n", - "wd_points = np.arange(\n", - " 250.0,\n", - " 290.0,\n", - " 2,\n", - ")\n", - "num_points_per_combination = 10 * points_per_group # How many \"seconds\" per combination\n", - "\n", - "# I know this is dumb but will come back, can't quite work out the numpy version\n", - "wd_array = []\n", - "ws_array = []\n", - "for wd in wd_points:\n", - " for ws in ws_points:\n", - " for i in range(num_points_per_combination):\n", - " ws_array.append(ws)\n", - " wd_array.append(wd)\n", - "t = np.arange(len(ws_array))\n", - "\n", - "print(f\"Num Points {len(t)}\")\n", - "\n", - "fig, axarr = plt.subplots(2, 1, sharex=True)\n", - "axarr[0].plot(t, ws_array, label=\"Wind Speed\")\n", - "axarr[0].set_ylabel(\"m/s\")\n", - "axarr[0].legend()\n", - "axarr[0].grid(True)\n", - "axarr[1].plot(t, wd_array, label=\"Wind Direction\")\n", - "axarr[1].set_ylabel(\"deg\")\n", - "axarr[1].legend()\n", - "axarr[1].grid(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total_number_of_points = len(ws_array)\n", - "total_number_of_points" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulate benchmark FLORIS and save power in kW" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute the power of the second turbine for two cases\n", - "# Baseline: The front turbine is aligned to the wind\n", - "# WakeSteering: The front turbine is yawed 25 deg\n", - "fm_benchmark.set(\n", - " wind_speeds=ws_array,\n", - " wind_directions=wd_array,\n", - " turbulence_intensities=0.06 * np.ones_like(ws_array),\n", - ")\n", - "fm_benchmark.run()\n", - "power_baseline = fm_benchmark.get_turbine_powers().squeeze() / 1000.0" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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n512xG2PD1JtSOnuoFFVK54bRKKXSK3Vv2C45IUVoGPVTOvXmYlrFmoZr/YtK2O6jXA81eUqnqZW+bwDuxc8s09TNAXJ2JMLzPKosVtH/qi02Scd7lMHZUMPZ3OO4XnrpqFGjkJGRIfydkZGBkSNHYsSIEcL26upq7N+bLTg7nqiqqsLbb7+Nr776Cps2bcKZM2fw3HPPCfvfeecdLF++HF988QW2bNmC4uJirFq1ypsFkZiYiO3bt+P06dNebb1+/XocOnQImZmZ+Pbbb7Fy5UrMf/VVAI6OtHDhQnz55ZeY/9Z7WLl+Gx6b9iTuu+8+bNy4EQBQWlqK6667Dv369cOuXbuwevVqXLhwAXfeeadwjVmzZmHjxo34+eef8fWPv2DX9i3Yv29vA419w2saS6mQsU9SGpHd2AR9Psr1FslgNXpFqbQNg2wCs8JZT2tiCZdUyM6son6e04U+9hXGxeuA8ulTbxNO+j7C0HM/bM6pdV+gNJZEqjkbus9dE5Brb59zHaJFHjtq1CjMmDEDVqsV1dXV2LNnD0aMGAGO47B06VIAQNb27bCYzRg8dHijcpzHO9Nc06dPx6u1DgcAvPvuu5gzZw5uv92xNOTSpUuxZk2dfTw1+Hnz5uH2229Hhw4dcOWVV2LIkCGYMGEC/va3v7nMR6TX6/HFF18gPDwcPXr0wKuvvopZs2bhnidmwWaz4vXXX8e6deuQlNobphoOg/p0w75dWfjkk08wYsQIfPjhh+jXrx9ef/11QeYXX3yBlJQUHD16FMnJyfj888/xn//8B6NHj8bZkirMX7wE4wf3EGllcbAMc7Odh8NVtlKwHC7vzzSW0gXKSs8NxHKmalYPNFazSQPuaRuli9cBQMUofMAyraf0q1JzLVAmZydEGTlyJCorK7Fz506UlJTgyiuvRHx8PEaMGIEpU6agpqYGmzdtRNt2HZDcNsXtfGc/CA8PFxwdAEhKSkJhoWM9mbKyMuTn52Pw4MHCfq1Wi4EDB6LS7Lnuh6+VsW3bNuTk5GDTpk3YunUrJk2ahH//+99YvXq14PD06dMH4eF1K+QOGTIEFRUVKDh/FubqKlRVVWHs2LHgeYdclQrgLBb069cPALBv3z5kZGR4XNrhxIkTqK6uhsVicdE/OjYWnTpfKcrGYvH0Ruq8D/he0Onlzc/nt3dXAcI1FHrzq38zVC6l4CpRKTuznBtIak2eWDyNtqmzs1JRkoZ2Vj76oljbaCBPKTynqZVJRdrtDdKF9Qyj9BIoSunMMtrsC+TsSMSo0+Dgq+NFHWu321FuKkdkVKRPMygfL6xADWeDUSd+AcrOnTujbdu2yMjIQElJCUaMGAEASE5ORkpKCrZu3YpNGzMxaFjjUR3AsfBlfVQqlcQbWuNNvmfPnujZsyeeeOIJPP7447j22muxceNGjBo1qkmpVZWVAIDff/8dvDHWsVxElGNpCoPBMYtyRUUFbrrpJrzxxhtu5yclJeH48eOuG33OR3jGdeg5G9ksbix2Dw8eJWgs1aQErNeZYmNnVtEMdlESOyN7sFxKxM4opOji7DCbd4jdAASldWZ13/AVcnYkolKpEK4XZza73Q6rXoNwvdYnZ8fp5KglxuZHjRqFzMxMlJSUYNasWcL24cOH448//kD2rp145e5JshtldHQ0kpKSkJWVheHDHU6T1WpFdnY2uvbsI0lW9+6OFcwra50YwBGZqa6uFpbp2L59OyIiIpCY3BYt4+JgMBhw5swZXHvDQJhqOCTHGNEqom65iP79++PHH39Ehw4doNW6f2edOnWCTqdDVlYW2rVrBwAwlZbixIljGH3dSEn6e4XpaA3XIlRFxbMavWJ31VlJmI0ggwe5Chehsl4eQUnY2ZlNGhJwH52mXMF9XQcPplGAdW1aYbmMnChfIWcnGBHZhkaNGoVp06aB4zghsgMAI0aMwPTp02GxWHDV0MaLk8Xw9NNPY9GiRUhNTUXXrl2xePFilJaWej1n6tSpSE5OxnXXXYe2bdsiPz8f8+fPR3x8PIYMGSIcZ7FY8NBDD+Gll17CqVOnMG/ePDw+9Qmo1Wq0iIjCc889h2eeeQZzyqrRte9VKIIZB/fsRFRUFCZNmoRp06bhs88+wz333IPnn38ecXFxOH78OL777jv8+9//RkREBB566CHMmjULLVu2hN0QiQWvzBMcU6VHmNSXqVjI2MtblHKjeWqvoZhcV3n1//A1DdJAXL1rKJNe8dQofE7dNCJasdRbvW2KpfUayFO+PddLvTlHpymls9tSIj7KdUljsUqfOuXX2+eb6HrRSoV1pjQWIRe5bW/UqFGorq5G165d0bp1a2H7iBEjUF5ejtQrr0R860SfdHv22WeRn5+PSZMmQa1W48EHH8Rtt92G84WXAHhu8GPGjMEXX3yBJUuW4NKlS2jVqhWGDBmC9evXo2XLlsJxo0ePRmpqKoYPHw6z2Yx77rkHL82dh9OljuHnr732GuLj4/H+e2/jzOlTiImOwYAB/fGPf/wDgCNl9+eff+KFF17AuHHjYDab0b59e1x//fWCQ/PWW28J6a4WERG475FpsFRX+GSThvizQDkYZLN882s4S67ychUVC4BdIbFfJpxUWK5/CpSVletSoMwo5cuyf7NKrVOBMqEA4hpRhw4dPL51tm/fHjzPo7yGQ+7FurTRyJEjcexCuTCp4OTJkzF58mSXc2+99VYXmVqtFu+++y7effddl+NOFlWgwmz1qOodd9yBO+64Q9RneOWVV/DKK68If1dbbAAczo5KpcLTTz+NWyc+jDIPaSwASE1NxcqVKxuVHxERga+++gpfffUV8oqrUFJlwZwXnkd8ZBgsVpsoHZuC6TwcflwbS+nRKyxGNnmbd8gnubU/PRX7+iy7QZ2K0mkslyiJwvMOKZ168xTlCpa24SJbGdHM+iDQeOTWd8HsHFZfoHl2ggi3MDcjuUGBJ6UVSoGwGhUDeIhmMJw7xNf0it3Tk0dBPH6FPsq0N/KZWc4dopjODZuGj3K9OcLMdFbMzu5aK6Wzex/0TS5vr/vdPUWmVP/2YA+F+ndD2b4vceGU65MYxQl6Z8dms+Hll19Gx44dYTQa0alTJ7z22muK5f6bBYw+ij8sxKA0kpFcdvZguVYMqynf68MuJcSqdbBfkDEYZDPVGWxks1zigt2kguwLlJl0b0a29uawBpKgT2O98cYbWLJkCVasWIEePXpg165dmDJlCqKjo/HUU08FWj1FYDb80MPTXamCzsblimP58uXshPsbT4WiSoluEH2pf3/x5Rv0tLihYssjeIgYKS27YeGsz3I9OKzKLxfh/MnGFg1/V0S2MuLq5Nb+dC1QVli2M13YYLtsuR4it8x0VtCBqIv8sU9FNgeC3tnZunUrbrnlFtx4440AHHUq3377LXbs2BFgzRjAKp3A4i1K8g4f5SpBAAr2fA/NNy5bCbmeZLPUWbmJ0hTW2cvihqx09pVG0zaAAikQz0XVSq3y7XkpEXaylZDrUbaPbcOrPXwTXa8fKiuYCpQZMXToUHz66ac4evQorrzySuzbtw9btmzB4sWLGz3HbDbDbDYLf5tMJgCOpRE4jnM5luM48DwPu90Ou90OKTg7p/N8seSX1cBsrTues9mdAh1yhM4lTW41Z0NRuUXoQLbaVskDghxBMg9Jsi+Um2uLhx3UcHW/2+12l5ugFLlmqx0XTGZB5/o3FkHn2k31P4cYLlZaUFFTN9NzDeeU57Br/XurU65jOw+O46DReJ7k8XxpNd5ccwymGk74DIDDiXK2L6c8m83u1ua88UP2OfyRUyD8faLIUWDO2x1yuHrtxspx4ETOQ1lSZcHraUdwqdJR/F3/fme1WsFxKthsdd+pFJ3XHLiAH7LPCd9dXkm14xp8/c/O17uWONk1nA2v/X4Y+WU1wjanzW21cqy1Otvt0uy89cQlLNt6WugjF0y194t6Ojv7t80mXme7ncfrq4/gZFHdwIAzxVUucqy2ujYpRee/zpZhycaTQnsrqao9t7a9uupskyT7/Q3HsTevTPh7/zlTrRyHPazWWp15aTqfKKrEv9YdQ1XtvaPS4t7G6vc9KbKXbzuNTUfrFv09cqHCRQ5nlWfnAlMNFq0+ClO14xzh/gwIdhD6t0SdV+05j1//qlto2TmAxLWv1F6L48Bx4ipRTNUc5qcdxsWKuoWUnW3b2edsNnl2Xn+oEN/uPCv073Ol7v274U8lESsz6J2d2bNnw2QyoWvXrtBoNLDZbFiwYAEmTpzY6DkLFy50GeHjZO3atS7LEwCO0UaJiYmoqKiApd6K21IoLy8XfazZBlyq9ryvuqoS1hqgqraP2qw2wVETw8V657pgq5PjfJ5VVVU5Bz01idUOFFV53ldT7ZBjrpVrt9sl6VxiBso9tGUVXyfHec+qqamByVbjfrAHeB4oqPS8jzM75FgFH5MXrmWxWFBdXY1NmzbV3eAbsO6cCr+fcfcyIjR2pKWlAQBOn1YDUOP48eNIsxwVpTMALNipQaXV/Y2p6MxxpKUdq9XZ0a3XrE1HuMgevqNQhZ9OuOts0PDYtCEdWjVw9JwKgOOY9PR00Tov2qtBfrW7zqYLZ5GWdgYAwHEaACps2rQRR4zi5B4qVeH7Q+46q8Fj9/ZNOKYD9hc6dC4sLBRsL4aPD6pxpMz9QWIuLRLkVFY4dN6+PQuXDol7HT5XCaz4y/OXcnhPFkqPAAdLHDrzkGbn/xxTY+dFDw+/mjJB50uXHO1u79690JzdI0quyQJ8kO1Z59yDe5F2bg/OVgKAFjU1NZLs/PNpNTacd9fZYDcLcs6fd+h88OBBpJUeECXXzgMLt2tg9xAOP3/iENJMB2GyOHQGpNk547wKv592b3dROl7QOfeUQ+eTJ08iLe2427GNsWCXBmWcu84Xz55EWtqJ2r8cOq9bvx6ROrdDPZJ9UYVVx9x11qt5bMlcD4MGOHze0e7OnTuHtLQ80Tq//ZcGeZXuOlcUnUda2lmXbVLsLJaqqkYePg0Iemfn+++/x9dff41vvvkGPXr0wN69ezFjxgwkJydj0qRJHs+ZM2cOZs6cKfxtMpmQkpKCcePGISoqyuVYq9WK3Nxc6PV6t31NwfM8ysvLERkZKTrXWmWxAdWV0KrVSIyuG0at16gRrnc0VlWNFRdrqqDRahAV1UK0PqXWasDKITZcjxaGuoYfadBCUxvLvGSpgtlmRXh4OKLCxDUPi80OVFVApVKhTUyYsF2nVgvXqeZsuFBdCbVajago97WqGqOirAbgLIgK0yHKWKdPhEELba3OZbWfKywsDFEt9KLk2nkAlQ4Hpk2MUUhdadUqRBgc1+FsPFBVDqhUiIqKBOBoK0ajEdddd53HWZkB4ETGCeDMCVzdMRa392sjbO/fPgbt4xzO9K7fDwMFZ9CpcydMGJMq2h7/2L0esNrw/PhUtGphEGwx4spW0GvVsFjteDZrHQBg3NixiDKKuxuadp4FThxE96RITB7SXtjeMzkKqa0d39eZjSfx25nj4Hlg7NixbkuJNMa/jm4BqqswdURHdGzpaK9hOjVGXNlKmI183t4MVFk5XHvtcHROENc+DIcKgUN7kRJrxJOj6tZv65zQAr3aRAMAqnafw7cnDiA+IQETJvQXJRcA/pO/EygrwX2DU9C7VpZWo8KI1FaCTT84/idQXYnBgwfj6iviRMn962wZ8FcWYow6/OOGLsL2trFGXNUhFgDQ4mgRPjnscESk2HnNf/cBFy/glj5JGNbJMV+VWgVc07klWtZOyfDfC7twtKwYffr0xYQ+SaLk5pfVANmboFGrsPDWukVy4yMNGHpFHNRqFQ7mm/DWX9thCAvDhAkjRMkFgOzfDwPnz+C6LvG4vodjLjCVChjcMQ5J0Y57yfof9iP7Yj66deuGCcM6iJJrtdnxzHZHP/jn/3UV2llMuA7Xdm4JrUaNonIzXs7eCECanfM25QKnj2Fg+xjcOaCtsL1fu2h0qG3ff60+AuSfRseOHTHh+i6NiXLjn/syAI7Ds2M6o3WU4/O3MGgw4sp4GLQOp/DpbWsBOOYhazjVRmNU7z4HHDuArq0j8GA9G3ZLikTXRMe9Lf/PU/j59FEkt2mDCRN6idb5oxNbgcoKPHptB3SOd/Rdg9bRv1s476Uch/T0dEl2FovYl+egd3ZmzZqF2bNn4+677wYA9OrVC6dPn8bChQsbdXYMBoOwflJ9dDqd2xeh1WrRokULXLx4EXq9XtKyD3a7HRaLBWazWfR5FosVvNUClUaDcE39B7cNltoQr8XCgbdaYFNpYLGIbzg2zgzeaoUWaoRr6vSxWTnYXI6xgTOrYVGLcxwsnM2hs0qFcE19u9bX2fG57Ha1pAiZ1WIGb+WghQrh9dJGdisnBJ6stZ/LalFBrDlsdh681SHBqDbUW4qDF/SzWO2OY1QqWCwW2O12XLx4ES1atEBYWFijDqyqdunj1NZRuHNQe4/HaGrbg0atkdT5nam1/+vdFu1ahrsfoK4LqWs9tOfGUNXqkxIX3qjO6nr299RXGtW59ueY7okY0L4Rp0DllKsVr3OtPvGRhsbtXHuMWqWSdZMd1jkeN/Ty7BQ424xGK/47dNowIkzbqM71nWgpdnZ67AM6xDUqW1Wrs1aSzo7QqlatEq2zWJx9qEeb6MbbndPOGvE686q6fnDbgHaI9uD063T2er9L7yud4iObbHdSdAbq6l1u6N2mSadfqxWvs1rt0Cc5tvH+rVE7lyVSS7sn1f4c1TURQzq19HqspPYsErHygt7ZqaqqcnMkNBqN5PqaxlCpVEhKSkJubi5Onz4t6Vye54W1ncRGdsxWR12NTqMCKsI8HlPN2XCpwgK9Vg27SZxnDwCXKsyo5uzgSnUoNXj+6ovKzTBb7bCV6WHUiyv4sNrsKDSZoVYB2irPOQiL1Y7CcjO0ahVUlZ4/lydKqiyoNNtQY9SiPMxzo3Z+LotRi9JGjmmInedRWOpIeemqPDsuNjuPwrIaqFSArvZzqdVqtGvXzuv3WTeiRJQqkpA0fFZCoWHDUR+ekPt56mqfvMj2Qa6370LuV8BumDIbuYC4EVJyRnqJsYXcEWRiRu7IGYHkskRLE6fzEnUXMzO1/HYnQrZKelG8mMlHZfdvH8/3F0Hv7Nx0001YsGAB2rVrhx49emDPnj1YvHgxHnzwQcWuodfrkZqaKrlmh+M4bNq0CcOHDxftfe45U4J//rIP7ePC8cWUbh6P+fP4RfwzIwddE6Pw0cSuovX5bNV+ZJ28hOfGdcENXT2/rb733R7sP1eGuTd1x4iOCaLknr5UiX/+vBMRBi1+nn6Nx2MO55vwz192IyEyDN8+6vlzeeLHNUfwR04hHrymIyZ28/xG8uUvOdh07CKmjrgCf+vWTpTc8hoOj676EwDwx9PDode6R94KTTV47Kft0KnVWP2MY6FTMdE9KaNrpI5eYb2qt9JLFgDSRsFIuYlLmf5f6gATbyOZfBHOcl4kKTpLs7Pjp7j2LA3WOgPsRiqK01maRaTZWrxsljqznDdLSYLe2fnggw/w8ssv44knnkBhYSGSk5Px2GOPYe7cuYpeR61WIyxMfEQCcESYrFYrwsLCxIdfNTqcK7ehRTjf6PXsascx8TGNH+OJkhoe58pt4FTaRs8rNgPnym2wqXSiZWt0HM6V2xBja9xGKm0NzpXbAI1dks4mzqFPjV3T6HllFscxZr7xYxpitmsc+gAwGsOg07g/5bW19tJrpNmZ5XyWdW9RjaTQ5MoVogLK37BYrYUlJhrlu+zG8SlK4k2uTDuxnjSPxaOMmc71+mBjon2NVLKcskPx+YsY6sxqyg6lCXpnJzIy0uPaTMGKmIeO7Ada7U9Rbw0y3qKYhEgZhdA9rUTuJlemzmKiL7JvDI3NjeHxUPFfIi/iS/TVqVA8VSHiySDXcWC2CGrtT6UjGS6yFU5FikoX+poCUTrF6TKrsdLfoQjnT/b9TlyKTOr7FEuHVbjfMZCtJEG/XESoIcUDlx4yrpUt8TyxcsU5UVJDpGxkS1mJXGqqiZVTCdS/sbAJzbNMVSgtm1V6rP4JSvdDu51NGhKQmtaT0lck2EKqg8ZM57rf/TlJaEOk92/xsqU0PJb9W4oDH0jI2WlmSKoVkAirwjqmaz8xeiPxtBK5UrB802mqGFB2CsR5vqyzm5DNutiXgdZiFjOUFSVxnuvlmGZbVK2sWIdsZmmsph/Zsi/JcL0qX2egblwwO52Zrt+lIOTsNDPqogKNH+NrSkjx4jcvU+k7kT1aQ4zOMkR7XYlcEOtbbF7t5UuUK5vVW7aYN2ymaT0ZcsUUVcvPFrJ56WBZzMnqRanOzsqnsUTp7GP/ZhUV9iZXfv+ule3V1tJli9LZx1qx5l6gTM5OM0NM9KXewdJkM4rASIm+SM8msEknSLGF/NoJ8ceKls2okJhVETEgLQIjr1aMQWheQspXUvtoJsWtStfkuWshUbbS/ZBhGktSLZx04QCUv3eIqcmrO1aCYEhtH4GDnJ1mRqBHxTh3yboZMryBsxLMpGCPUXFU/bfPxl786m+W5fxJV6tp2czSKyxD846fzAqURRQRy3bQAlhULVu20s67iALl+p+n+dzvWL2QOmDTvymNRcjA2diZphPknd4oYsKY8nUWIZuVXN+yWIrLdhlOG4C5Q+TXkjSdupHzcUSlT2WnfNncwFnWN4gp2JaXAhEh18e0rNKyXdLUks8WJzvk7ncy5ALSBmQEEnJ2mhnCxM8squYZ5ZolpbEkh0gZ1ahISWOJFwtAmlMpTec6Go3s+FjPxcIeTRVVux4rPUUgzs7y0itiahykDfF3PdejXF+H+CtezyVIVlSu44RayUqnqUUMQPC5nkuUHhJls7p3MCoHAOrud80dcnaaGXVeMgPZjG4sUmbnlAqr4rfg1LneDVzUg0dK0WXTb9hyYSWbpc6s0nqShsvLls2qqFpRsQ1ks+nfgLg+Ls2Rcvz0VkQsF1b3JZZFxFSgTMhCTFTA+aCTXjjL5qYl7a1B4hu2iAiMvJSQGDu7HitZttfQfO2xMqICAKBi1HNFTUgns4BRjLXlFfuySycovowBnHKl66SEHnK+QlETe8qtMxJxjKw6Qkb3OoceEu4dMqsOxdlaShRUhM6CnaXpLGaahuZAQJ2dvLw8nD17Vvh7x44dmDFjBj799NMAahVYmI6KEfHQ8WnuEJbRKFZymUQFamUrLrd+ZMcz8oePsqslYSWbbVG1eIdVmmDnuSzqjJzns3nqBNNSIiwdYZ5RB3dJvSkrmpnOtdIdosnZaZx7770XGRkZAICCggKMHTsWO3bswIsvvohXX301kKoFEBFvJLLfVqXUv0iJOLApQAXEzcPhyxpFSs/fU1+20jdaqQXKSqciWc6VJKtAmaXOwvnKIiklJDEoIK5AWZpM0XKli2UqW+qASMXn2fGhPTcp24eCbSYFypTGapqcnBwMGjQIAPD999+jZ8+e2Lp1K77++mssX748kKoFjLpOKuZhJjUl5EDx4bQSbiyy53BQ2kFjODeQpBE3sgpFWaQiHT+ZFihL0EOUXEltQ7zc+seLqcuQkxJi8RpcZ2dl67mkRF8kp3xrf4rrKtL7N5MaFUYFylJndZc1IEPUsdKgtbFEwHEcDAYDAGDdunW4+eabAQBdu3ZFfn5+IFULGCzDjWLedli9YctFivMnS24QzR0itUBZjmwWNyxWw62ZLsshpi7DhygJg8At8+HyLKjTmU3/9mZMXyOVytu57nd2k4YqKtYhG+xkK0lAnZ0ePXpg6dKl2Lx5M9LT03H99dcDAM6fP4+WLVsGUrWAwTJk7Gzx3qbXlydWTH2D7Fs4AOVD8yx1Frf0gozUm8v5YvSQLpvJvEOMQugsdWY2Kqb2J4vRWHV2bvwYn+zsbVkOn9uGsrKlLDAKSI3OiUjb+zCFB9DEAASf7OGtr8j7EusWt23e3k5AnZ033ngDn3zyCUaOHIl77rkHffr0AQD88ssvQnrrckNaKJqdbHlyxR8rWjaj9Aqr1Er94wMxd4hsGM7DISf9IO5gKTpLTa+wqm9DrVx2EUUxrVpO6o3lshyi5hWTk+JkUVQNp2yF5bpEdsTrIUW20qn1+oc3b1cH0AbiolVVVQgPD8fIkSNx8eJFmEwmxMbGCvsfffRRhIeHB0K1gCNmcUO5iFuQUU7xG7v8eCAXN5QLK51FFzCqpDtorJZHcJGtsK2Z6mxvWjaziIPsaJS0aIZYpEZJpMBOZzQt10c7s5oLB6B5xVgQEGenVatWuO6663DzzTfjlltuQevWrV32d+jQIRBqBQxbvaeY83fvQ1PFNSqe510ekGKm16871/t+u73uXblObuPHi+0HbjqLqTMSJ9pFZ6tgZy9yJfTd+t+hmHkn5Ohc/xpKFHS66qxcXUbD7xBivkORBvHUV5R4pXTT2amX76Jddeab7t9iabSvKJCq8NTuvJ8r854kIgIj1lau/dtxUxJ/r/S+X3L/Fvn1etK5SdniRDfS7rzIlXmPDpa1sQLi7Bw+fBg///wzvv/+ezz11FPo06cPbr75Ztx8883o1atXIFQKGJ9tOolFqw+7NEzA95qMaosNN76/GScvVrrt87VN/ph9FnNW7ofFZnfZ7mvqzWbncceSrdibV+q2z1cHLeNIIZ74z25UczbJcpvi4RU7se5Qodt2X0fF7M0rxf2fZ6G8xuou20e956z8C9/uyPMg1yexOHWxEncs2YpLlRZZsr19h++sPYIPM467HeNrKrKk0oIb39+M82U1bvt8Test/zMXr/1+yK1/+9oJazgbbvpgC44VVrjtEyXai9K/7DuP537YB4u1Yf8WIdaLXLudx92fbseOU8Vu+3xtd1uOXcSjX+1ClaVB//ZNLADgia+zkba/wG27r/0751wZJv47C2XVnOKy5/6cgy+3nXaX62PK92xJFW79aCsuVpjdZTfzRFZAanbatWuHJ598EuvWrcOFCxcwY8YM7N+/H9deey2uuOIKzJgxAxs2bIDNZmtaWJCTcaTQ/UYIYHDHOJ/kniiq8OjoRBt16JoY2eh5Ymbn3HSsyM3RAYCrr/BN50uVZo+OTgu9Br3aRPske+vxi26ODuC7zjzPe3R09Fo1+qbE+CR7R+4lj47OVR1ixRWhennweNJZq1ZhYHvf7LE3r9Sjo9MpvgVaRRh8kr3+UKG7o6Pyva8czDd5dHQSIg3o2KpFo+eJublnHCny2L+vvqLxARjCDOle5OYVV3l0dKLCtOiWHNWkXt7YfLTIzdEBgMFedBaDqYbz6OgYdQr07xMX3RwdwLvO9R/83mydfvCC2za9Ro3+7WMkaOjOrlPFHh2dPikxCNP59mhe76F/a9QqXNXBt76yL6/Mo6PTvmU4Wkf71r9ZE5DITn2io6Nxzz334J577gHHccjMzMQvv/yCKVOmoLy8HB988AEmTpwYaDWZ4UwfLLq9F8b3SAQAaDQqRIXpGj1HyhtWQqQBa2YMF7aHGzQwaDWy9QXqwrizxnfBvYPaAXDka6PDvegs4a1erQKyXxorbDfqNQjTNa6zGNlOnScP7YCnR6cK22O86dy0WJeH74ZnRyA2XA8ACNNpYNR7sbMEnf+vdxJeu6WnsD3aqPM5suN8K/zh8SHoHB8BwOGgtTA0fksQc01ne776ijgsmThA2B5l1EHjxUMT4zg4ZS+Z2F9wFrQaFSK99RUJOqcmROD7x4YI2yPCtNBpfHvoOGW/dksP/F/vZACO2qVoY+M6i5Pr+BnXQo/1M0cI25vq32JajVP2U6NTMWVoBwDK9O/6Pl/2S2OEGg8l+/fEwe3w3LguwnZv/VssTtlrZgxHQqTjgW7QqRGu99JXJMgd36M1Ft3eW9jeVP8WZw+H8K8fHozuSQ7nt8n+LUpnh9yB7WPx2QMDhe2RYVpofewrrAm4s1NTU4O//voLhYWFsNfmLMeOHYuxY8ciJSUFVqv7m20o4XxYRoRpEdtCL+1cr/sce7VqlWS5TV63VulwvUaG7Ma1rl/oJkdnb9Eop2yjLJ0dn9nTDaj+FWPD9dK/Qy9fonNfmE6azioRFcrO3dFGHROd9Vp5dhZDZBgbnbUatUQ7OwU0fayU/i0l1cCkr9TuayGjr3hLrdTfF9dCL9lh9yq7VmejhL4i9urO68aGy2h3IvYZZPYVMW1a8f5d+1Mnsa80BwLq7KxevRoPPPAALl686LZPpVJdFmksVpXsvlbfi+lI7FZXZjdCitXK2w7ZrOwh73xvN9pgXIk8uHVWuG0Io8UUFQuA/T0JYDeSSfaIPy83PHYrkbNvd0rfSlmOFmZNQFV+8skn8fe//x35+fmw2+0u/y4HRweoN5+HhHOkzP3BZsZM6bIlzdEhVWcJikgpopNcFK2wPeQcK5Y6taXoIV4ui6+QlyFcks4MHQfF5cpcfJHVvUPK6ECmOksTrbgeUg6WO+eSlHuppHuHFJ2beTGyJwLq7Fy4cAEzZ850G3p+OeHLNN7ew7ry5Ep5wMtp7t4jRj5GMkTI9nWmV7ft9d0GOd+h12vKfDiIOKZu1lNpspvEx6iAmBC6r7PTNiZYqty6LFbT6VMpDzQptWK+LnngcZ8siSLO9eG+AYiLNkt7tksb8aT4/Y7hauG+PVe87GOoM2sC6uz87W9/Q2ZmZiBVCDjMJ81j8NBhPakWi7cGdqHout+DKhVZ+5NV6o3FzTC401jSzxWXSpahVBOwmqzRLxNXyu0rTcj1RXZjsFr3D/D9/t8YUuZqa24EtGbnww8/xN///nds3rwZvXr1gk7nWjn/1FNPBUgz/yEn7C9lPhsWTVLOW4OUNAWLSIasdKEYufXrEKTIZmgPMchpH9LmnJEXJREjXFKURFJ6jF26kNnCrZJ1Fh/NkNa/xaRW5DrCEmRLFd2U3PqRHaXTenLvdxL6ofKpSPHHNjcC6ux8++23WLt2LcLCwpCZmenSYVQq1WXh7DS3tyhRKRAfdBZXNMsiGgXfZDcq17cCZTERNKlv71JqHNh9h5LF1sr2VijKJkoiW26t7ZROzYpzHFxUkIz39Knjp6y+IqIPyo5UetsnQ2cp9zpAnt6s2jPAqE2Drc6BJKDOzosvvohXXnkFs2fPhjoYy7uVgHHVvFwX3HventFbVO3P5hKNEiW33u+sZMuuy/A6vLhWtuLtjo1cgKHOTrnKinXIZtXumtkABFFyGRYRN9eaPDGwSWPVSVdWcK3UIExjBdTDsFgsuOuuuy5fRwcyw41SwphSFRKBrDSWKLkyC3Il5ECUTzXJK+cUpzL7+hcpSAlzS44oShr5JuXtXULKl2EaS+meKLfeQ1L6VIpcEcf4JW0jTXTTcpmmqWujJBIff6zupVLmdwo+VyfAzs6kSZPw3//+N5AqBBxfin3FhTGVvxn6prO3EKlTBwYFjM55SWTGXxvT29cCRu8hY3lypTzglV6JnNUbNuBraN7bNWWmC4XzGz9Gjs5SnHeWKRB5aZumr9lcBk1IqucCizS1oIlkuYC4UYCsdA7GyE5A01g2mw1vvvkm1qxZg969e7sVKC9evDhAmvkPZoXEPobPRQ1rlyfa21UBMJoozacBtV4FCyifXvFNZzGjeVi9CTOJkjCS7Wu60KtsH3T2XhslX26T12WW8mWZxnL8lF/D5NnaLNNYLEfUsY50BaGvE1hnZ//+/ejXrx8AICcnx2VfMHqOcpDz9i68UTKICohBls61h7K4gQsLJ4qIGkmzc92xjUmWW6As2EPEWxSL77D+cgNicX4v3r9D3x5oSkdgVHWdpVF8HS7PImrU5DXlpoSc5yt876hrzywilbWyA3C/kxu5FbOYq9x2J/RDhSP7UnSmAmWJZGRkBPLyzQJWhYZyowIing3sdGb5psPsbbWOZvMWJcmxlKqVd3xNVXiVXftT6QhMcx0CzNKp9A4b2b4OmmhCumTRLi8zjRhbbk2eGOTWt4mSzfh+RzMoE7KRNq1304ewTCcIakgSLaKORGbwlVlhnYQiQ7F61IkWbw82YX8ZDwdGD3fRsmXcwEUVcwrHMnzoMHPQGPQVWXYW056dx0qDlc5icHmZUbruSqbDKq5NS3fSJOkcfL4OOTuBprnNHSLm5unLfDhedRZm55Qs1iFbxHWVLo5sjosbSnrAKzyXiq/Di72nQeTfaL0Xc9a2Z5mjYpTWWYrzHlwFyk47K9sHAYYFyva635tbsa+YyK0cWwcqtc4acnYCjNCwWIUbGRRPsIrs+noD9yqbcbqQyXwnDB0HdvO/OH4GZRqrmRUoi+mDLBeJZZaWVViui2yFX5RcCpTlifZyTYZREka2ZpuKZAs5OwGmLvcur3C2MXgZcsXCepin3AUZvWGX4aCxmpNErGw7K0eYl3cDF2dneQaRMi+J0lESf6zn1VzqucT0LTkRByntmcUq36zWmZIbuZVyT5KsM6M2LU3n4IOcnQBTV6Qm/1yP+2QW+0pI28pMvTUdbWCy6jmcsuWGjD0L9z09JsYeytZl+D43UNP7lF6WA/DN1qKG4Uu2s+N4MX60NJ3Fp5LZLL3AJrXuc+rN6z42UeH6/VOebJb3Oy+y4ZQtRzgruYGFnJ1Aw6iQuC49puzD3bGvVrTS6QT4dgP3KjsY01g+FiiLGWHCKo3FMDJ/2SwXIcapZPnIYdU2mGjtaxqrsb5S73fl53dqvinwJuUGn68TGs7OuXPncN9996Fly5YwGo3o1asXdu3aFWi1RMFqhlWW8yHImu9ExDFyh0NLSTexWihQemi+aVjVv8guqmZYvC4uVSGnTbOSKw5WKTJBZ6lF1ZLuHUq3O3l2bg46N5d7EsDuvsRS5+ZAQOfZUYKSkhIMGzYMo0aNwh9//IH4+HgcO3YMsbGxgVZNFL68rbKY5Zj1ME8x4XMmo7F8fItqKkoiO/ri9Zoyb7RNaOPrrLBi0jZK29l1n7KpN7nRVefRYtKnkmryRHnC0uW6nt90ekWWWBFpG/mDJpq+30lBmp3l4b09+ya8MdG+Rm7FpCKDz9UJAWfnjTfeQEpKCpYtWyZs69ixYwA18s7hAhPyiquFvyvNttrffGs+NZwN209eAmdzNMa9eaUOqQoMtT5ZVIETRZXC3yVVltrffNOZs9mRdbIY1ZxNuI5Dqu9d6VxpNQ6eNwl/F5TV1Mr2DZudx47cYlSYrQCAixVmh1wFen9RuVn43gDgTHGVQ7aPcnmex+4zJSiu5AA47O7EV9llVRx2nioWbrzHLpQ75PoomOd55JwzocBUI2yzWO2KyK40W5GVewlOMxw4X+aQ65tYAMDRC+U4falK+LuixtFOfNW5hrMhK7dYsMFfZ0sVkQsApy5W4lhhhfD3pQpLrWzf+/eO3GJUWRz9+/Qlxz1Eif59vrQaB+r17/zS2v7t4/BTu53HzlPFMNV+b857nRIprIsVZuw5Uyr8nXvR2b99k83zPPbklQrfm9wBCJ4oq+aw61SxECk6UlChjOAAEPTOzi+//ILx48fj73//OzZu3Ig2bdrgiSeewCOPPNLoOWazGWazWfjbZHJ0Go7jwHGcYro5ZTl/nr5Uhevf3eLxWBVvE31tm83REXm+TvYrvxzEtzvPuh2rUUHSZ7LXtmqbzaFPaRWH8e9uEpwoF51hl6wzUKfPBxkn8P6GE27HatXydLbX6szZ7Ljxvc0orXaXoQIvWrbVWqezlePAwY7vdp7Fy78c9KCzWqLOduGn87w7l25Fbr0HpRydHTjswVkd7Xn94UI8/vVez0fabBAr2m6zCb879Xn4y53YearE7Vg1pH2HTp1tVis4jsNfZ8twxydZjSkiWrZTZ56vs+GsH/YhLeeCu84S+4rzoWKzOXQ+X1qN8e9u9vhmrOLF95X67c55zsK0w1ix7YzbsVL7d53ODhuW11gx/t1NMFvtbsdK0dlT//5kUy7eTj/mdqzU/s3X9hVbbV+x2Xnc9MEWXKq0uB2rlnBP4up9Zo5zfIcr95zDCysPeNBZ5XP/vvfT7Thaz6ms01la/3b6XdZanTcfu4gHv9zt8VheQl+x1Xr/9nrf+9T/7MLWE8Vux0ptdw2fhUoiVmbQOzsnT57EkiVLMHPmTPzjH//Azp078dRTT0Gv12PSpEkez1m4cCFeeeUVt+1r165FeHi44jqmp6cDAI6VqQBooFPxaNOibn+8kcepvX8ib584ecdNAKBFRUUF0tLSAAD7jqkBqNHSwCOydj1VtQrobbgoHCOGwkKHnP379yOi8C9cqAY4mxZq8GgXUXdcjJ5H6dGdSHP3VTzLrXbozHGcoM+OE45rxeh5xOjrjh0SXS5J53NnHXKOnziONPMxVFmB0mpH024fwQsvIRE6HvazfyGt8C9RcmtsDp0B4I/Va6DXAJvPOK4VqePR0lB3bN+WZkk6H8tzyDl16jTS0nIBAGeLNQBUaNuCh7ZW6TANj4jiI0hLOyJats3mkJOZkYmWYcDmAke7C9fySAirO+7KaB4Z69aIlptTKweoa9Mn8h3XSjTyCHPsgk7NI7E6V/hcYqiqdMjZum0r8nOAfZcc1zKoeSTV65JtWvDYvy0TOSLfLP8qdsgpLi4Rvp+DpxzXSgjjEV57B9SogFRVPtLSzovWuaTEISc7ezesp3jklgM8r4VWxaNtvf7dMozH2b+2oiCnUVEunCoHnO3Oaec9RxztJc7AI6q2f6tUQF9jsaR2V5DvkJNz4ADSLuXgYg1gtmqhAo/29fp3lJ5HxYlspJ0WJ7fY7NDZZrUJ+mw/6bhWtJ5HbL3+fXVcpSSdT512yDl27DjSzEdhsQGXKh32adeCF2qAwrU81OdzkJYmztAOX8chJyMjA0YtsPGso71E6Hi0qte/e8dxknQ+UivnTF4e0mqNeOaSo720Ceehq621CtPwiC49irS0o6JlcxaHnI2bNuKwEdh6wXEto4ZHa2PdcZ2iePyZkS5a7v5Ch5wLFwqFz3r8nOf+3dZ8WvhcUnC2ZyWpqnJ/QfRE0Ds7drsdAwcOxOuvvw4A6NevH3JycrB06dJGnZ05c+Zg5syZwt8mkwkpKSkYN24coqKiFNON4zikp6dj7Nix0Ol02H6yGB8e3IUO8RFIe3KYbLk7ThXjgwO7EBHRAhMmXAMAWHlpN1B6Ec9N6Im/9W8jW/YvJXuQU1KEnr16YcLAtjheWAHs3Yooox7pL4ySLffUpUos2PsntFodJkwYDwDYvOoAUHgODw1PxeMjrpAte8uqHKDwPDp16owJo1NRVs1hzs4MAMAfz42FTiOvDr/CbMULOzYAAK6/fjzCdBocXncMOJeLWwe0x9wbu8rW+dj641hz9iTat2+PCRO6AQBm7VwHWO34+vHhSI4xNiGhceZkr4fFYsOIkSPRLi4cl7afAXIPY0SXRLx/dx/Zckt35OGH3EMAILTpNw9tAsw1+PCBq9GnbbRs2e8c2QyYqzFkyFD0bxcD9YELwNF96JUSi28fHiRbru5gIT4/shexsTGYMGEwAGDZ2Sygogyv3N4PY7olyJb91fkdOFleiv79+2N8j9bIPl0C5OxEm9gWSH/mGtly9+aV4l85O8Cjzs6/le4FigsxY3x33HNVimzZf5j2AcUX0KNHD0wY3A6ni6vw2p4tCNdrkf7CaNlyz5VW45Xdm6HWaIT+nfXrQeDCWUwa1hlPXtdJtuxdvx3C5oI8dO7cGRPGdEa1xYZZO9YDAH55ZjRaGOQ9xixWO57NWgcAGDlqJFpGhuNExgkg7wT+r28KXru5u2ydT288id/zjiMlJQUTJvQAAPxj93rAZsOKR69F+5byX6rn7t0AVFsxfPgIdIpvAdPOs/jvyYMYlpqAJRP7yZZbmX0O3544gISEBEyY0B8A8O7RLUBNFRbfOwhXdZBfB9vwWagkzsxMUwS9s5OUlITu3V0bZbdu3fDjjz82eo7BYIDBYHDbrtPpFP8i6stVaxyusUat9uk6Om3dq12dHFXtPq1PslUqh2Og0Wig0+mg0TqaiFqtUkRnFVAnpzYeq/VRZ02tM6OutavGUpdHMOj10MgcZqO3153n+A41gn20Gt++Q01tW1DXawvOFINer0w71GkdclRq53fqm85araZOdm2bdqZs9DrfvkO10BYc7U6tVqavOHVW1esrvGJ9pYHOGm2tzr71Fa227rYs2FkhnTXqBv27Vme1ysf+rbPW+93Zv2v7Sq195OLsK872a6nXLw16PXQ6TWOneoVX1aWxhL6idP9W1e/fjn16H58zqgb3TaX7t8s9CU6dfWt3Tlg8Y8XKC/qh58OGDcORI64h/qNHj6J9+/YB0qhxFF9mgXf/1de6MWc+2Kmr0vOm1DeB0ksW8A1+Agrq7bSHIFcZyfVHryi1BIAwSqhWtk9LFniA99julJWt9BwkLl1PoeHgzs9c11eU6SyevydlR8EorbMg18M1WPVBwLfvsP65De8dTPu3YsW9fL3/leyD9XR2yg7CguSGBL2z88wzz2D79u14/fXXcfz4cXzzzTf49NNPMW3atECr5oavs55643KcNK9RuX6YNI/J/EW1Py+XSfO8wWr6f6DOHkrPFRKMdmarM6N7EqvF+RzCAbC93ykvmJ3OLJ9Z/ibonZ2rrroKq1atwrfffouePXvitddew7vvvouJEycGWjU36m6yvsnx1O6Umuyp4dl1K5H7KNefOte7p/iit2edFYoKeDhfqQnoGn5mX6b/d5Hr4ZHIXGcf71Cevn+563e5C/csV+n2XF+2zw+dBqf7uhJ5I2JrZdfuYzgppi+29mxn3+UCjfXvWtkK21oxnT1sC+J1P90I+podAPi///s//N///V+g1WgSpWdTdQkZKx72511+KheK5t1+V+xeyCgFUl9mXfSFQUpI4WiGWyqSRUSx9qdyYf/an4xs4SpbGfgGvyjWBxmkqetkN0hxKiS3/k1J+fudex5LaVsrHX3hXW/SAII0FUmRHUISCnngnlAqAtMQltODy11qoSmY6mxnH5pXOkXGclVvpSIwbnIZpk+VehP2l1yWslnqzCpdaHfpK8FjD3ZLWoCJXEC5qHBzgJwdP6JUlMRz+aJCst3C3J63S5brQTOl3lYb1dlXuV519jVk3DBtU2+fwuFoxXT2kopUqqjaTS6T0LxSBcoN5CoUFWCaPm0oVyGn0nvbUFa2S4GyT3I99G9/3O+UTvkK11RWrqvs4Pd2yNnxI87oi1Kvq64pIUVFu4+KUUZsgxAp49SbkmmsBikhpd50+AY/AeVtzWrUW/3flW53doUelIJcD9uUehNmlS70pLPiIxeFiyilM7s0tVvaRknZrNL29X/3cR29xmSzsjPANirsb8jZ8SN1YV0GshmHudmMIHP8DCadWa9iDTSf1aYDKZtl+Nyu8EOnoVy2dg6etsEujVX3O7MUOAODBGPantUowEBAzo4fUeomK8yFU2+bcimyhukVZcPcrkWXSoXmVS6ylXqD8jQPhxOlRms0jAo4dvokutHzlU41AQpGBgR7NCicVWpUjMcoqELfoVvxuk9iPadAFKqbdX5m92iUj3Ib9EHA1YFXRHbt38rdN9xR+t7BIhrVsEkrXR5R/1tjWQ/kb8jZ8SPBVOzrbPBMC/aCsqiabVQAUDBFVivTWVStdBExwDCyAz9EdphFo9i1O8XnBvJDVCAYdHZLnzLSGQiuSDYUciybA+Ts+BHlcque3vxYNUp2jb25F1V7QrhpMSicFfYpfaMVddWm8agWo0LiulSF8jorVdDpJlfhOiOPshWOZrDqg4CSczC5/h2cdm58n2TZjO533uYGogJlQhJ1N1llCyNdZfsG6xsLy+nT3cLcyoh1yHSmVxSPOLimbQDls1h+KVBWRrRfiqqFKSB8ntzNc0rIZ0fYq4OmbOSWZVG14m2jYdqGwcNd+QJlZ/9WLo3lJtsPc6HR0HNCEiwjDoqnm5wpED+k3oJp3glWbzosCpTrUpHBVzjrlzQWI7lKj9QD2D10mI62UcipbAiLtE1D5y8oi6oZzpVEaSxCEqzCugCEFs+qDsHn+lOP5ytbWCdIZaizUkPPvc4donj9S61checGApQMobsKUMqp9D7fCZt0YbNOrzTS7lgU+yo2IKPB30oP4fYoW3E7KxnZcRXAdAkbYRLV4Pd2yNnxI87mrtybe10HUnpNF1YFyq6jNdjIZhHZqXvzU9aprNNZuZuh+4gbhSMOLt8hG3s4f1GqqJqFzg37iuIFyh51VqqvuKZXlOuDntLUyt7vlIpkuOjVIJKt2MO9YYoT7O53bJeDCX7I2fEj7IqI6zk+ikcF2L9FKS+coZ0Z3Vhca3bYyGZ6M2Q1woRBy2M9SohN/66VrbRcVl0QLEe9gYlcgN39TsmavMZks7zf0dBzQhK8QjdwL1ks5ZdIYJSmqCfa987fQDYzW0DJ4sjG81gsQ+i+CfawTTFbNxCr3JfohmKiG0sJsVyWQ+FRb4o5qx6LfZ27lLUH2xcwz9eUitt8ZQqmsVjZw3OammFNl58hZ8ePMAv5I3hSQvUfu8rLZldUzcweHtJYyunNzh5OlC5QZvUdelrGQOnUjfKrfLtfQ/ECZYUXtmWqsx/S1Kzud/ULlJv9PZrhcyWQkLPjR5imE1iFjJWKCniSzVhnpmkspeXW+z2owtxgI5tlrQAzncnOrrIZ2YNd+htQatCEm1Se3X2U1eAUgO3939+Qs+NHlLqxeF2tV6Fp+50oVfzmNfWmcARdWHCVSTqBzY1F0QLlBn8rNgW+h21KL4HihNUq34Dyyxg0lMtiBJlist1GvSlT+Ox11JviOjvl+k7Dj103q7vCcpVMUzf4Wyl7eE+f+ii8GaANtAKhzLvrj2PPCTW2/nwQarUaJ4sqACgX1i2ptGDOyv0AgAJTjaKyf9l3HocLypFfVq2oXKvNLuh8rLC8VrYywjOPXERp9X6UVllq5SoiFgDw6q8HodeqsftMSa1sZYTvPF2MOSv3w8zZhG1KRf7+lX4MUUYd9jh1Vsgg56tUeKm2TSsdrfz35lz8ui8fhwtMAJSz87mSaqHdlVVzisr+bkcetp8sxulLlQ65Cr1CVtsg2PlciaMfKvXQ+e2vfJwoqsQFhe8bPA/BzofzHd+hUjpvPFoEU40VJuf3p2AHX7T6KMINOuw8XQxAufa850wJ5qzcD4tVeANTrN29v/4YYlvosS+vFIByOh8rLBe+Q87m0DsU0ljk7DDkj5wCnLyoBgrPumyPNup8kus8v9Jiw7c7zrjsi/JRdlSY4/zs0yXIPl3idk25tNBroVGrYLPzDHR2NONDBeU4VFAubPdVZ7VKhQiDFhVmK37c7fod+qqzU7eTRZU4WVTptt0Xoow6lFRx+H1/vsdr+iIXAEotKvx3V509dBoVjHqNb7Jr292Gw4Uu25XqKyVVnId259vtz3n+tpOXsO3kJbdr+iqXs7vaWRHZtX1lb14p9tY+JJWQa9RroNOowNnc+7dS3+HhgnIcVrB/Aw57lFVb8fM+hftKbXs+dakKpy7V2SPCoIXGR8chyqhDYbkZqw8UuGxXSucLJrPLd6hRq9BCH/yugopnmkwMDkwmE6Kjo1FWVoaoqCjF5C7/8yR27M3BlVd2gab2dU+nVePmPslIjjH6JPv3v/KFSJGTlLhw3NI32ScPv9BUg1V7zrm+iahVuKFnIq6Ij5AtFwAyjhQi52yZy7aEKAPu6N8WWo381+Gisiq8/u06tO9UZ2eVChjVNQE9kqN90jnr5CXsyC122RbTQo+/D2iLMJ38B3yVxYr/ZZ9FWRXnsn1o55YY0D5OtlwAyDlXhowGTkMLgxZ3DGjr0w3RYrXj+52nsXX3fpc23SclBsOvjPdJ5+OFFVhzoEAomAUAg06N2/q1RXykQbZcnuexas85ITLiJLV1BK7vmSRbLgCcLanCr/vyYbXV9RWtRo2b+iShbWy4T7J/23sWaX/udrFzm1gjbuvXxqf+XVRuxk97zqGmXiRRrVZhfI9EdE7wrX9vOlokRBmctIo04G8D2kLnQ/8uq+bwY/ZZVJqtLttHdU1AzzY+9u8TRVixepuLnaPDdfjbgLYI9+EBX22x4X+7z6K00uKyffAVLTGoo2/9+8B5R/+u/+QON2hxR/82iAnXy5Zrtdnx4+6zKDSZXbb3bBuNUV0SZMsFAI7jkJaWhgkTJkCn891JrY/Y5zc5O2Dn7LD8gok6yM7+g2ztH8jO/oHs7B+ag7NDBcoEQRAEQYQ0wZ+IUwBncMtkMikql+M4VFVVwWQy0VsDQ8jO/oNs7R/Izv6B7OwfWNrZ+dxuKklFzg6A8nJH0VtKSkqANSEIgiAIQirl5eWIjm68hotqdgDY7XacP38ekZGRik74ZzKZkJKSgry8PEVrgQhXyM7+g2ztH8jO/oHs7B9Y2pnneZSXlyM5ORlqL/M+UGQHgFqtRtu2bZnJj4qKoo7kB8jO/oNs7R/Izv6B7OwfWNnZW0THCRUoEwRBEAQR0pCzQxAEQRBESEPODkMMBgPmzZsHg0H+pGhE05Cd/QfZ2j+Qnf0D2dk/NAc7U4EyQRAEQRAhDUV2CIIgCIIIacjZIQiCIAgipCFnhyAIgiCIkIacHYIgCIIgQhpydgiCIAiCCGnI2SEIgiAIIqQhZ4cgCIIgiJCGnB2CIAiCIEIacnYIgiAIgghpyNkhCIIgCCKkIWeHIAiCIIiQhpwdgiAIgiBCGnJ2CIIgCIIIacjZIQiCIAgipNEGWoHmgN1ux/nz5xEZGQmVShVodQiCIAiCEAHP8ygvL0dycjLU6sbjN+TsADh//jxSUlICrQZBEARBEDLIy8tD27ZtG91Pzg6AyMhIAA5jRUVFKSaX4zisXbsW48aNg06nU0wu4QrZ2X+Qrf0D2dk/kJ39A0s7m0wmpKSkCM/xxiBnBxBSV1FRUYo7O+Hh4YiKiqKOxBCys/8gW/sHsrN/IDv7B3/YuakSFCpQJgiCIAgipKHIDkEQBEFc5uQVV+HuT7ejsLyGiXy7XYNugypxZVIME/lNQc4OQRAEQVzm7DxVjHOl1QyvENiRzuTsiMRut8NisUg6h+M4aLVa1NTUwGazMdKMCFY763Q6aDSaQKtBEAQBi9UOABjWuSXe/nsfRWVznBUZGzagbaxRUblSIGdHBBaLBbm5ubDb7ZLO43keiYmJyMvLo/l7GBLMdo6JiUFiYmLQ6U0QRGjB2RzPt0iDDknRyjolHMchxgDoNIErEyZnpwl4nkd+fj40Gg1SUlK8TlrUELvdjoqKCkREREg6j5BGMNqZ53lUVVWhsLAQAJCUlBRgjQiCuJwx10Z29NrguIdKhZydJrBaraiqqkJycjLCw8MlnetMfYWFhQXNQzgYCVY7G42Ot6fCwkIkJCRQSosgiCb5JusMftx9FjzPKyr3gskMgJydyxZnDYherw+wJkQo4nSgOY4jZ4cgiCZ5b/1RwTFhQUqstJf6YIGcHZFQTQXBAmpXBEFIoYZzpJtevaUHEqPCFJVt1Gtw9RUtFZXZXCBnhyAIgiCCBOeoqZFXJqBdy9CMwrAgNJNzhCwyMzOhUqlQWlrqk5zJkyfj1ltvVUQnJWUF07UJgiA8YbGFdiExKyiyE4IsXboUs2bNQklJCbRax1dcUVGB2NhYDBs2DJmZmcKxmZmZGDVqFI4fP46hQ4ciPz8f0dHRTPVzXhNwpHEiIyNxxRVXYOzYsXjmmWdcRia99957ihfiNeTUqVPo2LEj9uzZg759+/r12gRBhB6mGg7/3pyLsippc7M1BQ/AZnfck3QaSoFLgZydEGTUqFGoqKjArl27cPXVVwMANm/ejMTERGRlZaGmpgZhYY5cb0ZGBtq1a4dOnToBABITE/2m55EjRxAVFQWTyYTdu3fjzTffxOeff47MzEz06tULAJp0vCwWi+DQKQ1rp48giNDkl73n8f76Y8zk67VqtDDQ41sKZK0QpEuXLkhKSkJmZqbg7GRmZuKWW27Bhg0bsH37dowcOVLY7oyyOH8vKSlBTEwMli9fjhkzZuC///0vZsyYgby8PFxzzTVYtmyZEH2x2WyYNWsWvvjiC2g0Gjz00EOioyEJCQnCpHpXXnklbrnlFvTr1w9Tp07Fli1bADhSSaWlpfjpp58AACNHjkTPnj2h1Wrxn//8B7169cL69etx8OBBvPrqq9iyZQtatGiBcePG4V//+hdatWoFwDE8/e2338ann36KvLw8tG7dGo899hhefPFFdOzYEQDQr18/AMCIESOQmZnpdm2z2YxZs2bhu+++g8lkwsCBA/Gvf/0LV111lYv91q1bhxdeeAEHDx5E3759sWzZMnTp0sWHb5QgiGCirJoDAHRPisLobgmKyx/QPhZhOhq9KQVydiTC8zyqOXFLEtjtdlRbbNBarIrM/2LUaUSP3hk1ahQyMjIwe/ZsAI4IzvPPPw+bzYaMjAyMHDkS1dXVyMrKwoMPPtionKqqKrz99tv46quvoFarcd999+G5557D119/DQB45513sHz5cnzxxRfo1q0b3nnnHaxatQrXXXed9M9nNOLxxx/HM888I8w944kVK1Zg6tSp+PPPPwEApaWluOWWW/Dwww/j3XffRXV1NV544QXceeed2LBhAwBgzpw5+Oyzz/Cvf/0L11xzDfLz83H48GEAwI4dOzBo0CCsW7cOPXr0aHSageeffx4//vgjVqxYgfbt2+PNN9/E+PHjcfz4ccTFxQnHvfjii3jnnXcQHx+Pxx9/HA8++KCgK0EQoY+ziLh/+xg8O45edJoD5OxIpJqzofvcNQG59sFXxyNcL+4rGzVqFGbMmAGr1Yrq6mrs2bMHI0aMAMdxWLp0KQBg27ZtMJvNQmTHE87jnWmu6dOn49VXXxX2v/vuu5gzZw5uv/12AI56oTVr5Nuna9euABx1NI05O6mpqXjzzTeFv1977TX07t0bCxYsEJzKL774AikpKTh69CiSkpLw3nvv4cMPP8SkSZMAAJ06dcI111wDAIiPjwcAtGzZstE0XmVlJZYsWYLly5fjhhtuAAB89tlnSE9Px+eff45Zs2YJxy5YsAAjRowAAMyePRs33nijS+qQIIjQxrn0QiCXRyBcIWcnRBk5ciQqKyuxc+dOlJSU4Morr0R8fDxGjBiBKVOmoKamBpmZmbjiiivQrl27RuWEh4cLjg7gWNbAucRBWVkZ8vPzMXjwYGG/VqvFwIEDZRf2Os/zFsEaMGCAy9/79u3D5s2bERUV5XbsiRMnUFpaCrPZjNGjR8vSySmH4zgMGzZM2KbT6TBo0CAcOnTI5djevXsLvzvTfYWFhV7tTBCE/8m9WIkdhSrU7DkHjUa5x+GB8yYANGKqOUHOjkSMOg0Ovjpe1LF2ux3lpnJERkUqlsYSS+fOndG2bVtkZGSgpKREiDQkJycjJSUFW7duRUZGRpPpJp1O5/K3SqViOkLJ6Th06NCh0WNatGjh8ndFRQWuv/56vP322252TkpKwsmTJxXX0xv1beZ02qQuIksQBHsmfr4TRRUafH3iABP54Tp6xDYX6JuQiEqlEp1KstvtsOo1CNdrA7Jm06hRo5CZmYmSkhKXNMvw4cPxxx9/YMeOHZg6daps+dHR0UhKSkJWVhaGDx8OwLGWWHZ2Nvr37y9ZXnV1NT799FMMHz5cSC2JoX///vjf//6HDh06eKy3SU1NhdFoxPr16/Hwww+77Xee41waxBOdOnWCXq/Hn3/+ifbt2wNwpPh27tyJGTNmiNaVIIjmgdVmR1GFY2j40E5x0GuVLfiNMGhxe/82isok5BNQZ2fhwoVYuXIlDh8+DKPRiKFDh+KNN95wGblSUFCAWbNmIT09HeXl5ejSpQtefPFF3HHHHcIxxcXFePLJJ/Hrr79CrVbjjjvuwHvvvYeIiIhAfKxmw6hRozBt2jRwHCdEdgDHaKPp06fDYrF4rdcRw9NPP41FixYhNTUVXbt2xeLFi0VPSlhYWIiamhqUl5cjOzsbb775Ji5evIiVK1dK0uGJJ57AZ599hnvvvRcvvPAC4uLicPz4cXz33Xf497//jbCwMLzwwgt4/vnnodfrMWzYMBQVFeHAgQN46KGHkJCQAKPRiNWrV6Nt27YICwtzG3beokULTJ06FbNmzUJcXBzatWuHN998E1VVVXjooYck6UsQRODhbHUR6o/v6YuYCGMAtSFYE9CE4saNGzFt2jRs374d6enp4DgO48aNQ2VlpXDMAw88gCNHjuCXX37B/v37cfvtt+POO+/Enj17hGMmTpyIAwcOID09Hb/99hs2bdqERx99NBAfqVkxatQoVFdXo3PnzmjdurWwfcSIEYLjWH8CPzk8++yzuP/++zFp0iQMGTIEkZGRuO2220Sd26VLFyQnJ2PAgAFYtGgRxowZg5ycHHTv3l2SDsnJyVi9ejVsNhvGjRuHXr16YcaMGYiJiREiai+//DKeffZZzJ07F926dcNdd90l1B5ptVq8//77+OSTT5CcnIxbbrnF43UWLVqEO+64A/fffz/69++P48ePY82aNYiNjZWkL0EQgcc5Ygqg2prLARXfjKaILSoqQkJCAjZu3CikRSIiIrBkyRLcf//9wnEtW7bEG2+8gYcffhiHDh1C9+7dsXPnTgwcOBAAsHr1akyYMAFnz55FcnJyk9c1mUyIjo5GWVmZW5FrTU0NcnNz0bFjR8mjaex2O0wmE6KiogKSxrpcCGY7+9K+AgHHcUhLS8OECRPc6rkI5SA7u5JXXIUqi7gpP8RSUmXB3Z9uhwo8jrw6rtEpJwjfYdmevT2/69OsanbKysoAwGXOkqFDh+K///0vbrzxRsTExOD7779HTU2NMCnetm3bEBMTIzg6ADBmzBio1WpkZWV5jDKYzWaYzWbhb5PJUTnPcRw4jnM5luM48DwPu90uucjU6Uc6zyfYEMx2ttvt4HkeHMdBo2n+k4Q5+0fDfkIoC9m5jpV7zuGFlWwKiAFAo3LUGoqdw4yQDsv2LFZms3F27HY7ZsyYgWHDhqFnz57C9u+//x533XUXWrZsCa1Wi/DwcKxatQqdO3cG4KjpaTgfi1arRVxcHAoKCjxea+HChXjllVfctq9duxbh4a6ryGq1WiQmJqKiogIWi7x1TsrLy2WdR0gjGO1ssVhQXV2NTZs2wWq1Blod0aSnpwdahcsCsjOQdkoNQA2dmoeBQeC2fyue7OwnWNi5qqpK1HHNxtmZNm0acnJyhGUCnLz88ssoLS3FunXr0KpVK/z000+48847sXnzZmH9JKnMmTMHM2fOFP42mUxISUnBuHHjPKax8vLyEBERITnNwPM8ysvLERkZSW8NDAlmO9fU1MBoNGL48OFBk8ZKT0/H2LFjKb3CELJzHTt+PQTk5+Gx4Z3w9OjOisomO/sHlnZ2Zmaaolk4O9OnTxcKi9u2bStsP3HiBD788EPk5OSgR48eAIA+ffpg8+bN+Oijj7B06VIkJiYKhaZOrFYriouLG50N12AwwGAwuG3X6XRuX4TNZoNKpYJarZZcD+JMqTjPJ9gQzHZWq9VQqVQe215zJtj0DVbIzoCzjthoYGcLsrN/YGFnsfIC+mTgeR7Tp0/HqlWrsGHDBmFBRifO8FTDB5hGoxEecEOGDEFpaSmys7OF/Rs2bIDdbneZ2VcJXQlCaahdEYR36pZeCK6oLdG8CGhkZ9q0afjmm2/w888/IzIyUqixiY6OhtFoRNeuXdG5c2c89thjePvtt9GyZUv89NNPwhBzAOjWrRuuv/56PPLII1i6dCk4jsP06dNx9913ixqJ1RTOolGLxQKjkeZhIJTF6dDTWyURzNjtPO77PAu7TpcoLtvp7OhpnSnCBwLq7CxZsgQAhJFVTpYtW4bJkydDp9MhLS0Ns2fPxk033YSKigp07twZK1aswIQJE4Tjv/76a0yfPh2jR48WJhV8//33FdHRWRRdVFQEnU4nKU1it9thsVhQU1MTdOmVYCIY7czzPKqqqlBYWIiYmJigGIlFEI1RVGHG1hOXmMnXaVTo1Ta66QMJohEC6uyICeGnpqbixx9/9HpMXFwcvvnmG6XUckGlUiEpKQm5ubk4ffq0pHN5nkd1dTWMRmPQFc4GE8Fs55iYmEZrywgiWHBO0KfXqpH53EjF5UeEaREVRtFPQj7NokC5uaPX65Gamip56DnHcdi0aROGDx9OaQqGBKuddTodRXSIkMBSm2oyaNVIjqF0P9H8IGdHJGq1WvLQYI1GA6vVirCwsKB6CAcbZGeCCCzOyI6Bll0gmink7BAEQVwmZJ8uwTtrj8BsVXam8UqzY0JMKiImmivk7BAEQVwmfJ11mmkhcdvY8KYPIogAQM4OQRDEZUIN51hM855BKRhxZUITR0tDpQIGdYhr+kCCCADk7BAEQVwmOGtr+rSNwfU9aRQgcflACVaCIIjLBGetjo5qa4jLDIrsEARBNDO+3ZmHdblqZP9+WNGJMo8XVgBwzIdDEJcT5OwQBEE0I05drMTcXw4BUGNTwRkm14gN1zORSxDNFXJ2CIIgmhGl1RwAwKjhMfmaK6BRKzvxZOvoMAzp1FJRmQTR3CFnhyAIohnhLCKO1AEzx6TSRJkEoQCUuCUIgmhGOFf51gTXMm8E0ayRFdmJjY31uOCiSqVCWFgYOnfujMmTJ2PKlCk+K0gQBNEcKavikHm0EFZb0wsaS+FwgQkAQDXEBKEcspyduXPnYsGCBbjhhhswaNAgAMCOHTuwevVqTJs2Dbm5uZg6dSqsViseeeQRRRUmCIJoDsz7JQc/7T3PTL6enB2CUAxZzs6WLVswf/58PP744y7bP/nkE6xduxY//vgjevfujffff5+cHYIgQpLzZTUAgG5JUYiPNCgqWwUe3TUXFJVJEJczspydNWvW4I033nDbPnr0aDz77LMAgAkTJmD27Nm+aUcQBNFMcdbWPDMmFeN6KDsbMcdxSEtLU1QmQVzOyAqUxsXF4ddff3Xb/uuvvyIuzrE2SmVlJSIjI33TjiAIopniHDVFE/QRRPNHVmTn5ZdfxtSpU5GRkSHU7OzcuRNpaWlYunQpACA9PR0jRoxQTlOCIAgZlFRacLHCrLjcSrMVADk7BBEMyHJ2HnnkEXTv3h0ffvghVq5cCQDo0qULNm7ciKFDhwKAkM4iCIIIFGcuVWHM4o2w1KacWKCndaYIotkje1LBYcOGYdiwYUrqQhAEoSjHCsthsdmhUasQbVR+cr72LcPRIzlacbkEQSiLbGfnxIkTWLZsGU6ePIl3330XCQkJ+OOPP9CuXTv06NFDSR0JgiBk4Swi7pcSg/9NHRpgbQiCCBSy4q8bN25Er169kJWVhR9//BEVFY6VdPft24d58+YpqiBBEIRczFRETBAEZDo7s2fPxvz585Geng69vm713Ouuuw7bt29XTDmCIAhf4GpnN9ZRXQ1BXNbISmPt378f33zzjdv2hIQEXLx40WelCIK4vHhv3TEs3XgCdl7ZpRdsdoc8iuwQxOWNLGcnJiYG+fn56Nixo8v2PXv2oE2bNoooRhDE5cMv+86hmrMxk9+vXQwz2QRBNH9kOTt33303XnjhBfzwww9QqVSw2+34888/8dxzz+GBBx5QWkeCIEIc59Dwpff1R882yo5u0mvUSIgKU1QmQRDBhSxn5/XXX8e0adOQkpICm82G7t27w2q1YuLEiXjppZeU1pEgiBCHszrSTW1iwtE2NjzA2hAEEWrIcnb0ej0+++wzzJ07F/v370dFRQX69euH1NRUpfUjCOIywBnZodoagiBYINrZmTlzptf99UdhLV68WL5GBEE0S4orLZjz4z4cy9Pgq/M7oFKpFJNdVs0BIGeHIAg2iHZ29uzZ4/L37t27YbVa0aVLFwDA0aNHodFoMGDAAGU1JAiiWZBxuBBrDhYCUOFkeani8g1aNVpG6Js+kCAIQiKinZ2MjAzh98WLFyMyMhIrVqxAbGwsAKCkpARTpkzBtddeq7yWBEEEHOdoqfYRPGb9X19otRpF5XdOiERUmPJLOhAEQciq2XnnnXewdu1awdEBgNjYWMyfPx/jxo2jRUAJIgSx1M5G3NLAY3yP1tDpyDEhCCI4kJUgN5lMKCoqctteVFSE8vJyn5UiCKL54SwiprIagiCCDVmRndtuuw1TpkzBO++8g0GDBgEAsrKyMGvWLNx+++2KKkgQhDS2nriI9IMXFJe7L68UAKBRri6ZIAjCL8hydpYuXYrnnnsO9957LzjOMYpCq9XioYcewltvvaWoggRBSGPWD3/hXGk1M/ktZN01CIIgAoes21Z4eDg+/vhjvPXWWzhx4gQAoFOnTmjRooWiyhEEIZ3SKgsA4L6r2yle8GvQqNCy7LCiMgmCIFjj0ztaixYt0Lt3b6V0IQhCAZy1NdNGdUZStFFR2RzHIS2NnB2CIIILKjUkiBCC53lwNsfSCzoNdW+CIAjAx8gOQRDysNrs2HSsSJg5WDm5vPA7zUZMEAThgJwdgggAq/acw6z//cVMvkrlmJGYIAiCIGeHIAJCflkNACApOgydEyIUlz88NR4GhWc4JgiCCFYC6uwsXLgQK1euxOHDh2E0GjF06FC88cYbwnpbTrZt24YXX3wRWVlZ0Gg06Nu3L9asWQOj0VF8WVxcjCeffBK//vor1Go17rjjDrz33nuIiFD+IUIQSuCcjXh8j0T88+YeAdaGIAgitAlonHvjxo2YNm0atm/fjvT0dHAch3HjxqGyslI4Ztu2bbj++usxbtw47NixAzt37sT06dOhVtepPnHiRBw4cADp6en47bffsGnTJjz66KOB+EgEIQrniCmqqyEIgmBPQCM7q1evdvl7+fLlSEhIQHZ2NoYPHw4AeOaZZ/DUU09h9uzZwnH1Iz+HDh3C6tWrsXPnTgwcOBAA8MEHH2DChAl4++23kZyc7IdPQoQqNZwNZ0uqFJdbVG4GAOhpxBRBEARzmlXNTllZGQAgLi4OAFBYWIisrCxMnDgRQ4cOxYkTJ9C1a1csWLAA11xzDQBH5CcmJkZwdABgzJgxUKvVyMrKwm233eZ2HbPZDLPZLPxtMpkAOOYQcc4IrQROWUrKJNxhZWee53HjB1txoqiy6YNlolbxQdU+qE37B7KzfyA7+weWdhYrs9k4O3a7HTNmzMCwYcPQs2dPAMDJkycBAP/85z/x9ttvo2/fvvjyyy8xevRo5OTkIDU1FQUFBUhISHCRpdVqERcXh4KCAo/XWrhwIV555RW37WvXrkV4eLjCnwxIT09XXCbhjtJ2ttiAE0WOLhKu4QGF14QyagB90RGkpR1RVrAfoDbtH8jO/oHs7B9Y2LmqSlzkvdk4O9OmTUNOTg62bNkibLPbHXUNjz32GKZMmQIA6NevH9avX48vvvgCCxculHWtOXPmYObMmcLfJpMJKSkpGDduHKKionz4FK5wHIf09HSMHTsWOp2y0/YTdbCys6maA3ZkAAB2vDSWhnKD2rS/IDv7B7Kzf2BpZ2dmpimahbMzffp0obC4bdu2wvakpCQAQPfu3V2O79atG86cOQMASExMRGFhoct+q9WK4uJiJCYmeryewWCAwWBw267T6Zg0eFZyCVeUtrO9xi783iJMD5WKlvt2Qm3aP5Cd/QPZ2T+wsLNYeQF9VeV5HtOnT8eqVauwYcMGdOzY0WV/hw4dkJycjCNHXMP8R48eRfv27QEAQ4YMQWlpKbKzs4X9GzZsgN1ux+DBg9l/CCJk4WpHTOk0KnJ0CIIggpiARnamTZuGb775Bj///DMiIyOFGpvo6GgYjUaoVCrMmjUL8+bNQ58+fdC3b1+sWLEChw8fxv/+9z8AjijP9ddfj0ceeQRLly4Fx3GYPn067r77bhqJdZmw+fhFvLhLg9m71kHJwho771h6gUZMEQRBBDcBdXaWLFkCABg5cqTL9mXLlmHy5MkAgBkzZqCmpgbPPPMMiouL0adPH6Snp6NTp07C8V9//TWmT5+O0aNHC5MKvv/++/76GESA2XC4CBWcCoC9yWPl0LddDBO5BEEQhH8IqLPD83zTBwGYPXu2yzw7DYmLi8M333yjlFpEkOFMNz04tD2mXHOF4vLbxBgVl0kQBEH4j2ZRoEwQvuBceqFlhB4pccpPHUAQBEEEN1SMQAQ9FmttbQ0NDScIgiA8QJEdwm+8nnYIe/NKFZd77EI5ACokJgiCIDxDzg7hFwrKavDpppNMr9EmJoypfIIgCCI4IWeH8AvVnA0AYNRp8M6dfRSVbbVacSxnN4antlJULkEQBBEakLND+AVnEXG4XoMJvZIUlc1xHPgzoIn/CIIgCI9QkQPhF5zODhUREwRBEP6GIjuEC3nFVfhmxxnBOVGKwnIzAEBHRcQEQRCEnyFnh3Dh/fXH8EP2WWbyY8NpsT2CIAjCv5CzQ7hQWs0BAEZcGY/uyVGKylargBt6KluvQxAEQRBNQc4O4YJz6YWb+iTjbwPaBlgbgiAIgvAdKqAgXHDW6ug0NLKJIAiCCA0oshOk7M0rxelLlYrLLTDVAAAMNGqKIAiCCBHI2QlC8oqrcOtHfzK9hlFPTYMgCIIIDeiJFoRcqI2+hOnUGNg+TnH5SdFhGNxRebkEQRAEEQjI2QlCnHU1KbHh+M/DgwOsDUEQBEE0b6gwIwix2Gg2YoIgCIIQC0V2GJJXUoUL1cDJokpodcqZOq+kGgA5OwRBEAQhBnJ2GPLwl7tx8qIWr+9lU0xMSy8QBEEQRNOQs8OQiDAtwjU8dHodAGXnrdGqVbi1bxtFZRIEQRBEKELODkN+fOxqpKWlYcKE8dDpaE0ogiAIgggElAchCIIgCCKkIWeHIAiCIIiQhtJYAHieBwCYTCZF5XIch6qqKphMJkpjMYTs7D/I1v6B7OwfyM7+gaWdnc9t53O8McjZAVBeXg4ASElJCbAmBEEQBEFIpby8HNHR0Y3uV/FNuUOXAXa7HefPn0dkZCRUKuVGTZlMJqSkpCAvLw9RUVGKySVcITv7D7K1fyA7+weys39gaWee51FeXo7k5GSo1Y1X5lBkB4BarUbbtm2ZyY+KiqKO5AfIzv6DbO0fyM7+gezsH1jZ2VtExwkVKBMEQRAEEdKQs0MQBEEQREhDzg5DDAYD5s2bB4PBEGhVQhqys/8gW/sHsrN/IDv7h+ZgZypQJgiCIAgipKHIDkEQBEEQIQ05OwRBEARBhDTk7BAEQRAEEdKQs0MQBEEQREhDzg5BEARBECENOTsEQRAEQYQ05OwQBEEQBBHSkLNDEARBEERIQ84OQRAEQRAhDTk7BEEQBEGENOTsEARBEAQR0pCzQxAEQRBESEPODkEQBEEQIQ05OwRBEARBhDTaQCvQHLDb7Th//jwiIyOhUqkCrQ5BEARBECLgeR7l5eVITk6GWt14/IacHQDnz59HSkpKoNUgCIIgCEIGeXl5aNu2baP7ydkBEBkZCcBhrKioKMXkchyHtWvXYty4cdDpdIrJJVwhO/sPsrV/IDv7B7Kzf2BpZ5PJhJSUFOE53hjk7ABC6ioqKkpxZyc8PBxRUVHUkRhCdvYfZGv/QHb2D2Rn/+APOzdVgkLODkEQIYvdzuObHWdwrrQ60KpIwm6z48RpNQ6uPQa1hsaRsCJY7XxVh1hc17V1oNUIKsjZIQgiZNmTV4KXfsoJtBoyUWPd+dxAK3EZEHx2/lyrxl/zxiFMpwm0KkEDOTsEQYQs5TVWAEDLFnrc2q9NgLURj91uR25uLjp27Oh1hAnhG8FmZ5udx/Ktp2Cx2sHZ7OTsSICcHZHYbDZwHCfpHI7joNVqUVNTA5vNxkgzQo6dNRoNtFotTTVwmZAUE4aX/697oNUQDcdxSEs7gQk3dKFaEoYEm53NVhuWbz0VaDWCEnJ2RFBRUYGzZ8+C53lJ5/E8j8TEROTl5dFDlSFy7RweHo6kpCTo9XqG2hGBRFqPJYjggdq2NMjZaQKbzYazZ88iPDwc8fHxkh6mdrsdFRUViIiICIoQabAi1c48z8NisaCoqAi5ublITU2l74cgCCKEIWenCTiOA8/ziI+Ph9FolHSu3W6HxWJBWFgYPUwZIsfORqMROp0Op0+fFs4lQhcVKLJKBD/UjuVDT2CRUBoq9CAH9DKAYv1EiCKxquKyh+72BEEQBEGENOTsEEHL5MmTceuttwZaDSIIoMAsEQpQO5YPOTshCM/zGDNmDMaPH++27+OPP0ZMTAzOnj0bAM0Iwr/wlMciQhVq2pIgZycEUalUWLZsGbKysvDJJ58I23Nzc/H888/jgw8+8Lo6LEEQBEGEEuTshCgpKSl477338NxzzyE3Nxc8z+Ohhx7C2LFjkZmZiY4dO8JoNKJLly547733hPNycnKgVqtRVFQEACguLoZarcbdd98tHDN//nxcc801jV77q6++wsCBAxEZGYnExETce++9KCwsFPZnZmZCpVJh/fr1GDhwIMLDwzF06FAcOXLERc78+fORkJCAyMhIPPzww5g9ezb69u3b6HXtdjsWLlwofLY+ffrgf//7n1TTESEIRf+JUIDasXzI2ZEIz/OoslhF/6u22CQd7+2f1EkNJ02ahNGjR+PBBx/Ehx9+iJycHHz66ado27YtfvjhBxw8eBBz587FP/7xD3z//fcAgB49eqBly5bYuHEjAGDz5s0ufwPAxo0bMXLkyEavy3EcXnvtNezbtw8//fQTTp06hcmTJ7sd9+KLL+Kdd97Brl27oNVq8eCDDwr7vv76ayxYsABvvPEGsrOz0a5dOyxZssTr5120aBG+/PJLLF26FAcOHMAzzzyD++67z0V34vKCRqwQoQqlaKVB8+xIpJqzofvcNQG59sFXxyNcL+0r+/TTT9GjRw9s2rQJP/74I+Lj4/HKK68I+zt27Iht27bh+++/x5133gmVSoXhw4cjMzMTf/vb35CZmYkpU6bg3//+Nw4fPoxOnTph69ateP755xu9Zn2n5YorrsD777+Pq666Spj4z8mCBQswYsQIAMDs2bNx4403oqamBmFhYfjggw/w0EMPYcqUKQCAuXPnYu3ataioqPB4TbPZjIULF2LdunUYMmSIcO0tW7bgk08+Ea5DEARBXH5QZCfESUhIwGOPPYZu3boJI5c++ugjDBgwAPHx8YiIiMCnn36KM2fOCOeMGDECmZmZABxRnOuuu05wgHbu3AmO4zBs2LBGr5mdnY2bbroJ7dq1Q2RkpOBo1L8GAPTu3Vv4PSkpCQCEdNeRI0cwaNAgl+Mb/l2fkydPoqqqCmPHjkVERITw78svv8SJEyeasBIRqgiRHRrGQoQA9ed7o6ilNCiyIxGjToODr7qPcvKE3W5HuakckVGRikxgZ5S5wq1Wq4VW6/iqv/vuOzz33HN45513MGTIEERGRuKtt95CVlaWcPzIkSMxY8YMHDt2DAcPHsQ111yDw4cPIzMzEyUlJUKdjScqKysxfvx4jB8/Hl9//TXi4+Nx5swZjB8/HhaLxeXY+gvvOTux3W6X9RkrKysBAL///jvatHFd3dpgMMiSSRAEQYQGzdrZWbhwIVauXInDhw/DaDRi6NCheOONN9ClSxfhmJqaGjz77LP47rvvYDabMX78eHz88cdo3bo1E51UKpXoVJLdbodVr0G4XttsZuv9888/MXToUDzxxBPCtoaRj169eiE2Nhbz589H3759ERERgZEjR+KNN95ASUmJ13qdw4cP49KlS1i0aBFSUlIAALt27ZKsZ5cuXbBz50488MADwradO3d6Pd5gMODMmTOUsiLcoLgOEQpQO5ZP83gCN8LGjRsxbdo0bN++Henp6eA4DuPGjRPe4gHgmWeewa+//ooffvgBGzduxPnz53H77bcHUOvmTWpqKnbt2oU1a9bg6NGjePnll92cCGfdztdffy04Nr1794bZbMb69eu9OhPt2rWDXq/HBx98gJMnT+KXX37Ba6+9JlnPJ598Ep9//jlWrFiBY8eOYf78+fjrr78aXbYjMjISzz77LJ555hmsWLECJ06cwO7du/HBBx9gxYoVkq9PhAYU6SdCFWrb0mjWzs7q1asxefJk9OjRA3369MHy5ctx5swZZGdnAwDKysrw+eefY/HixbjuuuswYMAALFu2DFu3bsX27dsDrH3z5LHHHsPtt9+Ou+66C4MHD8alS5dcojxORowYAZvNJjg7arUaw4cPh0ql8lqvEx8fj+XLl+OHH35A9+7dsWjRIrz99tuS9Zw4cSLmzJmD5557Dv3790dubi4mT57sdcHOV199FS+//DIWLlyIbt264frrr8fvv/+Ojh07Sr4+QRAEETqoeKnjmQPI8ePHkZqaiv3796Nnz57YsGEDRo8ejZKSEsTExAjHtW/fHjNmzMAzzzzjUY7ZbIbZbBb+NplMSElJwcWLFxEVFeVybE1NDfLy8tChQwfJK2PzPI/y8nJERkbSQqIKMG7cOCQmJuLLL7902S7XzjU1NTh16hRSUlJo1XORcByH9PR0jB071qXmqrmy/lAhHv9mL/qmROOHRwcHWh3RBJudg5VgszPP87hybjoAIGv2SMS10AdYI3GwtLPJZEKrVq1QVlbm9vyuT7Ou2amP3W7HjBkzMGzYMPTs2RMAUFBQAL1e7+LoAEDr1q1RUFDQqKyFCxe6DL92snbtWrfCW61Wi8TERFRUVLgV2IqlvLxc1nmXM1VVVVi2bBmuu+46aDQa/Pjjj1i/fj1WrVoFk8nk8RypdrZYLKiursamTZtgtVqVUPuyIT09PdAqiGJ/sQqABqUlpUhLSwu0OpIJFjsHO8FiZ0dowvHYXrduHSKav3/mAgs7V1VViTouaJydadOmIScnB1u2bPFZ1pw5czBz5kzhb2dkZ9y4cY1GdiIiIiiy40d0Oh0yMjKwePFi1NTUoEuXLvjhhx9w8803ux3rS2THaDRi+PDhFNkRSbC9CesPFeLfR/YiJjYGEyZQZIdwJdjszPM8Zmx3OAyjx4xBS4rsNPry25CgcHamT5+O3377DZs2bXJZ0ykxMREWiwWlpaUu0Z0LFy4gMTGxUXkGg8HjcGSdTuf2RdhsNqhUKqjVaskjqpzDqJ3nE+Jp0aIF1q1bJ+pYuXZWq9VQqVQev3fCO8FiM43GMV2DuvZ7DjaCxc7BTjDaWafVBp/ODOwsVl6zfgLzPI/p06dj1apV2LBhg1uh6YABA6DT6bB+/Xph25EjR3DmzBlhFl2CIC5fgqYgkSAkQm1bGs06sjNt2jR88803+PnnnxEZGSnU4URHR8NoNCI6OhoPPfQQZs6cibi4OERFReHJJ5/EkCFDcPXVVwdYe4IgCIIgmgPN2tlxLvzYcBK7ZcuWCQtL/utf/4JarcYdd9zhMqmg0gTRoDVCJPSdXj5QzRxBXN40a2dHzMMoLCwMH330ET766CMmOjhz/haLBUajkck1iMDgrOIPtrw3IR7yZ4lQhdq2NJq1s9Mc0Gq1CA8PR1FREXQ6naQCWLvdDovFgpqaGipQZohUO/M8j6qqKhQWFiImJkZwaAmCIIjQhJydJlCpVEhKSkJubi5Onz4t6Vye51FdXQ2j0UhhdIbItXNMTIzXUXtE6EC9jwgVVCqK6siBnB0R6PV6pKamSp5UkOM4bNq0CcOHD6dUCUPk2Fmn01FE57KAngpEaMJT25YEOTsiUavVkiee02g0sFqtCAsLI2eHIWRngiAIwhtUSEIQRMhDWWQiVKCmLA9ydgiCCFmotoEIWahtS4KcHYIgCIIgQhpydgiCCHlUFPwnQgQa2SsPcnYIgghZKNJPhCrUtqVBzg5BEARBECENOTsEQYQsQoEyRf6JEMHZlKn4Xhrk7BAEQRAEEdKQs0MQRMhDgR0iVKD6ZHkwm0E5NjbWY9W4SqVCWFgYOnfujMmTJ2PKlCmsVCAI4jKHptQnQhVq29Jg5uzMnTsXCxYswA033IBBgwYBAHbs2IHVq1dj2rRpyM3NxdSpU2G1WvHII4+wUoMgCIIgiMscZs7Oli1bMH/+fDz++OMu2z/55BOsXbsWP/74I3r37o3333+fnB2CIJhCoX8iVHDMGUVRHakwq9lZs2YNxowZ47Z99OjRWLNmDQBgwoQJOHnyJCsVCIK4zKERK0SoQm1bGsycnbi4OPz6669u23/99VfExcUBACorKxEZGclKBYIgCIIgCHZprJdffhlTp05FRkaGULOzc+dOpKWlYenSpQCA9PR0jBgxgpUKBEEQAGi5CCKEoKYsC2bOziOPPILu3bvjww8/xMqVKwEAXbp0wcaNGzF06FAAwLPPPsvq8gRBEFTZQIQs1LalwczZAYBhw4Zh2LBhLC9BEARBEAThFaaTCp44cQIvvfQS7r33XhQWFgIA/vjjDxw4cIDlZQmCIFyg0VhEqEBNWR7MnJ2NGzeiV69eyMrKwo8//oiKigoAwL59+zBv3jxWlyUIghDgacgKEaJQ25YGM2dn9uzZmD9/PtLT06HX64Xt1113HbZv387qsgRBEARBEC4wc3b279+P2267zW17QkICLl68yOqyBEEQblAaiwgVqC3Lg5mzExMTg/z8fLfte/bsQZs2bVhdliAIgiBCHspiSYOZs3P33XfjhRdeQEFBAVQqFex2O/78808899xzeOCBB1hdliAIgiAIwgVmzs7rr7+Orl27IiUlBRUVFejevTuuvfZaDB06FC+99BKryxIEQbhBkwoSoQK1ZXkwm2dHr9fjs88+w9y5c7F//35UVFSgX79+SE1NZXVJgiAIFyjUTxAEoLCzM3PmTK/764/CWrx4sSiZmzZtwltvvYXs7Gzk5+dj1apVuPXWW4X9PM9j3rx5+Oyzz1BaWophw4ZhyZIl5FQRBEEQBAFAYWdnz549Ln/v3r0bVqsVXbp0AQAcPXoUGo0GAwYMEC2zsrISffr0wYMPPojbb7/dbf+bb76J999/HytWrEDHjh3x8ssvY/z48Th48CDCwsJ8+0AEQYQENIKFCBWoLctDUWcnIyND+H3x4sWIjIzEihUrEBsbCwAoKSnBlClTcO2114qWecMNN+CGG27wuI/nebz77rt46aWXcMsttwAAvvzyS7Ru3Ro//fQT7r77bh8+DUEQwQ5PKwgRIQqlaKXBrGbnnXfewdq1awVHBwBiY2Mxf/58jBs3TpFFQHNzc1FQUIAxY8YI26KjozF48GBs27atUWfHbDbDbDYLf5tMJgAAx3HgOM5nvZw4ZSkpk3CH7Ow/gs3WNqsNAGC380GjMxB8dg5WgtnOnFXZ5xVLWNpZrExmzo7JZEJRUZHb9qKiIpSXlytyjYKCAgBA69atXba3bt1a2OeJhQsX4pVXXnHbvnbtWoSHhyuiW33S09MVl0m4Q3b2H8Fi671FKgAaXLpYhLS0tECrI5lgsXOwE0x2tlk1AFTIzMxEqyCr1GBh56qqKlHHMXN2brvtNkyZMgXvvPMOBg0aBADIysrCrFmzPNbe+JM5c+a4FFObTCakpKRg3LhxiIqKUuw6HMchPT0dY8eOhU6nU0wu4QrZ2X8Em60te8/jP8dz0Co+HhMmiK8VDDTBZudgJRjt/I/s9TBbbBgxciTaxyn/cs4ClnZ2Zmaagpmzs3TpUjz33HO49957hTCTVqvFQw89hLfeekuRayQmJgIALly4gKSkJGH7hQsX0Ldv30bPMxgMMBgMbtt1Oh2TBs9KLuEK2dl/BIutNRoNAECtVgeFvg0JFjsHO8FkZ1VthbJOqw0anZ2wsLNYecwmFQwPD8fHH3+MS5cuYc+ePdizZw+Ki4vx8ccfo0WLFopco2PHjkhMTMT69euFbSaTCVlZWRgyZIgi1yAIInihIk4iVKG2LQ1mkR0nLVq0QO/evWWfX1FRgePHjwt/5+bmYu/evYiLi0O7du0wY8YMzJ8/H6mpqcLQ8+TkZJe5eAiCIAiCuHxh7uz4yq5duzBq1Cjhb2etzaRJk7B8+XI8//zzqKysxKOPPorS0lJcc801WL16Nc2xQxCEAE1NQoQK1Jbl0eydnZEjR4L3Eq9TqVR49dVX8eqrr/pRK4IgggGK9BOhCrVtaTCr2SEIgiAIgmgOkLNDEETIQ1PsEyEDtWVZkLNDEETI4i0FThDBDLVtaZCzQxAEQRBESEPODkEQIQ9F/olQgdqyPMjZIQgiZKFAPxGqUNuWBjk7BEEQBEGENOTsEAQR8qhoOBYRIlBblgc5OwRBhC4U6ydCFBqMJQ1ydgiCIAiCCGnI2SEIIuShwD8RKlAWSx7k7BAEEbLwlMciQhZq21IgZ4cgCIIgiJCGnB2CIEIeCv0ToQI1ZXmQs0MQRMhCI1aIUIXatjTI2SEIgiAIIqQhZ4cgiJCl7uWXgv9EaOCcVJACO9IgZ4cgCIIgiJCGnB2CIEIeKlAmQgVqyvIgZ4cgiJCFijiJUIXatjTI2SEIgiAIIqQhZ4cgiJCHQv9EqEApWXmQs0MQRMhCy0UQoQq1bWmQs0MQBEEQREhDzg5BECEPhf6J0IEasxzI2SEIImShEStEqEJtWxrk7BAEQRAEEdKQs0MQRMijotA/ESJQSlYeIePsfPTRR+jQoQPCwsIwePBg7NixI9AqEQQRYCjST4QqlMaSRkg4O//9738xc+ZMzJs3D7t370afPn0wfvx4FBYWBlo1giAIgiACTEg4O4sXL8YjjzyCKVOmoHv37li6dCnCw8PxxRdfBFo1giCaART6J0IFasry0AZaAV+xWCzIzs7GnDlzhG1qtRpjxozBtm3bPJ5jNpthNpuFv00mEwCA4zhwHKeYbi/+lIOcE2qsvJgNFd1tmcHzPIqKyM7+INhsfa60GgBgt9sV7dusceoaTDoHI8Fs57k/70eEITge4c77Ru/BFWjbMkJR2WK/u+CwlBcuXrwIm82G1q1bu2xv3bo1Dh8+7PGchQsX4pVXXnHbvnbtWoSHhyumW+YBDQpr1EDpJcVkEo1BdvYfwWfryksFSEtLC7QakklPTw+0CpcFwWRnnU0DQIVdp0sDrYpE1FiXuQkJRmWlVlVViTou6J0dOcyZMwczZ84U/jaZTEhJScG4ceMQFRWl3IXansf23fvQo0cPaDQa5eQSLthsNhw4cIDs7AeC0dZ6jRqjurRCZJgu0KqIhuM4pKenY+zYsdDpgkfvYCMY7dx3aDW2nSwOquJ7533j5nGjEBeprLfjzMw0RdA7O61atYJGo8GFCxdctl+4cAGJiYkezzEYDDAYDG7bdTqdog1+Qu9k4OxeTLiqXdB0pGCE4zikFeWQnf0A2dq/KH1PIjwTTHZuH69D+3gFX8r9gPO+ERdpVNzOYuUFfYGyXq/HgAEDsH79emGb3W7H+vXrMWTIkABqRhAEQRBEcyDoIzsAMHPmTEyaNAkDBw7EoEGD8O6776KyshJTpkwRdT5fO2GB2HCYWDiOQ1VVFUwmU9C8NQQjZGf/Qbb2D2Rn/0B29g8s7ex8bvNNTDwUEs7OXXfdhaKiIsydOxcFBQXo27cvVq9e7Va03Bjl5eUAgJSUFJZqEgRBEATBgPLyckRHRze6X8U35Q5dBtjtdpw/fx6RkZGKDqd1Fj7n5eUpW/hMuEB29h9ka/9AdvYPZGf/wNLOPM+jvLwcycnJUKsbr8wJiciOr6jVarRt25aZ/KioKOpIfoDs7D/I1v6B7OwfyM7+gZWdvUV0nAR9gTJBEARBEIQ3yNkhCIIgCCKkIWeHIQaDAfPmzfM4pw+hHGRn/0G29g9kZ/9AdvYPzcHOVKBMEARBEERIQ5EdgiAIgiBCGnJ2CIIgCIIIacjZIQiCIAgipCFnhyAIgiCIkIacHYZ89NFH6NChA8LCwjB48GDs2LEj0Co1WxYuXIirrroKkZGRSEhIwK233oojR464HFNTU4Np06ahZcuWiIiIwB133OG22v2ZM2dw4403Ijw8HAkJCZg1axasVqvLMZmZmejfvz8MBgM6d+6M5cuXs/54zZZFixZBpVJhxowZwjayszKcO3cO9913H1q2bAmj0YhevXph165dwn6e5zF37lwkJSXBaDRizJgxOHbsmIuM4uJiTJw4EVFRUYiJicFDDz2EiooKl2P++usvXHvttQgLC0NKSgrefPNNv3y+5oDNZsPLL7+Mjh07wmg0olOnTnjttddc1kkiO8tj06ZNuOmmm5CcnAyVSoWffvrJZb8/7frDDz+ga9euCAsLQ69evZCWlib9A/EEE7777jter9fzX3zxBX/gwAH+kUce4WNiYvgLFy4EWrVmyfjx4/lly5bxOTk5/N69e/kJEybw7dq14ysqKoRjHn/8cT4lJYVfv349v2vXLv7qq6/mhw4dKuy3Wq18z549+TFjxvB79uzh09LS+FatWvFz5swRjjl58iQfHh7Oz5w5kz948CD/wQcf8BqNhl+9erVfP29zYMeOHXyHDh343r17808//bSwnezsO8XFxXz79u35yZMn81lZWfzJkyf5NWvW8MePHxeOWbRoER8dHc3/9NNP/L59+/ibb76Z79ixI19dXS0cc/311/N9+vTht2/fzm/evJnv3Lkzf8899wj7y8rK+NatW/MTJ07kc3Jy+G+//ZY3Go38J5984tfPGygWLFjAt2zZkv/tt9/43Nxc/ocffuAjIiL49957TziG7CyPtLQ0/sUXX+RXrlzJA+BXrVrlst9fdv3zzz95jUbDv/nmm/zBgwf5l156idfpdPz+/fslfR5ydhgxaNAgftq0acLfNpuNT05O5hcuXBhArYKHwsJCHgC/ceNGnud5vrS0lNfpdPwPP/wgHHPo0CEeAL9t2zae5x2dU61W8wUFBcIxS5Ys4aOioniz2czzPM8///zzfI8ePVyuddddd/Hjx49n/ZGaFeXl5Xxqaiqfnp7OjxgxQnB2yM7K8MILL/DXXHNNo/vtdjufmJjIv/XWW8K20tJS3mAw8N9++y3P8zx/8OBBHgC/c+dO4Zg//viDV6lU/Llz53ie5/mPP/6Yj42NFezuvHaXLl2U/kjNkhtvvJF/8MEHXbbdfvvt/MSJE3meJzsrRUNnx592vfPOO/kbb7zRRZ/Bgwfzjz32mKTPQGksBlgsFmRnZ2PMmDHCNrVajTFjxmDbtm0B1Cx4KCsrAwDExcUBALKzs8FxnItNu3btinbt2gk23bZtG3r16uWy2v348eNhMplw4MAB4Zj6MpzHXG7fy7Rp03DjjTe62YLsrAy//PILBg4ciL///e9ISEhAv3798Nlnnwn7c3NzUVBQ4GKj6OhoDB482MXOMTExGDhwoHDMmDFjoFarkZWVJRwzfPhw6PV64Zjx48fjyJEjKCkpYf0xA87QoUOxfv16HD16FACwb98+bNmyBTfccAMAsjMr/GlXpe4l5Oww4OLFi7DZbC4PAwBo3bo1CgoKAqRV8GC32zFjxgwMGzYMPXv2BAAUFBRAr9cjJibG5dj6Ni0oKPBoc+c+b8eYTCZUV1ez+DjNju+++w67d+/GwoUL3faRnZXh5MmTWLJkCVJTU7FmzRpMnToVTz31FFasWAGgzk7e7hEFBQVISEhw2a/VahEXFyfpuwhlZs+ejbvvvhtdu3aFTqdDv379MGPGDEycOBEA2ZkV/rRrY8dItTutek40O6ZNm4acnBxs2bIl0KqEHHl5eXj66aeRnp6OsLCwQKsTstjtdgwcOBCvv/46AKBfv37IycnB0qVLMWnSpABrFzp8//33+Prrr/HNN9+gR48e2Lt3L2bMmIHk5GSyM+ECRXYY0KpVK2g0GrcRLBcuXEBiYmKAtAoOpk+fjt9++w0ZGRlo27atsD0xMREWiwWlpaUux9e3aWJiokebO/d5OyYqKgpGo1Hpj9PsyM7ORmFhIfr37w+tVgutVouNGzfi/fffh1arRevWrcnOCpCUlITu3bu7bOvWrRvOnDkDoM5O3u4RiYmJKCwsdNlvtVpRXFws6bsIZWbNmiVEd3r16oX7778fzzzzjBC1JDuzwZ92bewYqXYnZ4cBer0eAwYMwPr164Vtdrsd69evx5AhQwKoWfOF53lMnz4dq1atwoYNG9CxY0eX/QMGDIBOp3Ox6ZEjR3DmzBnBpkOGDMH+/ftdOlh6ejqioqKEB8+QIUNcZDiPuVy+l9GjR2P//v3Yu3ev8G/gwIGYOHGi8DvZ2XeGDRvmNnXC0aNH0b59ewBAx44dkZiY6GIjk8mErKwsFzuXlpYiOztbOGbDhg2w2+0YPHiwcMymTZvAcZxwTHp6Orp06YLY2Fhmn6+5UFVVBbXa9TGm0Whgt9sBkJ1Z4U+7KnYvkVTOTIjmu+++4w0GA798+XL+4MGD/KOPPsrHxMS4jGAh6pg6dSofHR3NZ2Zm8vn5+cK/qqoq4ZjHH3+cb9euHb9hwwZ+165d/JAhQ/ghQ4YI+51DoseNG8fv3buXX716NR8fH+9xSPSsWbP4Q4cO8R999NFlNSTaE/VHY/E82VkJduzYwWu1Wn7BggX8sWPH+K+//poPDw/n//Of/wjHLFq0iI+JieF//vln/q+//uJvueUWj0N3+/Xrx2dlZfFbtmzhU1NTXYbulpaW8q1bt+bvv/9+Picnh//uu+/48PDwkB4SXZ9Jkybxbdq0EYaer1y5km/VqhX//PPPC8eQneVRXl7O79mzh9+zZw8PgF+8eDG/Z88e/vTp0zzP+8+uf/75J6/Vavm3336bP3ToED9v3jwaet7c+OCDD/h27drxer2eHzRoEL99+/ZAq9RsAeDx37Jly4Rjqqur+SeeeIKPjY3lw8PD+dtuu43Pz893kXPq1Cn+hhtu4I1GI9+qVSv+2Wef5TmOczkmIyOD79u3L6/X6/krrrjC5RqXIw2dHbKzMvz66698z549eYPBwHft2pX/9NNPXfbb7Xb+5Zdf5lu3bs0bDAZ+9OjR/JEjR1yOuXTpEn/PPffwERERfFRUFD9lyhS+vLzc5Zh9+/bx11xzDW8wGPg2bdrwixYtYv7Zmgsmk4l/+umn+Xbt2vFhYWH8FVdcwb/44osuQ5nJzvLIyMjweE+eNGkSz/P+tev333/PX3nllbxer+d79OjB//7775I/j4rn6001SRAEQRAEEWJQzQ5BEARBECENOTsEQRAEQYQ05OwQBEEQBBHSkLNDEARBEERIQ84OQRAEQRAhDTk7BEEQBEGENOTsEARBEAQR0pCzQxAEQRBESEPODkEQBEEQIQ05OwRBEARBhDTk7BAEQRAEEdKQs0MQBEEQREjz/8+DGe6vK+vjAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up yaw angles to be positive for winds over 250\n", - "yaw_angles = np.zeros([len(ws_array), num_turbines])\n", - "yaw_angles[\n", - " ((np.array(wd_array) >= 270) & (np.array(wd_array) <= 285)), control_idx\n", - "] = 25 # Set control turbine yaw angles to 25 deg\n", - "\n", - "fig, axarr = plt.subplots(3, 1, sharex=True)\n", - "axarr[0].plot(t, ws_array, label=\"Wind Speed\")\n", - "axarr[0].set_ylabel(\"m/s\")\n", - "axarr[0].legend()\n", - "axarr[0].grid(True)\n", - "axarr[1].plot(t, wd_array, label=\"Wind Direction\")\n", - "axarr[1].set_ylabel(\"deg\")\n", - "axarr[1].legend()\n", - "axarr[1].grid(True)\n", - "axarr[2].plot(t, yaw_angles[:, control_idx], label=\"Yaw angle\")\n", - "axarr[2].set_ylabel(\"deg\")\n", - "axarr[2].legend()\n", - "axarr[2].grid(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "fm_benchmark.set(yaw_angles=yaw_angles)\n", - "fm_benchmark.run()\n", - "power_wakesteering = fm_benchmark.get_turbine_powers().squeeze() / 1000.0" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Recompute assuming some random perturbation of the wd signal\n", - "wd_std_benchmark = 2.0\n", - "wd_array_noisy = (wd_array + np.random.randn(len(wd_array)) * wd_std_benchmark,)\n", - "wd_array_noisy = wd_array_noisy[0]\n", - "plt.plot(wd_array_noisy)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "fm_benchmark.reset_operation()\n", - "fm_benchmark.set(\n", - " wind_speeds=ws_array,\n", - " wind_directions=wd_array_noisy,\n", - " turbulence_intensities=0.06 * np.ones_like(ws_array),\n", - ")\n", - "fm_benchmark.run()\n", - "power_baseline_noisy = fm_benchmark.get_turbine_powers().squeeze() / 1000.0\n", - "\n", - "\n", - "fm_benchmark.set(yaw_angles=yaw_angles)\n", - "fm_benchmark.run()\n", - "power_wakesteering_noisy = fm_benchmark.get_turbine_powers().squeeze() / 1000.0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Assemble the data into pandas dataframes" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def get_df(data, wd_, ws_):\n", - " # Define the power column names\n", - " pow_cols = [\"pow_%03d\" % t for t in range(num_turbines)]\n", - "\n", - " # Build the dataframe\n", - " df_ = FlascDataFrame(data=data, columns=pow_cols)\n", - "\n", - " # Add ws and wd and pow_ref\n", - " df_ = df_.assign(\n", - " wd=wd_, # + np.random.randn(len(wd_array))* wd_noise,\n", - " ws=ws_, # + np.random.randn(len(ws_array))* ws_noise\n", - " )\n", - "\n", - " # Aggregate every 10 points together\n", - " df_[\"group\"] = df_.index // points_per_group\n", - "\n", - " # Group the DataFrame by the 'group' column and calculate the mean\n", - " df_ = df_.groupby(\"group\").agg(\"mean\")\n", - "\n", - " # Reset the index to have a clean index\n", - " df_ = df_.reset_index(drop=True)\n", - " # df_['pow_ref'] = df_['pow_%03d' % ref_idx]\n", - "\n", - " # Reorganize columns\n", - "\n", - " df_ = df_[[\"wd\", \"ws\"] + pow_cols]\n", - "\n", - " return df_" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_baseline = get_df(power_baseline, wd_array, ws_array)\n", - "df_wakesteering = get_df(power_wakesteering, wd_array, ws_array)\n", - "df_baseline_noisy = get_df(power_baseline_noisy, wd_array_noisy, ws_array)\n", - "df_wakesteering_noisy = get_df(power_wakesteering_noisy, wd_array_noisy, ws_array)\n", - "# df_wakesteering = get_df(power_wakesteering)\n", - "\n", - "# # Get noisy versions\n", - "# df_baseline_noisy = get_df(power_baseline, wd_noise=2., ws_noise=0.)\n", - "# df_wakesteering_noisy = get_df(power_wakesteering, wd_noise=2., ws_noise=0.)\n", - "\n", - "n_row = power_baseline.shape[0]\n", - "fig, ax = plt.subplots(figsize=(12, 4))\n", - "ax.plot(list(range(n_row)), power_baseline_noisy[:, single_wake_idx])\n", - "ax.plot(np.arange(0, n_row, 10), df_baseline_noisy[\"pow_%03d\" % single_wake_idx])\n", - "ax.plot(np.arange(0, n_row, 10), df_baseline[\"pow_%03d\" % single_wake_idx], \"k--\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_row = power_baseline.shape[0]\n", - "fig, ax = plt.subplots(figsize=(12, 4))\n", - "ax.plot(list(range(n_row)), power_wakesteering_noisy[:, single_wake_idx])\n", - "ax.plot(np.arange(0, n_row, 10), df_wakesteering_noisy[\"pow_%03d\" % single_wake_idx])\n", - "ax.plot(np.arange(0, n_row, 10), df_wakesteering[\"pow_%03d\" % single_wake_idx], \"k--\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_row = power_baseline.shape[0]\n", - "fig, ax = plt.subplots(figsize=(12, 4))\n", - "ax.plot(\n", - " list(range(n_row)),\n", - " power_wakesteering_noisy[:, single_wake_idx] - power_baseline_noisy[:, single_wake_idx],\n", - ")\n", - "ax.plot(\n", - " np.arange(0, n_row, 10),\n", - " df_wakesteering_noisy[\"pow_%03d\" % single_wake_idx]\n", - " - df_baseline_noisy[\"pow_%03d\" % single_wake_idx],\n", - ")\n", - "ax.plot(\n", - " np.arange(0, n_row, 10),\n", - " df_wakesteering[\"pow_%03d\" % single_wake_idx] - df_baseline[\"pow_%03d\" % single_wake_idx],\n", - " \"k--\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Similarly average the yaw angle matrices along every 10\n", - "yaw_angles_reshaped = yaw_angles.reshape(\n", - " total_number_of_points // points_per_group, points_per_group, yaw_angles.shape[1]\n", - ")\n", - "yaw_angles = np.mean(yaw_angles_reshaped, axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the yaw angles\n", - "yaw_angles_base = 0 * yaw_angles\n", - "yaw_angels_wakesteering = yaw_angles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tune Wake Expansion (First Index) to baseline (non-noisy) data" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "wake_expansion_rates = np.arange(start=0.010, stop=0.050, step=0.001)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[16.49475351 16.32929173 16.16761604 16.00982212 15.84299221 15.67823304\n", - " 15.51752308 15.35646901 15.19782758 15.03868464 14.87974144 14.72281613\n", - " 14.5682697 14.41617994 14.26546445 14.11709457 13.97165818 13.82976913\n", - " 13.69041331 13.55314438 13.41630418 13.28081246 13.14841388 13.01886068\n", - " 12.89154384 12.76397048 12.6374796 12.51256951 12.38920503 12.26796275\n", - " 12.14553334 12.02512474 11.90695745 11.79161179 11.67724949 11.56302711\n", - " 11.45080725 11.34117785 11.23375237 11.12797132] 14.416179942373729\n" - ] - } - ], - "source": [ - "df_scada = df_baseline.copy()\n", - "floris_wake_losses, scada_wake_loss = ft.sweep_velocity_model_parameter_for_overall_wake_losses(\n", - " parameter=[\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " value_candidates=wake_expansion_rates,\n", - " df_scada_in=df_scada,\n", - " fm_in=fm_emg,\n", - " param_idx=0,\n", - " ref_turbines=[ref_idx],\n", - " test_turbines=[single_wake_idx],\n", - ")\n", - "print(floris_wake_losses, scada_wake_loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Wake Loss')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(\n", - " floris_wake_losses, scada_wake_loss, wake_expansion_rates, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Wake Expansion Parameter\")\n", - "ax.set_ylabel(\"Percent Wake Loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Apply the best fit\n", - "fm_emg.set_param(\n", - " [\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " param_idx=0,\n", - " value=best_param,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.02299999999999999, 0.008]\n" - ] - } - ], - "source": [ - "print(\n", - " fm_emg.core.as_dict()[\"wake\"][\"wake_velocity_parameters\"][\"empirical_gauss\"][\n", - " \"wake_expansion_rates\"\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retune with noisy data" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Make a copy for the noisy fi_emg model\n", - "fm_emg_noisy = fm_emg.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.02299999999999999, 0.008]\n" - ] - } - ], - "source": [ - "print(\n", - " fm_emg_noisy.core.as_dict()[\"wake\"][\"wake_velocity_parameters\"][\"empirical_gauss\"][\n", - " \"wake_expansion_rates\"\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[16.5047578 16.33840156 16.17508838 16.01278983 15.85010167 15.68895898\n", - " 15.52795795 15.36496802 15.20388501 15.0446626 14.88483569 14.72827629\n", - " 14.57498183 14.42388355 14.27338504 14.12518506 13.97964414 13.83687472\n", - " 13.69638244 13.55834432 13.42250886 13.28898623 13.15762807 13.02764229\n", - " 12.89910225 12.77192524 12.64661226 12.52267673 12.40011365 12.27818225\n", - " 12.15705457 12.03775175 11.92036999 11.80451218 11.6892954 11.57599769\n", - " 11.46453665 11.35481812 11.2469225 11.14067457] 14.403171583603866\n" - ] - } - ], - "source": [ - "df_scada = df_baseline_noisy.copy()\n", - "floris_wake_losses, scada_wake_loss = ft.sweep_velocity_model_parameter_for_overall_wake_losses(\n", - " parameter=[\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " value_candidates=wake_expansion_rates,\n", - " df_scada_in=df_scada,\n", - " fm_in=fm_emg_noisy,\n", - " param_idx=0,\n", - " ref_turbines=[ref_idx],\n", - " test_turbines=[single_wake_idx],\n", - ")\n", - "print(floris_wake_losses, scada_wake_loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Wake Loss')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(\n", - " floris_wake_losses, scada_wake_loss, wake_expansion_rates, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Wake Expansion Parameter\")\n", - "ax.set_ylabel(\"Percent Wake Loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Apply the best fit\n", - "fm_emg_noisy.set_param(\n", - " [\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " param_idx=0,\n", - " value=best_param,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Identify wd_std" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "min_wd = np.floor(np.min([df_baseline.wd.min(), df_baseline_noisy.wd.min()]))\n", - "max_wd = np.ceil(np.max([df_baseline.wd.max(), df_baseline_noisy.wd.max()]))\n", - "\n", - "min_ws = np.floor(np.min([df_baseline.ws.min(), df_baseline_noisy.ws.min()]))\n", - "max_ws = np.ceil(np.max([df_baseline.ws.max(), df_baseline_noisy.ws.max()]))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 12:42:01\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 168 cases.\n", - "\u001b[32m2024-10-16 12:42:01\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" - ] - } - ], - "source": [ - "# Make approximate tables\n", - "wd_array = np.arange(min_wd, max_wd, 1.0)\n", - "ws_array = np.arange(min_ws, max_ws, 1.0)\n", - "\n", - "df_approx = ftools.calc_floris_approx_table(\n", - " fm_emg, wd_array=wd_array, ws_array=ws_array, ti_array=np.array([0.1])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 12:42:01\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 168 cases.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" - ] - } - ], - "source": [ - "df_approx_noisy = ftools.calc_floris_approx_table(\n", - " fm_emg_noisy, wd_array=wd_array, ws_array=ws_array, ti_array=np.array([0.1])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Select the values to check\n", - "wd_std_range = [0, 1, 2, 3, 4, 5]" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.005 seconds.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.003 seconds.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:02\u001b[0m Finished interpolation in 0.004 seconds.\n" - ] - } - ], - "source": [ - "# Evaluate\n", - "df_scada = df_baseline.copy()\n", - "er_error, df_list = ft.sweep_wd_std_for_er(\n", - " wd_std_range, df_scada, df_approx, ref_turbines=[ref_idx], test_turbines=[single_wake_idx]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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iIiI798MPYiNx/frAgAFyV2N+DDdERER2zlEaifUYboiIiOzYuXPAzp2O0Uisx3BDRERkx1atEv/t1w/w95e3FkthuCEiIrJTjtZIrCdruFm+fDmCg4Ph6ekJT09PhISEYNu2bQ99zbfffovmzZvDzc0NrVu3xtatWy1ULRERkW1JTAT++Qfw8XGMRmI9WcONr68v5s+fj7S0NBw+fBi9e/fGc889h5MnT5a6/L59+zB8+HCMGTMGR48eRXh4OMLDw3HixAkLV05ERGT9ijcSOzvLW4slyRpuBg4ciKeffhqBgYFo2rQp3n//fVSrVg0HDhwodfklS5agX79+mDp1Klq0aIE5c+agffv2WLp0qYUrJyIism5nzwK7djlWI7Ge1fTcaLVabNy4Efn5+QgJCSl1mf3796NPnz5G88LCwrB//35LlEhERGQz9I3E/fsDDRvKW4ulyb6T6vjx4wgJCUFhYSGqVauGzZs3IygoqNRls7Ky4OXlZTTPy8sLWVlZZa5frVZDrVYbHufm5gIANBoNNBqNBO/gHo0GKsOkBpBy3WRE/32T9PtHJXCcLYPjbDmONNZqNbB2rTMABcaMuQuNRrDYts01zqasT/Zw06xZM6SnpyMnJwebNm1CZGQkdu/eXWbAMVVcXBxiY2NLzE9KSoK7u7sk2wAAZWEh9HeP37VrF7RubpKtm0qXnJwsdwkOgeNsGRxny3GEsf7f/xrg+vWOqF37DoBkbN1quXCjJ/U4FxQUlHtZ2cONi4sLAgICAAAdOnTAoUOHsGTJEqzUd0EV4+3tjezsbKN52dnZ8Pb2LnP9MTExiI6ONjzOzc2Fn58f+vbtC09PT4neBYD8fMNk7969oapRQ7p1kxGNRoPk5GSEhoZC5QiX2pQJx9kyOM6W40hjvXixEgDw2msuGDiwv0W3ba5x1h95KQ/Zw82DdDqd0WGk4kJCQrBz505MmjTJMC85ObnMHh0AcHV1haura4n5KpVK2g93sXVJvm4qFcfZMjjOlsFxthx7H+s//wRSUgAnJ+DVV5VQqZSy1CH1OJuyLlnDTUxMDPr374+GDRvi9u3bSEhIQEpKCnbs2AEAiIiIQIMGDRAXFwcAmDhxInr06IGFCxdiwIAB2LhxIw4fPozPPvtMzrdBRERkNRy5kVhP1nBz7do1REREIDMzE9WrV0dwcDB27NiB0NBQAEBGRgacnO6f0NW1a1ckJCRgxowZeOeddxAYGIjExES0atVKrrdARERkNdRqID5enHakKxI/SNZw8/nnnz/0+ZSUlBLzhgwZgiFDhpipIiIiItv1/ffA9euAr6+458ZRWc11boiIiKhy9F0ajnZF4gcx3BAREdmBM2fuNxKPGSN3NfJiuCEiIrID+kbip58G/PzkrUVuDDdEREQ2rrCQjcTFMdwQERHZuO+/B27cYCOxHsMNERGRjdM3Eo8dCyjluWafVWG4ISIismGnTwO7d7ORuDiGGyIiIhumbyQeMEA8LEUMN0RERDaLjcSlY7ghIiKyUd99B/z7r3jqd79+cldjPRhuiIiIbBQbiUvHcENERGSDTp0CUlPZSFwahhsiIiIbpG8kfuYZoEEDeWuxNgw3RERENqawEFi3TpxmI3FJDDdEREQ2ZtMmsZG4YUMgLEzuaqwPww0REZGNYSPxwzHcEBER2ZA//gD+9z8x1LCRuHQMN0RERDZE30g8cCDg4yNvLdaK4YaIiMhG3Llzv5H41VflrcWaMdwQERHZiE2bgJs3AX9/oG9fuauxXgw3RERENoKNxOXDcENERGQDTp4E9uwRQ80rr8hdjXVjuCEiIrIB+kbiZ59lI/GjMNwQERFZOTYSm4bhhoiIyMp9+y1w6xYbicuL4YaIiMjK6RuJx40T7wJOD8chIiIismInTwJ79wLOzmwkLi+GGyIiIiu2cqX477PPAvXry1uLrWC4ISIislIFBcAXX4jTbCQuP4YbIiIiK6VvJG7UCAgNlbsa28FwQ0REZKXYSFwxHCoiIiIrdOIEsG8fG4krguGGiIjICukbiZ97DvD2lrcWWyNruImLi0OnTp3g4eGBevXqITw8HGfOnHnoa+Lj46FQKIy+3NzcLFQxERGR+bGRuHJkDTe7d+/GhAkTcODAASQnJ0Oj0aBv377Iz89/6Os8PT2RmZlp+Lp48aKFKiYiIjK/b74BcnKAxo2BPn3krsb2OMu58e3btxs9jo+PR7169ZCWlobu3buX+TqFQgFv7qMjIiI7xUbiypE13DwoJycHAFCrVq2HLpeXlwd/f3/odDq0b98e8+bNQ8uWLUtdVq1WQ61WGx7n5uYCADQaDTQajUSVA9BooDJMagAp101G9N83Sb9/VALH2TI4zpZjK2N9/Diwf78Kzs4CXnrprs39OjHXOJuyPoUgCIKkW68gnU6HZ599Frdu3cKePXvKXG7//v04e/YsgoODkZOTgwULFiA1NRUnT56Er69vieVnz56N2NjYEvMTEhLg7u4uWf3KwkI8M2wYAGDLxo3Qsg+IiIgq4LPPWmPr1ibo2vUKpk07LHc5VqOgoAAjRoxATk4OPD09H7qs1YSb119/Hdu2bcOePXtKDSll0Wg0aNGiBYYPH445c+aUeL60PTd+fn64fv36IwfHJPn5UNWsCQAouHYNqho1pFs3GdFoNEhOTkZoaChUKtWjX0AVwnG2DI6z5djCWBcUAA0bOiM3V4GtW++iTx+r+BVtEnONc25uLurUqVOucGMVh6WioqKwZcsWpKammhRsAEClUqFdu3Y4d+5cqc+7urrC1dW11NdJ+uEuti7J102l4jhbBsfZMjjOlmPNY/3990BuLtCkCRAW5mzT/TZSj7Mp65J12ARBQFRUFDZv3oxdu3ahcePGJq9Dq9Xi+PHjqM+7iRERkY3TNxK/+iobiSvDpKG7e/cu1q9fj+zsbEk2PmHCBHz55ZdISEiAh4cHsrKykJWVhTt37hiWiYiIQExMjOHxe++9h6SkJPz11184cuQIXnrpJVy8eBFjx46VpCYiIiI5/P47cOCAeEXiUaPkrsa2mXRYytnZGa+99hpOnTolycaXL18OAOjZs6fR/LVr12LUve9sRkYGnIrF15s3b2LcuHHIyspCzZo10aFDB+zbtw9BQUGS1ERERCQH/RWJBw0CvLzkrcXWmdxz07lzZ6Snp8Pf37/SGy9PL3NKSorR40WLFmHRokWV3jYREZG1yM8HvvxSnOYViSvP5HDzxhtvIDo6GpcuXUKHDh1QtWpVo+eDg4MlK46IiMgRfP212Ej82GNA795yV2P7TA43w+5dy+XNN980zFMoFBAEAQqFAlqtVrrqiIiIHAAbiaVlcri5cOGCOeogIiJySMeOAQcPilcUYSOxNEwON1L02hAREZGoeCNxvXry1mIvKnQRv/Pnz2Px4sWGs6aCgoIwceJEPPbYY5IWR0REZM/YSGweJh/Z27FjB4KCgvDbb78hODgYwcHBOHjwIFq2bInk5GRz1EhERGSXNm4Ebt8GAgKAXr3krsZ+mLznZvr06Zg8eTLmz59fYv7bb7+N0NBQyYojIiKyZ2wkNg+Th/LUqVMYM2ZMifmvvPIK/vjjD0mKIiIisnfp6cBvv4mNxJGRcldjX0wON3Xr1kV6enqJ+enp6ajHTigiIqJy0TcSDx7MRmKpmXxYaty4cXj11Vfx119/oWvXrgCAvXv34oMPPkB0dLTkBRIREdmbvDxgwwZxmo3E0jM53Lz77rvw8PDAwoULDTe09PHxwezZs40u7EdERESl0zcSBwaykdgcTAo3d+/eRUJCAkaMGIHJkyfj9u3bAAAPDw+zFEdERGSPijcSKxTy1mKPTOq50d8VvLCwEIAYahhsiIiIyu/oUeDQIcDFhY3E5mJyQ3Hnzp1x9OhRc9RCRERk94o3EtetK28t9qpCdwWfMmUKLl++zLuCExERmaB4I/H48fLWYs94V3AiIiIL+eorMeA0bQr06CF3NfaLdwUnIiKyEP0hKTYSm5dJ4Uaj0aB3797YsmULWrRoYa6aiIiI7E5amvjFRmLzM6mhWKVSGc6UIiIiovLTn/79/PNAnTry1mLvTD5basKECfjggw9w9+5dc9RDRERkd27fBhISxGk2EpufyT03hw4dws6dO5GUlITWrVuXOFvq+++/l6w4IiIie6BvJG7WDOjeXe5q7J/J4aZGjRp4/vnnzVELERGRXWIjsWWZHG7Wrl1rjjqIiIjsUloacOSI2EgcESF3NY7B5J4bQLzH1C+//IKVK1ca7i919epV5OXlSVocERGRrdPvtXnhBTYSW4rJe24uXryIfv36ISMjA2q1GqGhofDw8MAHH3wAtVqNFStWmKNOIiIim8NGYnmYvOdm4sSJ6NixI27evIkqVaoY5g8aNAg7d+6UtDgiIiJblpAA5OcDzZsDTz4pdzWOw+Q9N//73/+wb98+uLi4GM1v1KgRrly5IllhREREtkwQ2EgsF5P33Oh0ulLvH3X58mV4eHhIUhQREZGtS0sDjh4FXF3ZSGxpJoebvn37YvHixYbHCoUCeXl5mDVrFp5++mkpayMiIrJZxRuJa9eWtxZHY/JhqYULFyIsLAxBQUEoLCzEiBEjcPbsWdSpUwdfffWVOWokIiKyKbm54oX7ADYSy8HkcOPr64tjx47h66+/xrFjx5CXl4cxY8Zg5MiRRg3GREREjkrfSNyiBfDEE3JX43hMDjcA4OzsjJEjR2LkyJFS10NERGTT2EgsvwpdxI+IiIhKd/gwkJ7ORmI5yRpu4uLi0KlTJ3h4eKBevXoIDw/HmTNnHvm6b7/9Fs2bN4ebmxtat26NrVu3WqBaIiKiR9PvtRkyBKhVS95aHJWs4Wb37t2YMGECDhw4gOTkZGg0GvTt2xf5+fllvmbfvn0YPnw4xowZg6NHjyI8PBzh4eE4ceKEBSsnIiIqiY3E1qFCPTdS2b59u9Hj+Ph41KtXD2lpaehexj3hlyxZgn79+mHq1KkAgDlz5iA5ORlLly7lrR+IiEhWGzYABQViI3G3bnJX47hkDTcPysnJAQDUesh+vP379yM6OtpoXlhYGBITE0tdXq1WQ61WGx7n5uYCADQaDTQaTSUrLkajgcowqQGkXDcZ0X/fJP3+UQkcZ8vgOFuOucdaEIAVK5wBKDB2rBZ37+rMsh1rZ65xNmV95Qo3NWvWhKKc7d7//vtvuTdenE6nw6RJk9CtWze0atWqzOWysrLg5eVlNM/LywtZWVmlLh8XF4fY2NgS85OSkuDu7l6hWkujLCzEM/emd+3aBa2bm2TrptIlJyfLXYJD4DhbBsfZcsw11n/+WQO//94DLi5a1Ku3A1u3OnZglXqcCwoKyr1sucJN8SsS37hxA3PnzkVYWBhCQkIAiHtTduzYgXfffde0SouZMGECTpw4gT179lR4HaWJiYkx2tOTm5sLPz8/9O3bF56entJtqFifUO/evaGqUUO6dZMRjUaD5ORkhIaGQqVSPfoFVCEcZ8vgOFuOucc6MVEJABgyRIGhQ0MlX7+tMNc464+8lEe5wk1kZKRh+vnnn8d7772HqKgow7w333wTS5cuxS+//ILJkyebUKooKioKW7ZsQWpqKnx9fR+6rLe3N7Kzs43mZWdnw9vbu9TlXV1d4erqWmK+SqWS9sNdbF2Sr5tKxXG2DI6zZXCcLcccY52TA3zzjTj9+utOUKl4pRWpx9mUdZk8+jt27EC/fv1KzO/Xrx9++eUXk9YlCAKioqKwefNm7Nq1C40bN37ka0JCQrBz506jecnJyYa9SERERJambyQOCgK6dpW7GjI53NSuXRs//PBDifk//PADapt4Z7AJEybgyy+/REJCAjw8PJCVlYWsrCzcuXPHsExERARiYmIMjydOnIjt27dj4cKFOH36NGbPno3Dhw8b7UkiIiKylOJXJB4/nlcktgYmny0VGxuLsWPHIiUlBV26dAEAHDx4ENu3b8eqVatMWtfy5csBAD179jSav3btWowaNQoAkJGRASen+xmsa9euSEhIwIwZM/DOO+8gMDAQiYmJD21CJiIiMpfffgN+/x1wcwNeflnuagioQLgZNWoUWrRogU8++QTff/89AKBFixbYs2ePIeyUlyAIj1wmJSWlxLwhQ4ZgyJAhJm2LiIjIHPR7bV58EahZU95aSFSh69x06dIFGzZskLoWIiIim3LrFrBxozjNKxJbjwq1c58/fx4zZszAiBEjcO3aNQDAtm3bcPLkSUmLIyIismYbNgB37gAtWwI8r8V6mBxudu/ejdatW+PgwYP47rvvkJeXBwA4duwYZs2aJXmBRERE1oiNxNbL5HAzffp0zJ07F8nJyXBxcTHM7927Nw4cOCBpcURERNbq4EHg+HE2Elsjk8PN8ePHMWjQoBLz69Wrh+vXr0tSFBERkbXT77UZOhTgRemti8nhpkaNGsjMzCwx/+jRo2jQoIEkRREREVmzW7eAr78Wp9lIbH1MDjfDhg3D22+/jaysLCgUCuh0OuzduxdvvfUWIiIizFEjERGRVfnyS7GRuHVr4PHH5a6GHmRyuJk3bx6aN28OPz8/5OXlISgoCN27d0fXrl0xY8YMc9RIRERkNYo3Er/6KhuJrZFJ17kRBAFZWVn45JNPMHPmTBw/fhx5eXlo164dAgMDzVUjERGR1ThwADhxAqhSBXjpJbmrodKYHG4CAgJw8uRJBAYGws/Pz1x1ERERWSU2Els/kw5LOTk5ITAwEDdu3DBXPURERFbr5k02EtsCk3tu5s+fj6lTp+LEiRPmqIeIiMhqffklUFgIBAcDJt5OkSzI5HtLRUREoKCgAG3atIGLiwuqVKli9Py///4rWXFERETWgo3EtsPkcLN48WIzlEFERGTd9u8HTp5kI7EtMDncREZGmqMOIiIiq6bfazNsGFC9ury10MOZHG6KKywsRFFRkdE8T0/PShVERERkbW7eBL75RpxmI7H1M7mhOD8/H1FRUahXrx6qVq2KmjVrGn0RERHZmy++EBuJ27QBOneWuxp6FJPDzbRp07Br1y4sX74crq6uWL16NWJjY+Hj44P169ebo0YiIiLZsJHY9ph8WOqnn37C+vXr0bNnT4wePRpPPvkkAgIC4O/vjw0bNmDkyJHmqJOIiEgW+/YBf/wBuLsD/BVnG0zec/Pvv/+iSZMmAMT+Gv2p30888QRSU1OlrY6IiEhmbCS2PSaHmyZNmuDChQsAgObNm+Obex1WP/30E2rwOtRERGRH/v2XjcS2yORwM3r0aBw7dgwAMH36dCxbtgxubm6YPHkypk6dKnmBREREcvniC0CtBtq2BTp1krsaKi+Te24mT55smO7Tpw9Onz6NtLQ0BAQEIDg4WNLiiIiI5MJGYttVqevcAIC/vz/8/f2lqIWIiMhq7N0LnDoFVK3KRmJbY3K4ee+99x76/MyZMytcDBERkbXQ77UZPhzg9Wlti8nhZvPmzUaPNRoNLly4AGdnZzz22GMMN0REZPNu3AC+/VacfvVVeWsh05kcbo4ePVpiXm5uLkaNGoVBgwZJUhQREZGc9I3E7doBHTvKXQ2ZyuSzpUrj6emJ2NhYvPvuu1KsjoiISDZsJLZ9koQbAMjJyUFOTo5UqyMiIpLFnj3A6dNiI/GIEXJXQxVh8mGpTz75xOixIAjIzMzEF198gf79+0tWGBERkRz0e21GjGAjsa0yOdwsWrTI6LGTkxPq1q2LyMhIxMTESFYYERGRpd24AWzaJE6zkdh2mRxu9LdeICIisjfr14uNxO3bs5HYlknWc1MRqampGDhwIHx8fKBQKJCYmPjQ5VNSUqBQKEp8ZWVlWaZgIiKyWw82EpPtMnnPzaBBg6AoZ+v4999//9Dn8/Pz0aZNG7zyyisYPHhwuWs4c+YMPIsdCK1Xr165X0tERFSa1FTgzBmgWjU2Ets6k8NN9erVsXnzZlSvXh0d7+2zS0tLQ05ODsLDw8sdfACgf//+FWpCrlevHu9ATkREkvrsM/HfESMADw95a6HKMTnceHl54cUXX8SKFSugVCoBAFqtFm+88QY8PT3x0UcfSV7kg9q2bQu1Wo1WrVph9uzZ6Natm9m3SURE9uv6dTYS2xOTw82aNWuwZ88eQ7ABAKVSiejoaHTt2tWs4aZ+/fpYsWIFOnbsCLVajdWrV6Nnz544ePAg2rdvX+pr1Go11Gq14XFubi4A8bYRGo1GuuI0GqgMkxpAynWTEf33TdLvH5XAcbYMjrPlPGys1651QlGREu3b6xAcrOWP8Eow12falPWZHG7u3r2L06dPo1mzZkbzT58+DZ1OZ+rqTNKsWTOj7Xbt2hXnz5/HokWL8MUXX5T6mri4OMTGxpaYn5SUBHd3d8lqUxYW4pl707t27YLWzU2ydVPpkpOT5S7BIXCcLYPjbDkPjrUgAEuW9AbggS5dfsfWrRflKczOSP2ZLigoKPeyJoeb0aNHY8yYMTh//jw6d+4MADh48CDmz5+P0aNHm7q6SuvcuTP27NlT5vMxMTGIjo42PM7NzYWfnx/69u1r1JRcafn5hsnevXtDxZ4gs9FoNEhOTkZoaChUKtWjX0AVwnG2DI6z5ZQ11rt3K3DlijOqVRMwd25LeHi0lLFK22euz7T+yEt5mBxuFixYAG9vbyxcuBCZmZkAxMNFU6dOxZQpU0xdXaWlp6ejfv36ZT7v6uoKV1fXEvNVKpW0P0iKrUvydVOpOM6WwXG2DI6z5Tw41mvWiP+OHKlArVr8HkhF6s+0KesyOdw4OTlh2rRpmDZtmiFFVXQPSF5eHs6dO2d4fOHCBaSnp6NWrVpo2LAhYmJicOXKFaxfvx4AsHjxYjRu3BgtW7ZEYWEhVq9ejV27diEpKalC2yciIsd2/Trw3XfiNBuJ7YfJ4ebOnTsQBAHu7u7w9PTExYsXsWbNGgQFBaFv374mrevw4cPo1auX4bH+8FFkZCTi4+ORmZmJjIwMw/NFRUWYMmUKrly5And3dwQHB+OXX34xWgcREVF5rVsHFBWJVyMu47wUskEmh5vnnnsOgwcPxmuvvYZbt26hc+fOcHFxwfXr1/Hxxx/j9ddfL/e6evbsCUEQynw+Pj7e6LF+jxEREVFlCcL9a9uMHy9vLSQtk2+/cOTIETz55JMAgE2bNsHb2xsXL17E+vXrS9wxnIiIyFqlpAB//ilesG/YMLmrISmZHG4KCgrgce/SjUlJSRg8eDCcnJzw+OOP4+JFnj5HRES2Qb/XZuRI8ZYLZD9MDjcBAQFITEzEpUuXsGPHDkOfzbVr16Q9tZqIiMhM/vmHjcT2zORwM3PmTLz11lto1KgRunTpgpCQEADiXpx27dpJXiAREZHU1q0TLyTfqRPAX132x+SG4hdeeAFPPPEEMjMz0aZNG8P8p556CoMGDZK0OCIiIqmxkdj+mRxuAMDb2xve3t5G8/RXKyYiIrJmKSkKnD0rNhIPHSp3NWQOJh+WIiIismWrV4u/+l56iY3E9orhhoiIHMatWy5ITFQAYCOxPWO4ISIih/Hrrw2h0SjQuTPQtq3c1ZC5MNwQEZFD0OmApCR/AGwktncMN0RE5BBSUhTIzKwGT0+BjcR2juGGiIgcwiefiL/ynn1Wh6pVZS6GzIrhhoiI7N6iRcDWrWIjcUKCEz7/XOaCyKwYboiIyK5dvAhERwOAGG50OgXGjwcuX5a1LDIjhhsiIrJbWi0wblzp88+ds3w9ZBkMN0REZJe0WuCVV4Dk5JLPKZVAQIDlayLLYLghIiK7o9MBY8cC69eLQea11wClUgAg/rtyJeDrK3ORZDYMN0REZFf0wSY+Xgw2X30FLF8OnD17F3Pm7MHZs3cxZozcVZI5MdwQEZHd0OnE2yqsXQs4OQEbNgBDhojP+foCrVvf4B4bB8BwQ0REdkGnE688/Pnn94MNL9bnmBhuiIjI5ul0Yl/N6tVisPniC2DYMLmrIrkw3BARkU3T6YA33gBWrRKDzfr1wIgRcldFcmK4ISIimyUIwIQJwMqVgEIBrFsHjBwpd1UkN4YbIiKySYIAREUBK1aIwSY+HnjpJbmrImvAcENERDZHEID//Af49FMx2KxdC0REyF0VWQuGGyIisimCAEycCCxbJgabNWuAyEi5qyJrwnBDREQ2QxCASZOA//s/MdisXg2MGiV3VWRtGG6IiMgmCAIweTLwySfi41WrxHtHET2I4YaIiKyeIABTpgBLloiPV60Cb6FAZWK4ISIiqyYIwNSpwKJF4uOVK8V7RxGVheGGiIisliAA06YBCxeKj1esEO8dRfQwDDdERGSVBAGYPh1YsEB8/Omn4r2jiB6F4YaIiKyOIAAxMcCHH4qPly0DXn9d3prIdsgablJTUzFw4ED4+PhAoVAgMTHxka9JSUlB+/bt4erqioCAAMTHx5u9TiIishxBAP77X+CDD8THS5eK944iKi9Zw01+fj7atGmDZcuWlWv5CxcuYMCAAejVqxfS09MxadIkjB07Fjt27DBzpUREZAmCAMyYAcTFiY8/+US8dxSRKZzl3Hj//v3Rv3//ci+/YsUKNG7cGAvvdZa1aNECe/bswaJFixAWFmauMomIyAIEAZg5E5g3T3y8eLF4iwUiU8kabky1f/9+9OnTx2heWFgYJk2aVOZr1Go11Gq14XFubi4AQKPRQKPRSFecRgOVYVIDSLluMqL/vkn6/aMSOM6WwXG+LzbWCe+/rwQALFigxRtv6CT9UcqxtgxzjbMp67OpcJOVlQUvLy+jeV5eXsjNzcWdO3dQpUqVEq+Ji4tDbGxsiflJSUlwd3eXrDZlYSGeuTe9a9cuaN3cJFs3lS45OVnuEhwCx9kyHH2cN25sho0bmwMAXnnlOAIC/sLWrebZlqOPtaVIPc4FBQXlXtamwk1FxMTEIDo62vA4NzcXfn5+6Nu3Lzw9PaXbUH6+YbJ3795Q1agh3brJiEajQXJyMkJDQ6FSqR79AqoQjrNlcJyBuXOdsHGjuMfmgw+0mDy5OYDmkm+HY20Z5hpn/ZGX8rCpcOPt7Y3s7GyjednZ2fD09Cx1rw0AuLq6wtXVtcR8lUol7Ye72LokXzeViuNsGRxny3DUcZ4zB3jvPXH6o4+At95SAlCadZuOOtaWJvU4m7Ium7rOTUhICHbu3Gk0Lzk5GSEhITJVREREFfX++2IDMSCe9v3WW/LWQ/ZD1nCTl5eH9PR0pKenAxBP9U5PT0dGRgYA8ZBSRESEYfnXXnsNf/31F6ZNm4bTp0/j008/xTfffIPJkyfLUT4REVVQXJx4yrd+eto0eesh+yJruDl8+DDatWuHdu3aAQCio6PRrl07zLwX5TMzMw1BBwAaN26Mn3/+GcnJyWjTpg0WLlyI1atX8zRwIiIbMn8+8M474vS8eeItFoikJGvPTc+ePSEIQpnPl3b14Z49e+Lo0aNmrIqIiMzlww/F2yoAwNy596eJpGRTPTdERGS7PvoIePttcXrOHPEWC0TmwHBDRERmt3Dh/b6a2Nj7/TZE5sBwQ0REZvXxx/fPhJo9+/4ZUkTmwnBDRERms3gxMGWKOD1zJjBrlqzlkINguCEiIrNYsgTQX6ljxgxxrw2RJTDcEBGR5P7v/wD9PY3/+1/xKsQKhawlkQNhuCEiIkktXQq8+aY4HRMjnhnFYEOWxHBDRESSWbYM+M9/xOnp08VbLDDYkKUx3BARkSSWLweiosTpadPEqw8z2JAcGG6IiKjSVqwA3nhDnH7rLfEWCww2JBeGGyIiqpTPPgNef12cnjJFvMUCgw3JieGGiIgqbNUqYPx4cXryZPEWCww2JDeGGyIiqpDPPwdefVWcnjRJvMUCgw1ZA4YbIiIy2Zo1wLhx4vSbb4q3WGCwIWvBcENERCZZuxYYOxYQBPG078WLGWzIujDcEBFRua1bB4wZIwabCRPEWyww2JC1YbghIqJyWb8eGD1aDDZvvCHeYoHBhqwRww0RET3Sl18Co0aJweb118VbLDDYkLViuCEioofasAGIjBSDzfjxDDZk/RhuiIioTAkJQEQEoNOJp31/+ingxN8cZOX4ESUiolJ99RXw8stisBk7Vrx3FIMN2QJ+TImIqISvvwZeekkMNmPGACtXMtiQ7eBHlYiIjHzzDTBypBhsRo8W7x3FYEO2hB9XIiIy+PZbYMQIQKsVz45avZrBhmwPP7JERAQA+O47YPhwMdhERjLYkO3ix5aIiPD998CwYWKwefll8aaYSqXcVRFVDMMNEZGD27wZGDoUuHtXbCJeu5bBhmwbww0RkQP74QfgxRfFYDNiBBAfz2BDto/hhojIQf34IzBkiBhshg8Xb4rJYEP2gOGGiMgB/fQT8MILgEYj9tqsXw84O8tdFZE0GG6IiBzMli3A88+LwWboUOCLLxhsyL4w3BAROZCff74fbIYMEe/2zWBD9obhhojIQWzdCgweDBQViYekNmxgsCH7ZBXhZtmyZWjUqBHc3NzQpUsX/Pbbb2UuGx8fD4VCYfTl5uZmwWqJiGzP9u3AoEFisHn+efFu3yqV3FURmYfs4ebrr79GdHQ0Zs2ahSNHjqBNmzYICwvDtWvXynyNp6cnMjMzDV8XL160YMVERLZlxw4gPFwMNoMGiXf7ZrAheyZ7uPn4448xbtw4jB49GkFBQVixYgXc3d2xZs2aMl+jUCjg7e1t+PLy8rJgxUREtiMpCXjuOUCtFgPOxo0MNmT/ZD3aWlRUhLS0NMTExBjmOTk5oU+fPti/f3+Zr8vLy4O/vz90Oh3at2+PefPmoWXLlqUuq1aroVarDY9zc3MBABqNBhqNRqJ3AkCjgcowqRG79cgs9N83Sb9/VALH2TLMOc6//KLA4MFKqNUKDByow5dfaqFQOO6PJ36mLcNc42zK+mQNN9evX4dWqy2x58XLywunT58u9TXNmjXDmjVrEBwcjJycHCxYsABdu3bFyZMn4evrW2L5uLg4xMbGlpiflJQEd3d3ad4IAGVhIZ65N71r1y5o2QdkdsnJyXKX4BA4zpYh9TgfO1YX77/fBUVFCnTqlImIiEP45RdB0m3YKn6mLUPqcS4oKCj3sgpBEGT7tF+9ehUNGjTAvn37EBISYpg/bdo07N69GwcPHnzkOjQaDVq0aIHhw4djzpw5JZ4vbc+Nn58frl+/Dk9PT2neCADk50NVsyYAoODaNahq1JBu3WREo9EgOTkZoaGhUHH/utlwnC3DHOO8a5cC4eFKFBYq8PTTOnz9tRaurpKs2qbxM20Z5hrn3Nxc1KlTBzk5OY/8/S3rnps6depAqVQiOzvbaH52dja8vb3LtQ6VSoV27drh3LlzpT7v6uoK11L+V6tUKmk/3MXWJfm6qVQcZ8vgOFuGVOP8669i03BhITBgAPDdd05wdZW9vdKq8DNtGVKPsynrkvUT7+Ligg4dOmDnzp2GeTqdDjt37jTak/MwWq0Wx48fR/369c1VJhGRTUhJEQPNnTvA008D330H7rEhhyT75Zuio6MRGRmJjh07onPnzli8eDHy8/MxevRoAEBERAQaNGiAuLg4AMB7772Hxx9/HAEBAbh16xY++ugjXLx4EWPHjpXzbRARyWr37vvBpn9/BhtybLKHm6FDh+Kff/7BzJkzkZWVhbZt22L79u2GJuOMjAw4Od3fwXTz5k2MGzcOWVlZqFmzJjp06IB9+/YhKChIrrdARCSr1FRxT01BARAWBnz/PcBzGsiRyR5uACAqKgpRUVGlPpeSkmL0eNGiRVi0aJEFqiIisn7/+9/9YNO3L5CYyGBDxC4zIiIbtWePeAgqPx8IDWWwIdJjuCEiskF7994PNn36AD/8AFSpIndVRNaB4YaIyMbs2wf06wfk5QFPPcVgQ/QghhsiIhuyf//9YNO7N/Djj4CEF1snsgsMN0RENuLAAfFsqNu3gV69gJ9+YrAhKg3DDRGRDTh48H6w6dGDwYboYRhuiIis3G+/iad55+YC3bsDP/8MVK0qd1VE1ovhhojIih0+fD/YPPkkgw1ReTDcEBFZqbQ08fo1OTnAE08AW7cC1arJXRWR9WO4ISKyQkeOiNevuXUL6NaNwYbIFAw3RERWpniw6doV2LYN8PCQuyoi28FwQ0RkRY4eFYPNzZtASAiDDVFFMNwQEVmJ9PT7webxx4Ht2wFPT7mrIrI9DDdERFbg2DHxVgr//gt06cJgQ1QZDDdERDK6fBlITm6I0FBn/Psv0LkzsGMHUL263JUR2S5nuQsgInJEOh0wbx4wc6YzBKEdAMDfn8GGSAoMN0REZiYIwKVLwKFD4tWGDx0Sv/LyAEBhWO7yZXFejRpyVUpkHxhuiIgkduNGySCTnf3o12m1wLlzgK+v+WsksmcMN0RElZCfL16XRh9kfvsNuHCh5HJKJRAcDHTqJH75+wP9+omHp4ovExBgudqJ7BXDDRFROWk0wPHjxkHmjz+MA4pe06b3g0znzkDbtkCVKsbLfPYZMH68AK1WAaVSwMqVCu61IZIAww0RUSl0OuDPP+8fVvrtN/E6NGp1yWUbNLgfYjp1Ajp2LF/fzJgxQO/ed7Fhw0GMHNkFjRurpH4bRA6J4YaIHJ4giM28xYPM4cPinbgfVKOGcZDp1Anw8an4tn19gdatb0i6x0YQBNy9exdarVa6ldoBjUYDZ2dnFBYWcmzMqDLjrFKpoFQqK10Dww0ROZx//zUOMocOAVlZJZerUgVo1844yAQEAApFyWWtRVFRETIzM1FQUCB3KVZHEAR4e3vj0qVLUFjzN9HGVWacFQoFfH19Ua2Sd4lluCEiu5afL96vqXiQOX++5HJKJdC6tXGfTMuWgLMN/ZTU6XS4cOEClEolfHx84OLiwl/ixeh0OuTl5aFatWpwcuI1bM2louMsCAL++ecfXL58GYGBgZXag2ND/22JiB5OowFOnDA+BfvEidIbfgMDSzb8urtbvGRJFRUVQafTwc/PD+62/mbMQKfToaioCG5ubgw3ZlSZca5bty7+/vtvaDQahhsicjw6nXhNmOJB5uhRoLCw5LL1698/tNS5s9jwW7Om5Wu2FP7iJlsl1Z5GhhsisglXrhifgn34MJCTU3K5GjXE8FK8T6ZBA4uXS0QyYrghIqvz779ieCneJ5OZWXI5N7fSG36544LsXUpKCnr16oWbN2+iBu/XUQJ/BBCRrAoKgL17gcWLgREjxF6Y2rWBsDBgxgzgxx/FYKO/wu/YscDKleIhqNxcYN8+8bUjR4oXzmOwsU2jRo2CQqEo8dWvXz+5S6uw4u9JpVKhcePGmDZtGgpLO3b6ED179sSkSZOM5nXt2hWZmZmoXom7rMbHx5c65m5ubhVep7XgnhsishiNBjh50niPzIkT4j2VHvTYY8Z9Mu3a2X7DLz1cv379sHbtWqN5rq6uZt1mUVERXFxczLZ+/XvSaDRIS0tDZGQkFAoFPvjgg0qt18XFBd7e3pWuz9PTE2fOnDGa97C+l9LGSxAEaLVaOJt4amFFX1ce/BuHiMxCEICzZ4GEBGDSJKBbN8DTUwwpr74KrF4NHDsmBhtvb+DZZ4E5c4AdO8QbT547J7528mTxtQw2lnf5MvDrr+K/luDq6gpvb2+jr5rFOr8VCgVWr16NQYMGwd3dHYGBgfjxxx+N1nHixAn0798f1apVg5eXF15++WVcv37d8PwzzzyD//znP5g0aRLq1KmDsLAwAMCPP/6IwMBAuLm5oVevXli3bh0UCgVu3bqF/Px8eHp6YtOmTUbbSkxMRNWqVXH79u1Hvic/Pz+Eh4ejT58+SE5ONjx/48YNDB8+HA0aNIC7uztat26Nr776yvD8qFGjsHv3bixZssSwZ+Xvv/9GSkqKoT697777Di1btoSrqysaNWqEhQsXPnLMFQpFiTH38vIyPN+zZ09ERUUZjZd+29u2bUOHDh3g6uqKPXv2QK1W48033zSsp3v37jh06JBhXWW9zhy454aIJHH1ask7YRf7uWtQvXrpDb+8HIt5CIJ46M9U69YB//mPeFaakxPwf/8HREaW//Xu7ub5nsbGxuLDDz/ERx99hP/7v//DyJEjcfHiRdSqVQu3bt1C7969MXbsWCxatAh37tzB22+/jRdffBG7du0yrGP9+vV4/fXXsXfvXgDAhQsX8MILL2DixIkYO3Ysjh49irfeesuwfNWqVTFs2DCsXbsWL7zwgmG+/rGHh0e5aj9x4gT27dsHf39/w7zCwkJ06NABb7/9Njw9PfHzzz/j5ZdfxmOPPYbOnTtjyZIl+PPPP9GqVSu89957AO6fLl1cWloaXnzxRcyePRtDhw7Fvn378MYbb6B27doYNWqUqcNsZN26dUbjlXmvAW769OlYsGABmjRpgpo1a2LatGn47rvvsHbtWtSuXRvLly9HWFgYzp07h1q1ahnW9+DrzEKwAkuXLhX8/f0FV1dXoXPnzsLBgwcfuvw333wjNGvWTHB1dRVatWol/Pzzz+XeVk5OjgBAyMnJqWzZxvLyBEH8OSIU3bwp7brJSFFRkZCYmCgUFRXJXYpd++uvImHOnD3CX3+VHOd//xWEpCRBeP99QQgPFwQfH8PH3+jL1VUQHn9cEN58UxC++EIQTp8WBK1WhjdjxaT8PN+5c0f4448/hDt37hjmFfvRZNGvvDzTao+MjBSUSqVQtWpVo6/333/fsAwAYcaMGcXeW54AQNi2bZsgCIIwZ84coW/fvkbrvXTpkgBAOHPmjKDVaoVu3boJ7dq1M1rm7bffFlq1amU077///a8AQLh57+f5wYMHBaVSKVy9elUQBEHIzs4WnJ2dhZSUlHK9J1dXVwGA4OTkJGzatOmhYzFgwABhypQphsc9evQQJk6caLTMr7/+alTfiBEjhNDQUKNlpk6dKgQFBZW5nbVr1woASox5v379jLb94Hjpt52YmGiYl5eXJ6hUKmHDhg2CVqsVbt68KRQWFgo+Pj7Chx9+WObrHlTaZ1jPlN/fsu+5+frrrxEdHY0VK1agS5cuWLx4McLCwnDmzBnUq1evxPL79u3D8OHDERcXh2eeeQYJCQkIDw/HkSNH0KpVKxneAZH9+fxz4NVXnaHTdcOsWQKmTRMPHen3zJw9W/I1Tk7iFX2L75Fp3RpQ8V6QVE69evXC8uXLjeYV/4sfAIKDgw3TVatWhaenJ65duwYAOHbsGH799ddSL91//vx5BAQEAADat29v9NyZM2fQqVMno3mdO3cu8bhly5ZYt24dpk+fji+//BL+/v7o3r17ud5Tfn4+Fi1aBGdnZzz//POG57VaLebNm4dvvvkGV65cQVFREdRqtckXYTx16hSee+45o3ndunXD4sWLodVqy7wgnoeHB44cOWI0r8oDt6/v0KFDqa/t2LGjYfr8+fPQaDTo1q2bYZ5KpULnzp1x6tSpMl9nLrKHm48//hjjxo3D6NGjAQArVqzAzz//jDVr1mD69Oklll+yZAn69euHqVOnAgDmzJmD5ORkLF26FCtWrLBo7WW5cgVoVEPuKuzX5cvA8eN1EBwMNG4sdzXlJwjA3buP/tJopFmmouvKzQW2bwcA8ZiCTqfA/Pkl30+TJiUbfqtWteiQUjm4uwN5eaa95soVoEUL4ys7K5XAH3+U/5pBFemRqlq1qiGAlEX1QFpWKBTQ3Ss0Ly8PAwcOLLVZt379+kbbqYixY8di2bJlmD59OtauXYvRo0c/8qJzxd/TmjVr0KZNG3z++ecYM2YMAOCjjz7CkiVLsHjxYrRu3RpVq1bFpEmTUFRUVKEaTeXk5PTIMS9rvCo6jhV9nSlkDTdFRUVIS0tDTEyMYZ6TkxP69OmD/fv3l/qa/fv3Izo62mheWFgYEhMTzVnqIy1fDrx+b7pVK2eMjwb695e1pHIRBLkrMM22bcDixc4QhG6YOVPAG28APXuaNyRItYyt34Q4JET8TOuv8Fu7ttwVUXkoFKaHzqZNgc8+A8aPFz+3SqV4+n3TpuapUSrt27fHd999h0aNGpV6Bo6utPtwAGjWrBm2bt1qNK94I6zeSy+9hGnTpuGTTz7BH3/8gUhTmpAg/n575513EB0djREjRqBKlSrYu3cvnnvuObz00kuGGv/8808EBQUZXufi4vLIu2u3aNHC0BOjt3fvXjRt2lSSu2w/ymOPPQYXFxfs3bsXw4YNAyDeHfzQoUMlTmO3BFnDzfXr16HVao06swHAy8sLp0+fLvU1WVlZpS6fVdotfQGo1Wqo1WrD49zcXADioGs0msqUb3D5MjBtmmAINwIU+Phj4OOPJVk9lSD+pSQICixbBixbJnM5laRUCnB2Fm/QqFLBMF38S6lEsWWEhy5Tch1CieXK2o6zM3D7NjBzphMEQWFU44YNd+Hre79uif77ODz9zyEpfh5pNBoIggCdTlfmL/LyGj0aCA0Vz1oLCAB8fUu/R5dUBEFAYWEhrl69ajTf2dkZderUMTwu7b3p573++utYtWoVhg0bhqlTp6JWrVo4d+4cvv76a6xatcrothTF1zFu3Dh8/PHHmDZtGl555RWkp6cjPj7eUJd+2erVq2PQoEGYOnUqQkND4ePj89BxFgTB6PUA8Pzzz2Pq1KlYunQppkyZgoCAAHz33XfYs2cPatasiUWLFiE7OxstWrQwvM7f3x8HDx7EX3/9hWrVqqFWrVqG5/TvffLkyejSpQvee+89vPjii9i/fz+WLl2KpUuXllmjTqeDIAglxhwA6tWrZxivB9/Dg9sGxENZr732GqZOnYrq1asbGooLCgowevRoo2Uf9vnU11TavaVM+T8i+2Epc4uLi0NsbGyJ+UlJSZLdWO748TrQCW1LzPfyyoObm2X+XHeEM03u3FEiO7vksfRGjXLg4VEEpVKAUqmDUinAyUm4Fxp0hukHnys+Tz/94HNOTgKcnQU4ORVfruTj4uspa9nS1yNY5ffujTcaYvnyNtDpnODkpMNrrx3D779n4Pff5a7MfhU/PbiinJ2d4e3tjby8PEkOa3h6Avr2lHt/F5qNRqPBjh070OCB416BgYH47bffDI/v3Llj+CMVuB+KcnNzUa1aNWzbtg2zZ89GWFgYioqK4Ofnh6eeegp5eXmGQ0hFRUVG66hduzbi4+Px7rvv4pNPPkGnTp0wefJkTJkyBWq12mjZYcOG4auvvsKwYcOM5pf1nu7evVtiuTFjxuDDDz/EiBEj8Oabb+LPP/9E//79UaVKFURGRuLpp59Gbm6u4XXjx4/HG2+8gVatWuHOnTs4duwYCu6dAnf79m3DoaW1a9ciLi4Oc+fOhZeXF2JiYjB48OAy69SP24NjDgCnT5+Gl5cX7t69W2K8Hty2XkxMDAoLCxEZGYm8vDy0bdsWmzZtglKpRG5ubpmvK66oqAh37txBamoq7t69a/RcgQmn/SkEQb4DE0VFRXB3d8emTZsQHh5umB8ZGYlbt27hhx9+KPGahg0bIjo62mg316xZs5CYmIhjx46VWL60PTd+fn64fv06PD09JXkfly8DwY+pcVsQTwesijyole44e9b4L12qnMuXgYAAZ+h0xnsUOM7m8fffd/H112kYOrQDGjWy+7+DZKPRaJCcnIzQ0NAS/SSmKiwsxKVLl9CoUSO7uMqs1ARBwO3bt+Hh4fHIXpl58+Zh5cqVuHjxotH8L774AlOmTMHly5fNevE/W2bKOD+osLAQf//9N/z8/Ep8hnNzc1GnTh3k5OQ88ve3rD+xXFxc0KFDB+zcudMQbnQ6HXbu3ImoqKhSXxMSEoKdO3cahZvk5GSEhISUuryrq2upV7hUqVSV/kGi17gxsHRpETBBfKx0ErBypQKNG/M0ESk1bqzvAxCg1SqgVHKczalRI6B16xto1MhZsv8rVDYpfiZptVooFAo4OTnxzuCl0B8K0Y9RcZ9++ik6deqE2rVrY+/evViwYAGioqIMyxUUFCAzMxMffvghxo8fz/D4EA8b50dxcnIy3K7iwf8Ppvz/kP3THx0djVWrVmHdunU4deoUXn/9deTn5xvOnoqIiDBqOJ44cSK2b9+OhQsX4vTp05g9ezYOHz5cZhiylOJ9Zb//fhf3GuFJYmPGAGfP3sWcOXtw9izHmYikcfbsWTz33HMICgrCnDlzMGXKFMyePdvw/IcffojmzZvD29vb6HcSWSfZ9zUPHToU//zzD2bOnImsrCy0bdsW27dvNzQNZ2RkGCW/rl27IiEhATNmzMA777yDwMBAJCYmWtU1bsp7qiRVjK+vuEeBh6KISCqLFi3CokWLynx+9uzZRmGHrJvs4QYAoqKiytzzkpKSUmLekCFDMGTIEDNXRURERLZI9sNSRERERFJiuCEisjMyngRLVClSfXYZboiI7IT+bBJTrgdCZE3012eq7FWVraLnhoiIKk+pVKJGjRqGG0m6u7ubfJ0Re6bT6VBUVITCwkKeKm9GFR1nnU6Hf/75B+7u7qXePsMUDDdERHbE29sbAAwBh+4TBAF37txBlSpVGPrMqDLj7OTkhIYNG1b6+8NwQ0RkRxQKBerXr4969epJdv88e6HRaJCamoru3bvzwpRmVJlxdnFxkWSvGsMNEZEdUiqVFrkbtC1RKpW4e/cu3NzcGG7MyBrGmQcdiYiIyK4w3BAREZFdYbghIiIiu+JwPTf6CwTl5uZKu+L8fMOkJjcXKp5maDYajQYFBQXIzc3lcXMz4jhbBsfZcjjWlmGucdb/3i7Phf4cLtzcvn0bAODn52e+jfj7m2/dREREDuz27duoXr36Q5dRCA52nW6dToerV6/Cw8ND8usc5Obmws/PD5cuXYKnp6ek66b7OM6WwXG2DI6z5XCsLcNc4ywIAm7fvg0fH59Hni7ucHtunJyc4Ovra9ZteHp68j+OBXCcLYPjbBkcZ8vhWFuGOcb5UXts9NgYQkRERHaF4YaIiIjsCsONhFxdXTFr1iy4urrKXYpd4zhbBsfZMjjOlsOxtgxrGGeHaygmIiIi+8Y9N0RERGRXGG6IiIjIrjDcEBERkV1huJHIsmXL0KhRI7i5uaFLly747bff5C7J7qSmpmLgwIHw8fGBQqFAYmKi3CXZpbi4OHTq1AkeHh6oV68ewsPDcebMGbnLsjvLly9HcHCw4VogISEh2LZtm9xl2b358+dDoVBg0qRJcpdiV2bPng2FQmH01bx5c9nqYbiRwNdff43o6GjMmjULR44cQZs2bRAWFoZr167JXZpdyc/PR5s2bbBs2TK5S7Fru3fvxoQJE3DgwAEkJydDo9Ggb9++yC92/zSqPF9fX8yfPx9paWk4fPgwevfujeeeew4nT56UuzS7dejQIaxcuRLBwcFyl2KXWrZsiczMTMPXnj17ZKuFZ0tJoEuXLujUqROWLl0KQLzFg5+fH/7zn/9g+vTpMldnnxQKBTZv3ozw8HC5S7F7//zzD+rVq4fdu3eje/fucpdj12rVqoWPPvoIY8aMkbsUu5OXl4f27dvj008/xdy5c9G2bVssXrxY7rLsxuzZs5GYmIj09HS5SwHAPTeVVlRUhLS0NPTp08cwz8nJCX369MH+/ftlrIxIGjk5OQDEX7xkHlqtFhs3bkR+fj5CQkLkLscuTZgwAQMGDDD6WU3SOnv2LHx8fNCkSROMHDkSGRkZstXicPeWktr169eh1Wrh5eVlNN/LywunT5+WqSoiaeh0OkyaNAndunVDq1at5C7H7hw/fhwhISEoLCxEtWrVsHnzZgQFBcldlt3ZuHEjjhw5gkOHDsldit3q0qUL4uPj0axZM2RmZiI2NhZPPvkkTpw4AQ8PD4vXw3BDRGWaMGECTpw4Ieuxc3vWrFkzpKenIycnB5s2bUJkZCR2797NgCOhS5cuYeLEiUhOToabm5vc5dit/v37G6aDg4PRpUsX+Pv745tvvpHlMCvDTSXVqVMHSqUS2dnZRvOzs7Ph7e0tU1VElRcVFYUtW7YgNTUVvr6+cpdjl1xcXBAQEAAA6NChAw4dOoQlS5Zg5cqVMldmP9LS0nDt2jW0b9/eME+r1SI1NRVLly6FWq2GUqmUsUL7VKNGDTRt2hTnzp2TZfvsuakkFxcXdOjQATt37jTM0+l02LlzJ4+dk00SBAFRUVHYvHkzdu3ahcaNG8tdksPQ6XRQq9Vyl2FXnnrqKRw/fhzp6emGr44dO2LkyJFIT09nsDGTvLw8nD9/HvXr15dl+9xzI4Ho6GhERkaiY8eO6Ny5MxYvXoz8/HyMHj1a7tLsSl5entFfARcuXEB6ejpq1aqFhg0byliZfZkwYQISEhLwww8/wMPDA1lZWQCA6tWro0qVKjJXZz9iYmLQv39/NGzYELdv30ZCQgJSUlKwY8cOuUuzKx4eHiX6xapWrYratWuzj0xCb731FgYOHAh/f39cvXoVs2bNglKpxPDhw2Wph+FGAkOHDsU///yDmTNnIisrC23btsX27dtLNBlT5Rw+fBi9evUyPI6OjgYAREZGIj4+Xqaq7M/y5csBAD179jSav3btWowaNcryBdmpa9euISIiApmZmahevTqCg4OxY8cOhIaGyl0akckuX76M4cOH48aNG6hbty6eeOIJHDhwAHXr1pWlHl7nhoiIiOwKe26IiIjIrjDcEBERkV1huCEiIiK7wnBDREREdoXhhoiIiOwKww0RERHZFYYbIiIisisMN0RERGRXGG6IyKbEx8ejRo0aFt1mo0aNsHjxYotuk4gqjuGGiBySHCGJiCyD4YaIiIjsCsMNEVnUli1bUKNGDWi1WgBAeno6FAoFpk+fblhm7NixeOmllwCIe1gaNmwId3d3DBo0CDdu3Cj3to4dO4ZevXrBw8MDnp6e6NChAw4fPoyUlBSMHj0aOTk5UCgUUCgUmD17NgDxhpYDBw5ElSpV0LhxY2zYsEG6N09EFsFwQ0QW9eSTT+L27ds4evQoAGD37t2oU6cOUlJSDMvs3r0bPXv2xMGDBzFmzBhERUUhPT0dvXr1wty5c8u9rZEjR8LX1xeHDh1CWloapk+fDpVKha5du2Lx4sXw9PREZmYmMjMz8dZbbwEARo0ahUuXLuHXX3/Fpk2b8Omnn+LatWuSjgERmZez3AUQkWOpXr062rZti5SUFHTs2BEpKSmYPHkyYmNjkZeXh5ycHJw7dw49evTArFmz0K9fP0ybNg0A0LRpU+zbtw/bt28v17YyMjIwdepUNG/eHAAQGBhoVIdCoYC3t7dh3p9//olt27bht99+Q6dOnQAAn3/+OVq0aCHV2yciC+CeGyKyuB49eiAlJQWCIOB///sfBg8ejBYtWmDPnj3YvXs3fHx8EBgYiFOnTqFLly5Grw0JCSn3dqKjozF27Fj06dMH8+fPx/nz5x+6/KlTp+Ds7IwOHToY5jVv3pyNx0Q2huGGiCyuZ8+e2LNnD44dOwaVSoXmzZujZ8+eSElJwe7du9GjRw9JtjN79mycPHkSAwYMwK5duxAUFITNmzdLsm4isl4MN0Rkcfq+m0WLFhmCjD7cpKSkoGfPngCAFi1a4ODBg0avPXDggEnbatq0KSZPnoykpCQMHjwYa9euBQC4uLgYmpr1mjdvjrt37yItLc0w78yZM7h165aJ75CI5MRwQ0QWV7NmTQQHB2PDhg2GINO9e3ccOXIEf/75pyHwvPnmm9i+fTsWLFiAs2fPYunSpeXut7lz5w6ioqKQkpKCixcvYu/evTh06JChf6ZRo0bIy8vDzp07cf36dRQUFKBZs2bo168fxo8fj4MHDyItLQ1jx45FlSpVzDIORGQeDDdEJIsePXpAq9Uawk2tWrUQFBQEb29vNGvWDADw+OOPY9WqVViyZAnatGmDpKQkzJgxo1zrVyqVuHHjBiIiItC0aVO8+OKL6N+/P2JjYwEAXbt2xWuvvYahQ4eibt26+PDDDwEAa9euhY+PD3r06IHBgwfj1VdfRb169aQfACIyG4UgCILcRRARERFJhXtuiIiIyK4w3BCRzWrZsiWqVatW6hevLEzkuHhYiohs1sWLF6HRaEp9zsvLCx4eHhauiIisAcMNERER2RUeliIiIiK7wnBDREREdoXhhoiIiOwKww0RERHZFYYbIiIisisMN0RERGRXGG6IiIjIrjDcEBERkV35f8R+eH7UtzfdAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select the best result\n", - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wd_std(er_error, wd_std_range, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the values\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(df_list[0][\"wd_bin\"], df_list[0][\"SCADA\"].values, color=\"k\", lw=5, label=\"SCADA\")\n", - "\n", - "for i, wd_std in enumerate(wd_std_range):\n", - " ax.plot(df_list[i][\"wd_bin\"], df_list[i][\"FLORIS\"].values, label=wd_std)\n", - "\n", - "ax.grid()\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Repeat with noisy data" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.005 seconds.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.005 seconds.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.005 seconds.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (248.797, 289.232)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (248.000, 289.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m \u001b[33mWarning: the values in df[ws] exceed the range in the precalculated solutions df_fi_approx[ws].\u001b[0m\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df: (5.000, 9.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m minimum/maximum value in df_approx: (5.000, 8.000)\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 12:42:03\u001b[0m Finished interpolation in 0.023 seconds.\n" - ] - } - ], - "source": [ - "df_scada = df_baseline_noisy.copy()\n", - "er_error, df_list = ft.sweep_wd_std_for_er(\n", - " wd_std_range, df_scada, df_approx_noisy, ref_turbines=[ref_idx], test_turbines=[single_wake_idx]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select the best result\n", - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wd_std(er_error, wd_std_range, ax=ax)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the values\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(df_list[0][\"wd_bin\"], df_list[0][\"SCADA\"].values, color=\"k\", lw=5, label=\"SCADA\")\n", - "\n", - "for i, wd_std in enumerate(wd_std_range):\n", - " ax.plot(df_list[i][\"wd_bin\"], df_list[i][\"FLORIS\"].values, label=wd_std)\n", - "\n", - "ax.grid()\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tune deflection" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "hor_def_gains = np.arange(start=0.0, stop=5, step=0.2)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "floris_uplifts, scada_uplift = ft.sweep_deflection_parameter_for_total_uplift(\n", - " parameter=[\n", - " \"wake\",\n", - " \"wake_deflection_parameters\",\n", - " \"empirical_gauss\",\n", - " \"horizontal_deflection_gain_D\",\n", - " ],\n", - " value_candidates=hor_def_gains,\n", - " df_scada_baseline_in=df_baseline,\n", - " df_scada_wakesteering_in=df_wakesteering,\n", - " fm_in=fm_emg,\n", - " ref_turbines=[ref_idx],\n", - " test_turbines=[single_wake_idx],\n", - " yaw_angles_wakesteering=yaw_angles,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([2.3620752 , 2.58036597, 2.79926482, 3.01823547, 3.23910144,\n", - " 3.4610657 , 3.68223937, 3.90339759, 4.12402072, 4.34194854,\n", - " 4.5586649 , 4.77471308, 4.98895207, 5.19935324, 5.40542043,\n", - " 5.60677409, 5.80608574, 5.99994176, 6.18834151, 6.37196041,\n", - " 6.55081939, 6.72304104, 6.89010967, 7.05255549, 7.20769755]),\n", - " 5.606774094970125)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "floris_uplifts, scada_uplift" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Uplift')" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(floris_uplifts, scada_uplift, hor_def_gains, ax=ax)\n", - "ax.set_xlabel(\"Hor Def Parameter\")\n", - "ax.set_ylabel(\"Percent Uplift\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Small uplift in power at downstream turbine even with 0 wake deflection because effective axial induction factor at upstream turbine is reduced by yaw misalignment" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "## Repeat for noisy\n", - "floris_uplifts, scada_uplift = ft.sweep_deflection_parameter_for_total_uplift(\n", - " parameter=[\n", - " \"wake\",\n", - " \"wake_deflection_parameters\",\n", - " \"empirical_gauss\",\n", - " \"horizontal_deflection_gain_D\",\n", - " ],\n", - " value_candidates=hor_def_gains,\n", - " df_scada_baseline_in=df_baseline_noisy,\n", - " df_scada_wakesteering_in=df_wakesteering_noisy,\n", - " fm_in=fm_emg_noisy,\n", - " ref_turbines=[ref_idx],\n", - " test_turbines=[single_wake_idx],\n", - " yaw_angles_wakesteering=yaw_angles,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Uplift')" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(floris_uplifts, scada_uplift, hor_def_gains, ax=ax)\n", - "ax.set_xlabel(\"Hor Def Parameter\")\n", - "ax.set_ylabel(\"Percent Uplift\")" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "96c53852a1e56d9fbc8381f88ff3256056a2f574c5e86cd3dfe6ce1bc9d68e6a" - }, - "kernelspec": { - "display_name": "Python 3.10.4 64-bit ('flasc-reqs': conda)", - "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" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples_artificial_data/04_floris_tuning/README.txt b/examples_artificial_data/04_floris_tuning/README.txt new file mode 100644 index 00000000..02138376 --- /dev/null +++ b/examples_artificial_data/04_floris_tuning/README.txt @@ -0,0 +1,6 @@ +Calibrating FLORIS models using the floris_tuning package is deprecated as of FLASC v2.4. +If you are looking for these examples, please see FLASC v2.3 +(https://github.com/NREL/flasc/releases/tag/v2.3) + +We strongly recommend instead using the replacement ModelFit package, demonstrated in +examples_artificial_data/05_model_fit/ diff --git a/examples_artificial_data/04_floris_tuning/develop_ws_est.ipynb b/examples_artificial_data/04_floris_tuning/develop_ws_est.ipynb new file mode 100644 index 00000000..41c4dae2 --- /dev/null +++ b/examples_artificial_data/04_floris_tuning/develop_ws_est.ipynb @@ -0,0 +1,680 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Develop ws estimator\n", + "\n", + "This is likely a temporary example while working out the wins speed estimator method" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from floris import FlorisModel, TimeSeries\n", + "\n", + "from flasc.utilities.floris_tools import estimate_ws_with_floris" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load FlorisModel" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = Path.cwd()\n", + "fm_path = file_path / \"../floris_input_artificial/gch.yaml\"\n", + "fm = FlorisModel(fm_path)\n", + "\n", + "# Set to 1 turbine layout\n", + "fm.set(layout_x=[0], layout_y=[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Set to\n", + "N = 25\n", + "wind_speeds = np.linspace(0.01, 20.0, N)\n", + "time_series = TimeSeries(\n", + " wind_speeds=wind_speeds, wind_directions=270.0, turbulence_intensities=0.06\n", + ")\n", + "\n", + "fm.set(wind_data=time_series)\n", + "\n", + "fm.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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220.0000002.675833
330.0000003.508750
4485.8712844.341667
55214.0610515.174583
66401.8713706.007500
77677.9275126.840417
881030.4431917.673333
991450.1084888.506250
10102004.1540849.339167
11112650.50864410.172083
12123422.43085511.005000
13134328.81309911.837917
14145000.00000012.670833
15155000.00000013.503750
16165000.00000014.336667
17175000.00000015.169583
18185000.00000016.002500
19195000.00000016.835417
20205000.00000017.668333
21215000.00000018.501250
22225000.00000019.334167
23235000.00000020.167083
24245000.00000021.000000
\n", + "
" + ], + "text/plain": [ + " time pow_000 ws_000\n", + "0 0 0.000000 1.010000\n", + "1 1 0.000000 1.842917\n", + "2 2 0.000000 2.675833\n", + "3 3 0.000000 3.508750\n", + "4 4 85.871284 4.341667\n", + "5 5 214.061051 5.174583\n", + "6 6 401.871370 6.007500\n", + "7 7 677.927512 6.840417\n", + "8 8 1030.443191 7.673333\n", + "9 9 1450.108488 8.506250\n", + "10 10 2004.154084 9.339167\n", + "11 11 2650.508644 10.172083\n", + "12 12 3422.430855 11.005000\n", + "13 13 4328.813099 11.837917\n", + "14 14 5000.000000 12.670833\n", + "15 15 5000.000000 13.503750\n", + "16 16 5000.000000 14.336667\n", + "17 17 5000.000000 15.169583\n", + "18 18 5000.000000 16.002500\n", + "19 19 5000.000000 16.835417\n", + "20 20 5000.000000 17.668333\n", + "21 21 5000.000000 18.501250\n", + "22 22 5000.000000 19.334167\n", + "23 23 5000.000000 20.167083\n", + "24 24 5000.000000 21.000000" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Make a df_scada from the data\n", + "df_scada = pd.DataFrame(\n", + " {\n", + " \"time\": np.arange(0, N),\n", + " \"pow_000\": fm.get_turbine_powers().squeeze() / 1000.0,\n", + " \"ws_000\": wind_speeds + 1.0, # Add 1m/s bias\n", + " }\n", + ")\n", + "df_scada" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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timepow_000ws_000ws_est_000ws_est_gain_000
000.0000001.0100001.3709880.190999
110.0000001.8429172.2113210.348510
220.0000002.6758332.7892660.506021
330.0000003.5087503.1048250.663532
4485.8712844.3416673.5115990.821043
55214.0610515.1745834.1825930.978554
66401.8713706.0075004.9910291.000000
77677.9275126.8404175.8212061.000000
881030.4431917.6733336.6513831.000000
991450.1084888.5062507.4815601.000000
10102004.1540849.3391678.3117371.000000
11112650.50864410.1720839.1419141.000000
12123422.43085511.00500010.0908220.885052
13134328.81309911.83791711.8379170.000000
14145000.00000012.67083312.6708330.000000
15155000.00000013.50375013.5037500.000000
16165000.00000014.33666714.3366670.000000
17175000.00000015.16958315.1695830.000000
18185000.00000016.00250016.0025000.000000
19195000.00000016.83541716.8354170.000000
20205000.00000017.66833317.6683330.000000
21215000.00000018.50125018.5012500.000000
22225000.00000019.33416719.3341670.000000
23235000.00000020.16708320.1670830.000000
24245000.00000021.00000021.0000000.000000
\n", + "
" + ], + "text/plain": [ + " time pow_000 ws_000 ws_est_000 ws_est_gain_000\n", + "0 0 0.000000 1.010000 1.370988 0.190999\n", + "1 1 0.000000 1.842917 2.211321 0.348510\n", + "2 2 0.000000 2.675833 2.789266 0.506021\n", + "3 3 0.000000 3.508750 3.104825 0.663532\n", + "4 4 85.871284 4.341667 3.511599 0.821043\n", + "5 5 214.061051 5.174583 4.182593 0.978554\n", + "6 6 401.871370 6.007500 4.991029 1.000000\n", + "7 7 677.927512 6.840417 5.821206 1.000000\n", + "8 8 1030.443191 7.673333 6.651383 1.000000\n", + "9 9 1450.108488 8.506250 7.481560 1.000000\n", + "10 10 2004.154084 9.339167 8.311737 1.000000\n", + "11 11 2650.508644 10.172083 9.141914 1.000000\n", + "12 12 3422.430855 11.005000 10.090822 0.885052\n", + "13 13 4328.813099 11.837917 11.837917 0.000000\n", + "14 14 5000.000000 12.670833 12.670833 0.000000\n", + "15 15 5000.000000 13.503750 13.503750 0.000000\n", + "16 16 5000.000000 14.336667 14.336667 0.000000\n", + "17 17 5000.000000 15.169583 15.169583 0.000000\n", + "18 18 5000.000000 16.002500 16.002500 0.000000\n", + "19 19 5000.000000 16.835417 16.835417 0.000000\n", + "20 20 5000.000000 17.668333 17.668333 0.000000\n", + "21 21 5000.000000 18.501250 18.501250 0.000000\n", + "22 22 5000.000000 19.334167 19.334167 0.000000\n", + "23 23 5000.000000 20.167083 20.167083 0.000000\n", + "24 24 5000.000000 21.000000 21.000000 0.000000" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scada = estimate_ws_with_floris(df_scada, fm)\n", + "df_scada" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate power with wind speed and estimated wind speed" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate with biased measurement\n", + "time_series = TimeSeries(\n", + " wind_speeds=df_scada.ws_000.values, wind_directions=270.0, turbulence_intensities=0.06\n", + ")\n", + "fm.set(wind_data=time_series)\n", + "fm.run()\n", + "power_from_original_ws = fm.get_turbine_powers().squeeze() / 1000.0\n", + "\n", + "# Calculate with estimated wind speed\n", + "time_series = TimeSeries(\n", + " wind_speeds=df_scada.ws_est_000.values, wind_directions=270.0, turbulence_intensities=0.06\n", + ")\n", + "fm.set(wind_data=time_series)\n", + "fm.run()\n", + "power_from_estimated_ws = fm.get_turbine_powers().squeeze() / 1000.0\n", + "\n", + "# Compute the error of each relative to measured power\n", + "original_ws_error = df_scada.pow_000.values - power_from_original_ws\n", + "estimated_ws_error = df_scada.pow_000.values - power_from_estimated_ws\n", + "\n", + "# Plot the error against the measured power\n", + "fig, ax = plt.subplots()\n", + "sns.scatterplot(x=df_scada.pow_000, y=original_ws_error, ax=ax, label=\"Original WS\", s=100)\n", + "sns.scatterplot(x=df_scada.pow_000, y=estimated_ws_error, ax=ax, label=\"Estimated WS\")\n", + "ax.set_xlabel(\"Measured Power [kW]\")\n", + "ax.set_ylabel(\"Error [kW]\")\n", + "ax.set_title(\"Error vs Measured Power\")\n", + "ax.grid(True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.scatter(df_scada[\"ws_000\"], df_scada[\"ws_est_000\"])\n", + "ax.set_xlabel(\"Original Wind Speed [m/s]\")\n", + "ax.set_ylabel(\"Estimated Wind Speed [m/s]\")\n", + "ax.set_title(\"Estimated Wind Speed vs Original Wind Speed\")\n", + "ax.grid(True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples_artificial_data/05_model_fit/00_generate_data.py b/examples_artificial_data/05_model_fit/00_generate_data.py new file mode 100644 index 00000000..ce7b6975 --- /dev/null +++ b/examples_artificial_data/05_model_fit/00_generate_data.py @@ -0,0 +1,121 @@ +import pickle + +import numpy as np +import pandas as pd +from floris import TimeSeries, UncertainFlorisModel + +from flasc import FlascDataFrame +from flasc.utilities.utilities_examples import load_floris_artificial + +""" This example sets up a simple two turbine data set with the Jensen wake model. Data is saved +with both uncertain and certain wind speed data.""" + +# Parameters +N = 200 # Number of data points +wd_std_original = 3.0 # Standard deviation of wind direction in for uncertain model (default) +wd_std_set = 2.0 # Set point in created data +we_value_set = 0.03 # Wake expansion value that will be the used to generate the test data + +# Resolution parameters +# These are used in the UncertainFlorisModel +ws_resolution = 0.25 +wd_resolution = 2.0 + +# Get default FLORIS model +fm_default, _ = load_floris_artificial(wake_model="jensen") +fm_param = fm_default.copy() +ufm_default = UncertainFlorisModel( + fm_default.copy(), + wd_std=wd_std_original, + ws_resolution=ws_resolution, + wd_resolution=wd_resolution, +) +ufm_param = UncertainFlorisModel( + fm_default.copy(), wd_std=wd_std_set, ws_resolution=ws_resolution, wd_resolution=wd_resolution +) + +# Set a simple two turbine layout +layout_x = [0.0, 126.0 * 6.0] +layout_y = [0.0, 0.0] + +# Generate a random series of wind speeds and directions with wind directions +# focused on turbine 0 waking turbine 1 +np.random.seed(0) +wind_directions = np.random.uniform(230.0, 310.0, N) +wind_speeds = np.random.uniform(4.0, 15.0, N) + +# wind_directions = np.array([270.0]) +# wind_speeds = np.array([8.0]) + +time_series = TimeSeries( + wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=0.06 +) + +# Set layout and inflow +fm_default.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) +fm_param.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) +ufm_default.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) +ufm_param.set(layout_x=layout_x, layout_y=layout_y, wind_data=time_series) + +# Set the FLORIS model parameter +parameter = ("wake", "wake_velocity_parameters", "jensen", "we") +we_value_original = fm_param.get_param(parameter) +fm_param.set_param(parameter, we_value_set) +ufm_param.set_param(parameter, we_value_set) + +# Run +fm_param.run() +ufm_param.run() + +# Get the turbine powers in kW +powers = fm_param.get_turbine_powers() / 1000 +powers_u = ufm_param.get_turbine_powers() / 1000 + +# Make a time column for the flasc_dataframe convention +time = np.arange(N) + +# Build the dataframe +df = pd.DataFrame( + { + "time": time, + "wd": wind_directions, + "ws": wind_speeds, + } +) + +for i in range(powers.shape[1]): + df[f"pow_{i:03}"] = powers[:, i] + +df = FlascDataFrame(df) + + +df_u = pd.DataFrame( + { + "time": time, + "wd": wind_directions, + "ws": wind_speeds, + } +) + +for i in range(powers_u.shape[1]): + df_u[f"pow_{i:03}"] = powers_u[:, i] + +df_u = FlascDataFrame(df_u) + + +# Save the dataframe and default model and target parameter to a pickle file +with open("two_turbine_data.pkl", "wb") as f: + pickle.dump( + { + "df": df, + "df_u": df_u, + "fm_default": fm_default, + "ufm_default": ufm_default, + "parameter": parameter, + "we_value_original": we_value_original, + "we_value_set": we_value_set, + "wd_std_original": wd_std_original, + "wd_std_set": wd_std_set, + }, + f, + ) diff --git a/examples_artificial_data/05_model_fit/01a_evaluate_costs.py b/examples_artificial_data/05_model_fit/01a_evaluate_costs.py new file mode 100644 index 00000000..f087e32c --- /dev/null +++ b/examples_artificial_data/05_model_fit/01a_evaluate_costs.py @@ -0,0 +1,58 @@ +import pickle + +import matplotlib.pyplot as plt +import numpy as np + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit + +# Load the data from previous example +with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + +# Unpack +df = data["df"] +fm_default = data["fm_default"] +parameter = data["parameter"] +we_value_original = data["we_value_original"] +we_value_set = data["we_value_set"] + + +# Set up the ModelFit object with the data and FLORIS model +# Use the absolute turbine power error as cost function +cost_function = TurbinePowerMeanAbsoluteError() +mf = ModelFit( + df, + fm_default, + cost_function, +) + +# Evaluate the model +cost_value = mf.evaluate_floris() +print(f"Cost value: {cost_value} for model with original parameter value {we_value_original}") + +# Now loop over a range of values for the parameter and evaluate the cost function +n_steps = 10 +param_result = [] +cost_result = [] +for i, param_value in enumerate(np.arange(0.01, 0.07, 0.01)): + fm_default.set_param(parameter, param_value) + mf = ModelFit( + df, + fm_default, + cost_function, + ) + cost_value = mf.evaluate_floris() + + param_result.append(param_value) + cost_result.append(cost_value) + +# Show the results +fix, ax = plt.subplots() +ax.plot(param_result, cost_result) +ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") +ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") +ax.set_xlabel("Wake expansion value") +ax.set_ylabel("Cost value") +ax.legend() +plt.show() diff --git a/examples_artificial_data/05_model_fit/01b_evaluate_costs_uncertain.py b/examples_artificial_data/05_model_fit/01b_evaluate_costs_uncertain.py new file mode 100644 index 00000000..7b0a4935 --- /dev/null +++ b/examples_artificial_data/05_model_fit/01b_evaluate_costs_uncertain.py @@ -0,0 +1,62 @@ +import pickle + +import matplotlib.pyplot as plt +import numpy as np + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit + +# Since ModelFit is always parallel this is important to include +if __name__ == "__main__": + # Load the data from previous example + with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + + # Unpack + df_u = data["df_u"] # Get data from uncertain model + fm_default = data["fm_default"] + ufm_default = data["ufm_default"] + parameter = data["parameter"] + we_value_original = data["we_value_original"] + we_value_set = data["we_value_set"] + + # Now loop over a range of values for the parameter and evaluate the cost function + n_steps = 10 + param_result = [] + cost_result = [] + cost_result_u = [] + for i, param_value in enumerate(np.arange(0.01, 0.07, 0.01)): + fm_default.set_param(parameter, param_value) + mf = ModelFit( + df_u, + fm_default, + TurbinePowerMeanAbsoluteError(), + ) + cost_value = mf.evaluate_floris() + + param_result.append(param_value) + cost_result.append(cost_value) + print(f"... cost value: {cost_value}") + + print("--repeat for uncertain model--") + ufm_default.set_param(parameter, param_value) + mf = ModelFit( + df_u, + ufm_default, + TurbinePowerMeanAbsoluteError(), + ) + cost_value = mf.evaluate_floris() + + cost_result_u.append(cost_value) + print(f"~~~ cost value: {cost_value}") + + # Show the results + fix, ax = plt.subplots() + ax.plot(param_result, cost_result, label="Certain model") + ax.plot(param_result, cost_result_u, label="Uncertain model") + ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") + ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") + ax.set_xlabel("Wake expansion value") + ax.set_ylabel("Cost value") + ax.legend() + plt.show() diff --git a/examples_artificial_data/05_model_fit/02a_optimize_parameter_optsweep.py b/examples_artificial_data/05_model_fit/02a_optimize_parameter_optsweep.py new file mode 100644 index 00000000..6c8d494f --- /dev/null +++ b/examples_artificial_data/05_model_fit/02a_optimize_parameter_optsweep.py @@ -0,0 +1,64 @@ +import pickle + +import matplotlib.pyplot as plt + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.opt_library import opt_sweep + +""" Use ModelFit optimization to find the optimal wake expansion value that best fits the data. + +In this example using the opt_sweep optimization routine from the opt_library. +""" + + +n_grid = 10 + +# Load the data from previous example +with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + +# Unpack +df = data["df"] +fm_default = data["fm_default"] +parameter = data["parameter"] +we_value_original = data["we_value_original"] +we_value_set = data["we_value_set"] + +# Now pass the above cost function to the ModelFit class +mf = ModelFit( + df, + fm_default, + TurbinePowerMeanAbsoluteError(), + parameter_list=[parameter], + parameter_name_list=["wake expansion"], + parameter_range_list=[(0.01, 0.07)], + parameter_index_list=[], +) + +# Compute the baseline cost +print("Evaluating baseline cost") +baseline_cost = mf.evaluate_floris() + +# Optimize +opt_result = opt_sweep(mf, n_grid=n_grid) + +print("Final optimization results (opt_result dictionary):") +print(opt_result) + +# Print results +print("----------------------------") +print(f"Default parameter: {we_value_original}") +print(f"Set parameter: {we_value_set}") +print() +print(f"Calibrated parameter value: {opt_result['optimized_parameter_values'][0]:.2f}") +print("----------------------------") + +# Plot the results +plt.plot(opt_result["all_parameter_combinations"], opt_result["all_costs"]) +plt.axvline(we_value_original, color="k", linestyle="--", label="Original value") +plt.axvline(we_value_set, color="r", linestyle="--", label="Set value") +plt.xlabel("Wake expansion value") +plt.ylabel("Cost value") +plt.legend() +plt.show() diff --git a/examples_artificial_data/05_model_fit/02b_optimize_parameter_optuna.py b/examples_artificial_data/05_model_fit/02b_optimize_parameter_optuna.py new file mode 100644 index 00000000..3f359287 --- /dev/null +++ b/examples_artificial_data/05_model_fit/02b_optimize_parameter_optuna.py @@ -0,0 +1,69 @@ +import pickle + +import matplotlib.pyplot as plt +from optuna.visualization.matplotlib import ( + plot_optimization_history, + plot_slice, +) + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.opt_library import opt_optuna + +""" Use ModelFit optimization to find the optimal wake expansion value that best fits the data. + +In this example using the opt_optuna optimization routine from the opt_library. Additionally, two +of optuna's provided visualization functions are used to assess the study: plot_optimization_history +and plot_slice. +""" + +# Parameters +time_out = 5 + +# Load the data from previous example +with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + +# Unpack +df = data["df"] +fm_default = data["fm_default"] +parameter = data["parameter"] +we_value_original = data["we_value_original"] +we_value_set = data["we_value_set"] + +# Now pass the above cost function to the ModelFit class +mf = ModelFit( + df, + fm_default, + TurbinePowerMeanAbsoluteError(), + parameter_list=[parameter], + parameter_name_list=["wake expansion"], + parameter_range_list=[(0.01, 0.07)], + parameter_index_list=[], +) + +# Compute the baseline cost +print("Evaluating baseline cost") +baseline_cost = mf.evaluate_floris() + +# Optimize +opt_result = opt_optuna(mf, timeout=time_out, n_trials=None) + +# Print results +print("----------------------------") +print(f"Default parameter: {we_value_original}") +print(f"Set parameter: {we_value_set}") +print() +print(f"Calibrated parameter value: {opt_result['optimized_parameter_values'][0]:.2f}") +print("----------------------------") + +# Show an optuna progress plot +plot_optimization_history(opt_result["optuna_study"]) + +# Show a slice plot +ax = plot_slice(opt_result["optuna_study"]) +ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") +ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") +ax.legend() + +plt.show() diff --git a/examples_artificial_data/05_model_fit/02c_optimize_parameter_optuna_wd_std.py b/examples_artificial_data/05_model_fit/02c_optimize_parameter_optuna_wd_std.py new file mode 100644 index 00000000..a16afdba --- /dev/null +++ b/examples_artificial_data/05_model_fit/02c_optimize_parameter_optuna_wd_std.py @@ -0,0 +1,97 @@ +import pickle + +import matplotlib.pyplot as plt +from optuna.visualization.matplotlib import ( + plot_contour, + plot_optimization_history, + plot_slice, +) + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.opt_library import opt_optuna_with_wd_std + +""" Use ModelFit optimization to find the optimal wake expansion value and wind direction +standard deviation that best fits the uncertain data. + +In this example using the opt_optuna_with_wd_std optimization routine from the opt_library. +This version optimizes both the wake expansion parameter and the wind direction standard +deviation (wd_std) parameter. Additionally, optuna's provided visualization functions are +used to assess the study: plot_optimization_history, plot_slice, and plot_contour. +""" + + +n_trials = 150 + +# Load the data from previous example +with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + +# Unpack - using df_u (uncertain data) instead of df +df_u = data["df_u"] +ufm_default = data["ufm_default"] +parameter = data["parameter"] +we_value_original = data["we_value_original"] +we_value_set = data["we_value_set"] +wd_std_original = data["wd_std_original"] +wd_std_set = data["wd_std_set"] + +# Now pass the above cost function to the ModelFit class +# Note: using ufm_default (UncertainFlorisModel) instead of fm_default +mf = ModelFit( + df_u, + ufm_default, + TurbinePowerMeanAbsoluteError(), + parameter_list=[parameter], + parameter_name_list=["wake expansion"], + parameter_range_list=[(0.01, 0.07)], + parameter_index_list=[], +) + +# Compute the baseline cost +print("Evaluating baseline cost") +baseline_cost = mf.evaluate_floris() + +# Optimize using opt_optuna_with_wd_std which optimizes both the wake expansion +# parameter and the wind direction standard deviation +opt_result = opt_optuna_with_wd_std(mf, n_trials=n_trials) + +# Print results +print("----------------------------") +print(f"Default parameter (we_1): {we_value_original}") +print(f"Set (Target) parameter (we_1): {we_value_set}") +print(f"Calibrated parameter value (we_1): {opt_result['optimized_parameter_values'][0]:.2f}") +print() +print(f"Default parameter (wd_std): {wd_std_original}") +print(f"Set (Target) parameter (wd_std): {wd_std_set}") +print(f"Calibrated wd_std value: {opt_result['optimized_parameter_values'][1]:.2f}") +print("----------------------------") + +# Show an optuna progress plot +plot_optimization_history(opt_result["optuna_study"]) + +# Show a slice plot +axarr = plot_slice(opt_result["optuna_study"]) + +# we_1 tuning +ax = axarr[0] +ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") +ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") +ax.legend() + +# wd_std tuning +ax = axarr[1] +ax.axvline(wd_std_original, color="k", linestyle="--", label="Original value") +ax.axvline(wd_std_set, color="r", linestyle="--", label="Set value") +ax.legend() + +# Show a contour plot of wake expansion vs wd_std +ax = plot_contour(opt_result["optuna_study"]) +ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") +ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") +ax.axhline(wd_std_original, color="k", linestyle="--") +ax.axhline(wd_std_set, color="r", linestyle="--") +ax.legend() + +plt.tight_layout() +plt.show() diff --git a/examples_artificial_data/05_model_fit/02d_optimize_parameter_optuna_wd_std_par.py b/examples_artificial_data/05_model_fit/02d_optimize_parameter_optuna_wd_std_par.py new file mode 100644 index 00000000..663744d8 --- /dev/null +++ b/examples_artificial_data/05_model_fit/02d_optimize_parameter_optuna_wd_std_par.py @@ -0,0 +1,111 @@ +import pickle + +import matplotlib.pyplot as plt +from floris import ParFlorisModel, UncertainFlorisModel +from optuna.visualization.matplotlib import ( + plot_contour, + plot_optimization_history, + plot_slice, +) + +from flasc.model_fitting.cost_library import TurbinePowerMeanAbsoluteError +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.opt_library import opt_optuna_with_wd_std + +""" Use ModelFit optimization to find the optimal wake expansion value and wind direction +standard deviation that best fits the uncertain data. + +Demonstrate usage with parallelization via ParFlorisModel. Note for this small case, this +actually does not improve performance, but for larger cases, and on clusters can be very useful.s +""" + +# Since ModelFit is always parallel this is important to include +if __name__ == "__main__": + n_trials = 50 + + # Load the data from previous example + with open("two_turbine_data.pkl", "rb") as f: + data = pickle.load(f) + + # Unpack - using df_u (uncertain data) instead of df + df_u = data["df_u"] + parameter = data["parameter"] + we_value_original = data["we_value_original"] + we_value_set = data["we_value_set"] + wd_std_original = data["wd_std_original"] + wd_std_set = data["wd_std_set"] + + # Declare parallel FLORIS model + fm = data["fm_default"] + fm_par = ParFlorisModel(fm) + + # Repeat settings from 00_generate_data.py + wd_std_original = 3.0 # Standard deviation of wind direction in for uncertain model (default) + ws_resolution = 0.25 + wd_resolution = 2.0 + ufm_par = UncertainFlorisModel( + fm_par.copy(), + wd_std=wd_std_original, + ws_resolution=ws_resolution, + wd_resolution=wd_resolution, + ) + + # Now pass the above cost function to the ModelFit class + # Note: using ufm_default (UncertainFlorisModel) instead of fm_default + mf = ModelFit( + df_u, + ufm_par, + TurbinePowerMeanAbsoluteError(), + parameter_list=[parameter], + parameter_name_list=["wake expansion"], + parameter_range_list=[(0.01, 0.07)], + parameter_index_list=[], + ) + + # Compute the baseline cost + print("Evaluating baseline cost") + baseline_cost = mf.evaluate_floris() + + # Optimize using opt_optuna_with_wd_std which optimizes both the wake expansion + # parameter and the wind direction standard deviation + opt_result = opt_optuna_with_wd_std(mf, n_trials=n_trials) + + # Print results + print("----------------------------") + print(f"Default parameter (we_1): {we_value_original}") + print(f"Set (Target) parameter (we_1): {we_value_set}") + print(f"Calibrated parameter value (we_1): {opt_result['optimized_parameter_values'][0]:.2f}") + print() + print(f"Default parameter (wd_std): {wd_std_original}") + print(f"Set (Target) parameter (wd_std): {wd_std_set}") + print(f"Calibrated wd_std value: {opt_result['optimized_parameter_values'][1]:.2f}") + print("----------------------------") + + # Show an optuna progress plot + plot_optimization_history(opt_result["optuna_study"]) + + # Show a slice plot + axarr = plot_slice(opt_result["optuna_study"]) + + # we_1 tuning + ax = axarr[0] + ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") + ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") + ax.legend() + + # wd_std tuning + ax = axarr[1] + ax.axvline(wd_std_original, color="k", linestyle="--", label="Original value") + ax.axvline(wd_std_set, color="r", linestyle="--", label="Set value") + ax.legend() + + # Show a contour plot of wake expansion vs wd_std + ax = plot_contour(opt_result["optuna_study"]) + ax.axvline(we_value_original, color="k", linestyle="--", label="Original value") + ax.axvline(we_value_set, color="r", linestyle="--", label="Set value") + ax.axhline(wd_std_original, color="k", linestyle="--") + ax.axhline(wd_std_set, color="r", linestyle="--") + ax.legend() + + plt.tight_layout() + plt.show() diff --git a/examples_smarteole/07_emgauss_scada_tuning_optimization_method.ipynb b/examples_smarteole/07_emgauss_scada_tuning_optimization_method.ipynb index 6c679d61..48ad888c 100644 --- a/examples_smarteole/07_emgauss_scada_tuning_optimization_method.ipynb +++ b/examples_smarteole/07_emgauss_scada_tuning_optimization_method.ipynb @@ -5,924 +5,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Tuning Empirical Gaussian FLORIS Model to SCADA Using Interpolation and Mathematical Optimization\n", + "Calibrating FLORIS models using the floris_tuning package is deprecated as of FLASC v2.4.\n", + "If you are looking for these examples, please see FLASC v2.3\n", + "(https://github.com/NREL/flasc/releases/tag/v2.3)\n", "\n", - "In this notebook, the Empirical Gaussian FLORIS Model (emgauss) will be tuned to align with SCADA data using an interpolation/mathematical optimization technique that determines the parameter value(s) that minimize the error (mean squared error) between SCADA and FLORIS energy ratios. \n", - "\n", - "The parameters of interest in this tuning exercise are 'wake_expansion_rates' (1st expansion rate) and 'horizontal_deflection_gain_D'. These parameters are associated with the following operating scenarios:\n", - "\n", - "wake_expansion_rates => basline case\n", - "\n", - "horizontal_deflection_gain_D => wake steering case" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Import Relevant Libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" - ] - } - ], - "source": [ - "import os\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import flasc.model_fitting.floris_tuning as ft\n", - "import flasc.utilities.floris_tools as ftools\n", - "from flasc.analysis import energy_ratio as er\n", - "from flasc.analysis.analysis_input import AnalysisInput\n", - "from flasc.utilities.tuner_utilities import resim_floris\n", - "from flasc.utilities.utilities_examples import load_floris_smarteole" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Suppress warnings\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load and Inspect SCADA\n", - "\n", - "Load pre-processed SCADA data with power curve fiiltering and northing calibration applied, and inspect the data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " time pow_000 pow_001 pow_002 pow_003 \\\n", - "0 2020-02-17 16:30:00 2023.746948 2045.376953 2031.724976 NaN \n", - "1 2020-02-17 16:31:00 1959.036011 2050.572998 2034.890991 NaN \n", - "2 2020-02-17 16:32:00 2053.658936 2032.191040 2011.870972 NaN \n", - "3 2020-02-17 16:33:00 2044.296997 2060.478027 1995.057983 NaN \n", - "4 2020-02-17 16:34:00 2058.281006 2042.703003 2031.723999 NaN \n", - "\n", - " pow_004 pow_005 pow_006 ws_000 ws_001 ... \\\n", - "0 2028.063965 2032.461060 1983.390991 13.066 12.337 ... \n", - "1 2017.777954 1943.764038 2046.568970 12.091 13.057 ... \n", - "2 NaN 2052.092041 2039.948975 13.381 12.213 ... \n", - "3 NaN 2008.868042 2058.000000 14.345 13.141 ... \n", - "4 NaN 1819.896973 2059.760010 14.338 12.723 ... \n", - "\n", - " is_operation_normal_005 is_operation_normal_006 wind_vane_005 \\\n", - "0 True True 3.299 \n", - "1 True True 1.825 \n", - "2 True True 8.314 \n", - "3 True True 2.384 \n", - "4 True True 17.271 \n", - "\n", - " target_yaw_offset_005 control_mode wd_smarteole ws_smarteole \\\n", - "0 -0.0 baseline 251.041672 12.582482 \n", - "1 -0.0 baseline 251.282684 12.823891 \n", - "2 -0.0 baseline 252.874130 12.859400 \n", - "3 -0.0 baseline 251.341553 13.426766 \n", - "4 -0.0 baseline 253.343018 13.225105 \n", - "\n", - " pow_ref_smarteole ti wd \n", - "0 2021.060059 0.11 251.377068 \n", - "1 2022.767212 0.11 251.485048 \n", - "2 2034.417480 0.11 253.473037 \n", - "3 2039.458252 0.11 251.104001 \n", - "4 2048.116943 0.11 255.679234 \n", - "\n", - "[5 rows x 37 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Specify SCADA file path and load the dataframe\n", - "scada_path = os.path.join(\n", - " Path.cwd(), \"postprocessed\", \"df_scada_data_60s_filtered_and_northing_calibrated.pkl\"\n", - ")\n", - "df_scada = pd.read_pickle(scada_path)\n", - "\n", - "# Preview SCADA\n", - "df_scada.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['time', 'pow_000', 'pow_001', 'pow_002', 'pow_003', 'pow_004',\n", - " 'pow_005', 'pow_006', 'ws_000', 'ws_001', 'ws_002', 'ws_003', 'ws_004',\n", - " 'ws_005', 'ws_006', 'wd_000', 'wd_001', 'wd_002', 'wd_003', 'wd_004',\n", - " 'wd_005', 'wd_006', 'is_operation_normal_000',\n", - " 'is_operation_normal_001', 'is_operation_normal_002',\n", - " 'is_operation_normal_003', 'is_operation_normal_004',\n", - " 'is_operation_normal_005', 'is_operation_normal_006', 'wind_vane_005',\n", - " 'target_yaw_offset_005', 'control_mode', 'wd_smarteole', 'ws_smarteole',\n", - " 'pow_ref_smarteole', 'ti', 'wd'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_scada.columns" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare SCADA for Computing Energy Ratios (Baseline + Wake Steering Cases)\n", - "\n", - "The energy ratio class as presently implemented requires explicit identification of the dataframe of the reference wind direction, wind speed, and power columns: \"wd,\" \"ws,\" and \"pow_ref.\" Here, these will be set equal to the reference variables used in the SMARTEOLE wake steering experiment, which was computed in \"02_download_and_format_dataset.ipynb\"." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Specify offsets\n", - "start_of_offset = 200 # deg\n", - "end_of_offset = 240 # deg" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Limit SCADA to this region\n", - "df_scada = df_scada[\n", - " (df_scada.wd_smarteole > (start_of_offset - 20))\n", - " & (df_scada.wd_smarteole < (end_of_offset + 20))\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Assign wd, ws and pow ref and subset SCADA based on reference variables\n", - "# used in the SMARTEOLE wake steering experiment (TODO reference the experiment)\n", - "df_scada = df_scada.assign(\n", - " wd=lambda df_: df_[\"wd_smarteole\"],\n", - " ws=lambda df_: df_[\"ws_smarteole\"],\n", - " pow_ref=lambda df_: df_[\"pow_ref_smarteole\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# For tuning grab the reference, control and test turbines\n", - "ref_turbs = [0, 1, 2, 6]\n", - "test_turbs = [4]\n", - "control_turbs = [5]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Split SCADA into baseline and wake steeering (controlled)\n", - "df_scada_baseline = df_scada[df_scada.control_mode == \"baseline\"]\n", - "df_scada_controlled = df_scada[df_scada.control_mode == \"controlled\"]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load FLORIS model\n", - "\n", - "Specify the path of the Empirical Gaussian FLORIS Model (emgauss) YAML file. Instantiate the FLORIS model using this file. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "fm, _ = load_floris_smarteole(wake_model=\"emgauss\")\n", - "\n", - "# Define D\n", - "D = fm.core.farm.rotor_diameters[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "## Tune the wake expansion" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "wake_expansion_rates = np.arange(start=0.0005, stop=0.025, step=0.0005)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[43.27689076 43.14938946 43.02208633 42.89491991 42.76795681 42.64123563\n", - " 42.51456112 42.38781212 42.26106336 42.13444535 42.00752876 41.88067234\n", - " 41.75392085 41.62696185 41.49992442 41.3729645 41.24591003 41.11873161\n", - " 40.99155735 40.86430637 40.73721487 40.61018549 40.48326752 40.35645899\n", - " 40.22977133 40.10310542 39.97661195 39.85015656 39.72402267 39.59823444\n", - " 39.47271448 39.34761402 39.22287124 39.09839253 38.97415297 38.85027817\n", - " 38.72681744 38.60367319 38.48086052 38.35853918 38.23665281 38.11512427\n", - " 37.99400435 37.87323163 37.75282657 37.63283634 37.51324767 37.39406121\n", - " 37.27528243] 39.91532211996801\n" - ] - } - ], - "source": [ - "df_scada = df_scada_baseline.copy()\n", - "floris_wake_losses, scada_wake_loss = ft.sweep_velocity_model_parameter_for_overall_wake_losses(\n", - " parameter=[\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " value_candidates=wake_expansion_rates,\n", - " df_scada_in=df_scada,\n", - " fm_in=fm,\n", - " param_idx=0,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " wd_min=200, # FOCUS ON WAKE REGION WITH CONTROL\n", - " wd_max=225, # FOCUS ON WAKE REGION WITH CONTROL\n", - ")\n", - "print(floris_wake_losses, scada_wake_loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Wake Loss')" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(\n", - " floris_wake_losses, scada_wake_loss, wake_expansion_rates, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Wake Expansion Parameter\")\n", - "ax.set_ylabel(\"Percent Wake Loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Compare energy ratios before and after tuning\n", - "# Apply the best fit\n", - "fm_tuned = fm.copy()\n", - "fm_tuned.set_param(\n", - " [\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " param_idx=0,\n", - " value=best_param,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Comparing pre/post tuning FLORIS')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_floris_pre = resim_floris(fm, df_scada)\n", - "df_floris_tuned = resim_floris(fm_tuned, df_scada)\n", - "\n", - "a_in = AnalysisInput(\n", - " [df_scada, df_floris_pre, df_floris_tuned], [\"SCADA\", \"FLORIS default\", \"FLORIS tuned\"]\n", - ")\n", - "\n", - "er_out = er.compute_energy_ratio(\n", - " a_in,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " use_predefined_wd=True,\n", - " use_predefined_ws=True,\n", - " wd_step=2.0,\n", - " ws_step=1.0,\n", - " N=40,\n", - ")\n", - "ax = er_out.plot_energy_ratios(\n", - " overlay_frequency=True, color_dict={\"SCADA\": \"k\", \"FLORIS default\": \"b\", \"FLORIS tuned\": \"r\"}\n", - ")\n", - "ax[0].set_title(\"Comparing pre/post tuning FLORIS\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# Assign the new parameter\n", - "fm = fm_tuned" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Identify wd_std" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# Use the same wd region\n", - "min_wd = 200\n", - "max_wd = 225\n", - "\n", - "df_scada = df_scada_baseline.copy()\n", - "df_scada = df_scada[df_scada.wd > min_wd]\n", - "df_scada = df_scada[df_scada.wd <= max_wd]\n", - "\n", - "min_ws = np.floor(np.min([df_scada_baseline.ws.min(), df_scada_baseline.ws.min()]))\n", - "max_ws = np.ceil(np.max([df_scada_baseline.ws.max(), df_scada_baseline.ws.max()]))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 10:26:20\u001b[0m Generating a df_approx table of FLORIS solutions covering a total of 525 cases.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Finished calculating the FLORIS solutions for the dataframe.\n" - ] - } - ], - "source": [ - "# Make approximate tables\n", - "wd_array = np.arange(min_wd, max_wd, 1.0)\n", - "ws_array = np.arange(min_ws, max_ws, 1.0)\n", - "\n", - "df_approx = ftools.calc_floris_approx_table(\n", - " fm, wd_array=wd_array, ws_array=ws_array, ti_array=np.array([0.1])\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "df_scada = df_scada[\n", - " [\"ti\", \"wd\", \"ws\", \"pow_000\", \"pow_001\", \"pow_002\", \"pow_003\", \"pow_004\", \"pow_005\", \"pow_006\"]\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['ti', 'wd', 'ws', 'pow_000', 'pow_001', 'pow_002', 'pow_003', 'pow_004',\n", - " 'pow_005', 'pow_006'],\n", - " dtype='object')" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_scada.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df_approx: (200.000, 224.000)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Finished interpolation in 0.011 seconds.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df_approx: (200.000, 224.000)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Finished interpolation in 0.005 seconds.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m minimum/maximum value in df_approx: (200.000, 224.000)\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 10:26:20\u001b[0m Finished interpolation in 0.068 seconds.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df_approx: (200.000, 224.000)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Finished interpolation in 0.004 seconds.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m \u001b[33mWarning: not mirroring NaNs from the raw data to the FLORIS predictions. This may skew your energy ratios.\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Identified the following grid type: 2d.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m \u001b[33mWarning: the values in df[wd] exceed the range in the precalculated solutions df_fi_approx[wd].\u001b[0m\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df: (200.003, 224.998)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m minimum/maximum value in df_approx: (200.000, 224.000)\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Mapping the precalculated solutions from FLORIS to the dataframe...\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Creating a gridded interpolant with interpolation method 'linear'.\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Interpolating pow for all turbines...\n", - "\u001b[32m2024-10-16 10:26:21\u001b[0m Finished interpolation in 0.010 seconds.\n" - ] - } - ], - "source": [ - "# Select the values to check\n", - "wd_std_range = [0, 1, 2, 3, 4]\n", - "\n", - "er_error, df_list = ft.sweep_wd_std_for_er(\n", - " wd_std_range,\n", - " df_scada,\n", - " df_approx,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select the best result\n", - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wd_std(er_error, wd_std_range, ax=ax)" + "We strongly recommend instead using the replacement ModelFit package, demonstrated the\n", + "examples_smarteole/11_model_tuning_with_model_fit.ipynb." ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the values\n", - "fig, ax = plt.subplots()\n", - "\n", - "ax.plot(df_list[0][\"wd_bin\"], df_list[0][\"SCADA\"].values, color=\"k\", lw=5, label=\"SCADA\")\n", - "\n", - "for i, wd_std in enumerate(wd_std_range):\n", - " ax.plot(df_list[i][\"wd_bin\"], df_list[i][\"FLORIS\"].values, label=wd_std)\n", - "\n", - "ax.grid()\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate horizontal deflection gains" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "hor_def_gains = np.arange(start=0.5, stop=5, step=0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the yaw angle matrix\n", - "yaw_vec = df_scada_controlled.wind_vane_005\n", - "\n", - "yaw_angles = np.zeros((yaw_vec.shape[0], 7))\n", - "yaw_angles[:, control_turbs[0]] = yaw_vec" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "floris_uplifts, scada_uplift = ft.sweep_deflection_parameter_for_total_uplift(\n", - " parameter=[\n", - " \"wake\",\n", - " \"wake_deflection_parameters\",\n", - " \"empirical_gauss\",\n", - " \"horizontal_deflection_gain_D\",\n", - " ],\n", - " value_candidates=hor_def_gains,\n", - " df_scada_baseline_in=df_scada_baseline,\n", - " df_scada_wakesteering_in=df_scada_controlled,\n", - " fm_in=fm,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " yaw_angles_wakesteering=yaw_angles,\n", - " ws_min=5,\n", - " wd_min=205,\n", - " wd_max=225,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Uplift')" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(floris_uplifts, scada_uplift, hor_def_gains, ax=ax)\n", - "ax.set_xlabel(\"Hor Def Parameter\")\n", - "ax.set_ylabel(\"Percent Uplift\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples_smarteole/08_emgauss_tuning_day_night.ipynb b/examples_smarteole/08_emgauss_tuning_day_night.ipynb index dbcab546..a0af4153 100644 --- a/examples_smarteole/08_emgauss_tuning_day_night.ipynb +++ b/examples_smarteole/08_emgauss_tuning_day_night.ipynb @@ -5,781 +5,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Demonstrate adding day and night to a scada dataframe\n", + "Calibrating FLORIS models using the floris_tuning package is deprecated as of FLASC v2.4.\n", + "If you are looking for these examples, please see FLASC v2.3\n", + "(https://github.com/NREL/flasc/releases/tag/v2.3)\n", "\n", - "This notebook shows how, given lat/long and UTC time, a column with a boolean is_day value is added\n", - "This can be useful for a quick partinioning of the data into these two bins" + "We strongly recommend instead using the replacement ModelFit package, demonstrated the\n", + "examples_smarteole/11_model_tuning_with_model_fit.ipynb." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/anaconda3/envs/flasc-reqs/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.3' currently installed).\n", - " from pandas.core.computation.check import NUMEXPR_INSTALLED\n" - ] - } - ], - "source": [ - "from pathlib import Path\n", - "\n", - "import pandas as pd\n", - "\n", - "from flasc.data_processing.dataframe_manipulations import (\n", - " is_day_or_night,\n", - " plot_sun_altitude_with_day_night_color,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import flasc.model_fitting.floris_tuning as ft\n", - "from flasc.analysis import energy_ratio as er\n", - "from flasc.analysis.analysis_input import AnalysisInput\n", - "from flasc.utilities.tuner_utilities import resim_floris\n", - "from flasc.utilities.utilities_examples import load_floris_smarteole" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Suppress warnings\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 0: Demonstrate day/night selection\n", - "\n", - "Load the processed SCADA data with power curve filtering and northing calibration applied and apply day/night identification in various ways" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "root_path = Path.cwd()\n", - "f = root_path / \"postprocessed\" / \"df_scada_data_60s_filtered_and_northing_calibrated.pkl\"\n", - "df_scada = pd.read_pickle(f)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Add is_day flag\n", - "\n", - "Use the approximate lat/long of the wind farm to identify day/night using sun altitude" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'First 5000 points')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "latitude = 49.8435\n", - "longitude = 2.801556\n", - "\n", - "# Compute day/night in default settings and plot\n", - "df_scada = is_day_or_night(df_scada, latitude, longitude)\n", - "\n", - "# Plot the day/night data\n", - "fig, ax = plt.subplots(1, 2, sharey=True)\n", - "fig.set_size_inches(10, 5)\n", - "plot_sun_altitude_with_day_night_color(df_scada, ax=ax[0])\n", - "plot_sun_altitude_with_day_night_color(df_scada.iloc[:5000], ax=ax[1])\n", - "\n", - "ax[0].set_title(\"Full SCADA record\")\n", - "ax[1].set_title(\"First 5000 points\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'First 5000 points')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Use lag feature\n", - "df_scada = is_day_or_night(df_scada, latitude, longitude, lag_hours=1)\n", - "\n", - "# Plot the day/night data\n", - "fig, ax = plt.subplots(1, 2, sharey=True)\n", - "fig.set_size_inches(10, 5)\n", - "plot_sun_altitude_with_day_night_color(df_scada, ax=ax[0])\n", - "plot_sun_altitude_with_day_night_color(df_scada.iloc[:5000], ax=ax[1])\n", - "\n", - "ax[0].set_title(\"Full SCADA record\")\n", - "ax[1].set_title(\"First 5000 points\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'First 5000 points')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Use sunrise, sunset altitude specification feature\n", - "df_scada = is_day_or_night(df_scada, latitude, longitude, sunrise_altitude=20, sunset_altitude=-10)\n", - "\n", - "# Plot the day/night data\n", - "fig, ax = plt.subplots(1, 2, sharey=True)\n", - "fig.set_size_inches(10, 5)\n", - "plot_sun_altitude_with_day_night_color(df_scada, ax=ax[0])\n", - "plot_sun_altitude_with_day_night_color(df_scada.iloc[:5000], ax=ax[1])\n", - "\n", - "ax[0].set_title(\"Full SCADA record\")\n", - "ax[1].set_title(\"First 5000 points\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Step 1: Add day-night flag to dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute one more time with defaults\n", - "df_scada = is_day_or_night(df_scada, latitude, longitude)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Limit SCADA data to region of wake steering\n", - "\n", - "# Specify offsets\n", - "start_of_offset = 200 # deg\n", - "end_of_offset = 240 # deg\n", - "\n", - "# Limit SCADA to this region\n", - "df_scada = df_scada[\n", - " (df_scada.wd_smarteole > (start_of_offset - 20))\n", - " & (df_scada.wd_smarteole < (end_of_offset + 20))\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# Assign wd, ws and pow ref and subset SCADA based on reference variables used\n", - "# in the SMARTEOLE wake steering experiment (TODO reference the experiment)\n", - "df_scada = df_scada.assign(\n", - " wd=lambda df_: df_[\"wd_smarteole\"],\n", - " ws=lambda df_: df_[\"ws_smarteole\"],\n", - " pow_ref=lambda df_: df_[\"pow_ref_smarteole\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# For tuning grab the reference, control and test turbines\n", - "ref_turbs = [0, 1, 2, 6]\n", - "test_turbs = [4]\n", - "control_turbs = [5]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# Split SCADA into baseline and wake steeering (controlled)\n", - "df_scada_baseline = df_scada[df_scada.control_mode == \"baseline\"]\n", - "df_scada_controlled = df_scada[df_scada.control_mode == \"controlled\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_scada_baseline_day = df_scada_baseline[df_scada_baseline.is_day]\n", - "df_scada_baseline_night = df_scada_baseline[~df_scada_baseline.is_day]\n", - "df_scada_controlled_day = df_scada_controlled[df_scada_controlled.is_day]\n", - "df_scada_controlled_night = df_scada_controlled[~df_scada_controlled.is_day]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load the FLORIS model" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "fm, _ = load_floris_smarteole(wake_model=\"emgauss\")\n", - "D = fm.core.farm.rotor_diameters[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare expansion tuning" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "wake_expansion_rates = np.arange(start=0.0005, stop=0.025, step=0.0005)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Day Time" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[49.96312538 49.82663037 49.69014546 49.55376148 49.41760983 49.28169107\n", - " 49.14567933 49.00938806 48.87288826 48.73656018 48.59993532 48.46330975\n", - " 48.32684769 48.19016309 48.05326386 47.9163649 47.77916615 47.64174286\n", - " 47.50439982 47.36682279 47.22935417 47.09184541 46.95443031 46.81714668\n", - " 46.67989554 46.54276418 46.40566074 46.26859597 46.13186203 45.99566563\n", - " 45.85981792 45.72437105 45.58945632 45.45479906 45.32028706 45.18596406\n", - " 45.05204563 44.91833188 44.78485271 44.6519688 44.51950058 44.38738989\n", - " 44.25555488 44.12398089 43.99272673 43.86186534 43.7314794 43.60158152\n", - " 43.47218401] 45.00502722061688\n" - ] - } - ], - "source": [ - "df_scada = df_scada_baseline_day.copy()\n", - "floris_wake_losses, scada_wake_loss = ft.sweep_velocity_model_parameter_for_overall_wake_losses(\n", - " parameter=[\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " value_candidates=wake_expansion_rates,\n", - " df_scada_in=df_scada,\n", - " fm_in=fm,\n", - " param_idx=0,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " wd_min=200, # FOCUS ON WAKE REGION WITH CONTROL\n", - " wd_max=225, # FOCUS ON WAKE REGION WITH CONTROL\n", - ")\n", - "print(floris_wake_losses, scada_wake_loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Wake Loss')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param_day = ft.select_best_wake_model_parameter(\n", - " floris_wake_losses, scada_wake_loss, wake_expansion_rates, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Wake Expansion Parameter (day time)\")\n", - "ax.set_ylabel(\"Percent Wake Loss\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Night Time\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[38.39640696 38.27545961 38.15484804 38.0343994 37.91413428 37.79411179\n", - " 37.67423966 37.55444961 37.4348068 37.31526599 37.19542619 37.07569498\n", - " 36.95602116 36.83614784 36.7163013 36.59658226 36.47692097 36.35721384\n", - " 36.23744907 36.11772196 35.99819231 35.87880321 35.75953617 35.64036197\n", - " 35.52137821 35.40233919 35.28357955 35.16485573 35.04645069 34.92824456\n", - " 34.81025154 34.69269265 34.57535392 34.45828786 34.34153203 34.22526718\n", - " 34.10941994 33.99396893 33.87892555 33.76429372 33.6501105 33.53628746\n", - " 33.42296999 33.3100638 33.197558 33.08548573 32.97376978 32.86239136\n", - " 32.75135086] 36.30962235213149\n" - ] - } - ], - "source": [ - "df_scada = df_scada_baseline_night.copy()\n", - "floris_wake_losses, scada_wake_loss = ft.sweep_velocity_model_parameter_for_overall_wake_losses(\n", - " parameter=[\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " value_candidates=wake_expansion_rates,\n", - " df_scada_in=df_scada,\n", - " fm_in=fm,\n", - " param_idx=0,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " wd_min=200, # FOCUS ON WAKE REGION WITH CONTROL\n", - " wd_max=225, # FOCUS ON WAKE REGION WITH CONTROL\n", - ")\n", - "print(floris_wake_losses, scada_wake_loss)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Wake Loss')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param_night = ft.select_best_wake_model_parameter(\n", - " floris_wake_losses, scada_wake_loss, wake_expansion_rates, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Wake Expansion Parameter (night time)\")\n", - "ax.set_ylabel(\"Percent Wake Loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "## Make models of FLORIS for daytime and night time" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "fm_day = fm.copy()\n", - "fm_night = fm.copy()\n", - "\n", - "fm_day.set_param(\n", - " [\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " param_idx=0,\n", - " value=best_param_day,\n", - ")\n", - "\n", - "fm_night.set_param(\n", - " [\"wake\", \"wake_velocity_parameters\", \"empirical_gauss\", \"wake_expansion_rates\"],\n", - " param_idx=0,\n", - " value=best_param_night,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# Resimulate FLORIS\n", - "df_floris_day = resim_floris(fm_day, df_scada_baseline_day)\n", - "df_floris_night = resim_floris(fm_night, df_scada_baseline_night)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Comparing pre/post tuning FLORIS')" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compare energy ratios\n", - "\n", - "a_in = AnalysisInput(\n", - " [df_scada_baseline_day, df_floris_day, df_scada_baseline_night, df_floris_night],\n", - " [\"SCADA [Day]\", \"FLORIS [Day]\", \"SCADA [Night]\", \"FLORIS [Night]\"],\n", - ")\n", - "\n", - "er_out = er.compute_energy_ratio(\n", - " a_in,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " use_predefined_wd=True,\n", - " use_predefined_ws=True,\n", - " wd_step=2.0,\n", - " ws_step=1.0,\n", - " N=40,\n", - ")\n", - "ax = er_out.plot_energy_ratios(\n", - " overlay_frequency=True,\n", - " color_dict={\n", - " \"SCADA [Day]\": \"orange\",\n", - " \"FLORIS [Day]\": \"r\",\n", - " \"SCADA [Night]\": \"k\",\n", - " \"FLORIS [Night]\": \"navy\",\n", - " },\n", - ")\n", - "ax[0].set_title(\"Comparing pre/post tuning FLORIS\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Horizontal deflection gains" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "hor_def_gains = np.arange(start=0.25, stop=4, step=0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the yaw angle matrix\n", - "yaw_vec_day = df_scada_controlled_day.wind_vane_005\n", - "yaw_vec_night = df_scada_controlled_night.wind_vane_005\n", - "\n", - "yaw_angles_day = np.zeros((yaw_vec_day.shape[0], 7))\n", - "yaw_angles_day[:, control_turbs[0]] = yaw_vec_day\n", - "\n", - "yaw_angles_night = np.zeros((yaw_vec_night.shape[0], 7))\n", - "yaw_angles_night[:, control_turbs[0]] = yaw_vec_night" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Daytime" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "floris_uplifts, scada_uplift = ft.sweep_deflection_parameter_for_total_uplift(\n", - " parameter=[\n", - " \"wake\",\n", - " \"wake_deflection_parameters\",\n", - " \"empirical_gauss\",\n", - " \"horizontal_deflection_gain_D\",\n", - " ],\n", - " value_candidates=hor_def_gains,\n", - " df_scada_baseline_in=df_scada_baseline_day,\n", - " df_scada_wakesteering_in=df_scada_controlled_day,\n", - " fm_in=fm_day,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " yaw_angles_wakesteering=yaw_angles_day,\n", - " ws_min=5,\n", - " wd_min=205,\n", - " wd_max=225,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Uplift')" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param_day = ft.select_best_wake_model_parameter(\n", - " floris_uplifts, scada_uplift, hor_def_gains, ax=ax\n", - ")\n", - "ax.set_xlabel(\"Hor Def Parameter (day time)\")\n", - "ax.set_ylabel(\"Percent Uplift\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above demonstrates a potential issue with how we are fitting the \n", - "horizontal deflection parameter. Yawing reduces thrust so can produce a small uplift\n", - "even without any wake deflection actually occuring. We are looking into ways to \n", - "improve the tuning of the horizontal deflection parameter." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Night-time" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "floris_uplifts, scada_uplift = ft.sweep_deflection_parameter_for_total_uplift(\n", - " parameter=[\n", - " \"wake\",\n", - " \"wake_deflection_parameters\",\n", - " \"empirical_gauss\",\n", - " \"horizontal_deflection_gain_D\",\n", - " ],\n", - " value_candidates=hor_def_gains,\n", - " df_scada_baseline_in=df_scada_baseline_night,\n", - " df_scada_wakesteering_in=df_scada_controlled_night,\n", - " fm_in=fm_night,\n", - " ref_turbines=ref_turbs,\n", - " test_turbines=test_turbs,\n", - " yaw_angles_wakesteering=yaw_angles_night,\n", - " ws_min=5,\n", - " wd_min=205,\n", - " wd_max=225,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Percent Uplift')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "best_param = ft.select_best_wake_model_parameter(floris_uplifts, scada_uplift, hor_def_gains, ax=ax)\n", - "ax.set_xlabel(\"Hor Def Parameter (night time)\")\n", - "ax.set_ylabel(\"Percent Uplift\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples_smarteole/11_model_tuning_with_model_fit.ipynb b/examples_smarteole/11_model_tuning_with_model_fit.ipynb new file mode 100644 index 00000000..c12886d9 --- /dev/null +++ b/examples_smarteole/11_model_tuning_with_model_fit.ipynb @@ -0,0 +1,1254 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tuning FLORIS using ModelFit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Demonstrate tuning of FLORIS to the SCADA data using the ModelFit object" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from optuna.visualization.matplotlib import (\n", + " plot_contour,\n", + " plot_slice,\n", + ")\n", + "\n", + "from flasc.data_processing.dataframe_manipulations import (\n", + " is_day_or_night,\n", + ")\n", + "from flasc.model_fitting.cost_library import CostFunctionBase, TurbinePowerMeanAbsoluteError\n", + "from flasc.model_fitting.model_fit import ModelFit\n", + "from flasc.model_fitting.opt_library import extract_optuna_trial_data, opt_optuna\n", + "from flasc.utilities.utilities_examples import load_floris_smarteole" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of trials per optimization\n", + "n_trials = 20" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data and prepare data\n", + "\n", + "Use previously definied 60s data and condition the data as before" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in data\n", + "root_path = Path.cwd()\n", + "f = root_path / \"postprocessed\" / \"df_scada_data_60s_filtered_and_northing_calibrated.pkl\"\n", + "df_scada = pd.read_pickle(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Add day/night boolean to SCADA data\n", + "latitude = 49.8435\n", + "longitude = 2.801556\n", + "\n", + "# Compute day/night in default settings and plot\n", + "df_scada = is_day_or_night(df_scada, latitude, longitude)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Limit SCADA data to region of wake steering\n", + "\n", + "# Specify offsets\n", + "start_of_offset = 200 # deg\n", + "end_of_offset = 240 # deg\n", + "\n", + "# Limit SCADA to this region\n", + "df_scada = df_scada[\n", + " (df_scada.wd_smarteole > (start_of_offset - 20))\n", + " & (df_scada.wd_smarteole < (end_of_offset + 20))\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Assign wd, ws and pow ref and subset SCADA based on reference variables used\n", + "# in the SMARTEOLE wake steering experiment (TODO reference the experiment)\n", + "df_scada = df_scada.assign(\n", + " wd=lambda df_: df_[\"wd_smarteole\"],\n", + " ws=lambda df_: df_[\"ws_smarteole\"],\n", + " pow_ref=lambda df_: df_[\"pow_ref_smarteole\"],\n", + ").reset_index(drop=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load FLORIS Model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fm, _ = load_floris_smarteole(wake_model=\"emgauss\")\n", + "D = fm.core.farm.rotor_diameters[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split the data into sub groups" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Split SCADA into baseline and wake steeering (controlled)\n", + "df_scada_baseline = df_scada[df_scada.control_mode == \"baseline\"].reset_index(drop=True).copy()\n", + "df_scada_controlled = df_scada[df_scada.control_mode == \"controlled\"].reset_index(drop=True).copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Further split SCADA into day and night\n", + "df_scada_baseline_day = df_scada_baseline[df_scada_baseline.is_day].reset_index(drop=True).copy()\n", + "df_scada_baseline_night = df_scada_baseline[~df_scada_baseline.is_day].reset_index(drop=True).copy()\n", + "df_scada_controlled_day = (\n", + " df_scada_controlled[df_scada_controlled.is_day].reset_index(drop=True).copy()\n", + ")\n", + "df_scada_controlled_night = (\n", + " df_scada_controlled[~df_scada_controlled.is_day].reset_index(drop=True).copy()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tuning recovery on baseline data\n", + "\n", + "The first element of the wake expansion parameter array is named we_1 in this analysis. It governs the wake expansion in the empirical gaussian model up to the first defined breakpoint (often 10D). Given the close spacing it is only necessary to tune this parameter." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First define the parameter for model fit" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Just tune the first wake expansion parameter\n", + "parameter_list = [\n", + " (\n", + " \"wake\",\n", + " \"wake_velocity_parameters\",\n", + " \"empirical_gauss\",\n", + " \"wake_expansion_rates\",\n", + " )\n", + "]\n", + "\n", + "parameter_name_list = [\n", + " \"we_1\",\n", + "]\n", + "\n", + "parameter_range_list = [\n", + " (0.0, 0.05),\n", + "]\n", + "\n", + "parameter_index_list = [0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom cost function" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# ModelFit provides a number of pre-defined cost functions in the cost_library module\n", + "# However, there is also flexibility to define custom cost function. Custom cost functions\n", + "# should inherit from CostFunctionBase and implement the cost() method.\n", + "\n", + "# In this example, we define a custom cost function that compute the absolute error of the power\n", + "# for a single turbine (turbine 004) and use this for model tuning.\n", + "\n", + "\n", + "class Turbine004PowerErrorAbs(CostFunctionBase):\n", + " \"\"\"Custom cost function to compute the absolute error of turbine 004 power.\"\"\"\n", + "\n", + " def cost(self, df_floris):\n", + " \"\"\"Compute the absolute error of turbine 004 power.\"\"\"\n", + " return (self.df_scada[\"pow_004\"] - df_floris[\"pow_004\"]).abs().mean()\n", + "\n", + "\n", + "# Note that the provided TurbinePowerMeanAbsoluteError cost function can also be used for this\n", + "# purpose\n", + "cost_function_demo = TurbinePowerMeanAbsoluteError(turbine_power_subset=[4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Baseline tuning\n", + "\n", + "Tune we_1 to the best fit for all the baseline data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# We can also optionally df_scada_baseline to the cost function instantiation. We don't need to\n", + "# though, since it will be passed within the instantiation of ModelFit.\n", + "t004_cost_function = Turbine004PowerErrorAbs()\n", + "\n", + "# Model Fit object\n", + "mf = ModelFit(\n", + " df_scada_baseline,\n", + " fm,\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:37:19,926] A new study created in memory with name: ModelFit\n", + "[I 2025-09-15 09:37:21,494] Trial 0 finished with value: 147.3166409278423 and parameters: {'we_1': 0.01}. Best is trial 0 with value: 147.3166409278423.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:37:23,395] Trial 1 finished with value: 154.85182911860682 and parameters: {'we_1': 0.0004958531938503042}. Best is trial 0 with value: 147.3166409278423.\n", + "[I 2025-09-15 09:37:25,303] Trial 2 finished with value: 158.5500115420769 and parameters: {'we_1': 0.04344245871133526}. Best is trial 0 with value: 147.3166409278423.\n", + "[I 2025-09-15 09:37:27,266] Trial 3 finished with value: 146.0474656264817 and parameters: {'we_1': 0.013882442555512482}. Best is trial 3 with value: 146.0474656264817.\n", + "[I 2025-09-15 09:37:29,191] Trial 4 finished with value: 147.16258395884176 and parameters: {'we_1': 0.02408000417752251}. Best is trial 3 with value: 146.0474656264817.\n", + "[I 2025-09-15 09:37:31,106] Trial 5 finished with value: 145.88020423755225 and parameters: {'we_1': 0.015014265715594721}. Best is trial 5 with value: 145.88020423755225.\n", + "[I 2025-09-15 09:37:33,030] Trial 6 finished with value: 149.37318952488366 and parameters: {'we_1': 0.029185344698631058}. Best is trial 5 with value: 145.88020423755225.\n", + "[I 2025-09-15 09:37:34,954] Trial 7 finished with value: 149.44037622643 and parameters: {'we_1': 0.02931675266389946}. Best is trial 5 with value: 145.88020423755225.\n", + "[I 2025-09-15 09:37:36,934] Trial 8 finished with value: 145.9772199651148 and parameters: {'we_1': 0.014302941656348857}. Best is trial 5 with value: 145.88020423755225.\n", + "[I 2025-09-15 09:37:38,849] Trial 9 finished with value: 154.65994488588564 and parameters: {'we_1': 0.03793033059006276}. Best is trial 5 with value: 145.88020423755225.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:37:40,767] Trial 10 finished with value: 153.7331438845193 and parameters: {'we_1': 0.0015842717247131662}. Best is trial 5 with value: 145.88020423755225.\n", + "[I 2025-09-15 09:37:42,697] Trial 11 finished with value: 145.76576121204877 and parameters: {'we_1': 0.0168170410188809}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:44,645] Trial 12 finished with value: 146.28608593891866 and parameters: {'we_1': 0.021120010101192563}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:46,589] Trial 13 finished with value: 148.34102345380876 and parameters: {'we_1': 0.008171014164146244}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:48,541] Trial 14 finished with value: 145.80236056481942 and parameters: {'we_1': 0.017782544031131983}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:50,469] Trial 15 finished with value: 146.0386887296801 and parameters: {'we_1': 0.019891987917737608}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:52,369] Trial 16 finished with value: 151.88678207530685 and parameters: {'we_1': 0.033643787010318976}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:54,272] Trial 17 finished with value: 162.6844755279383 and parameters: {'we_1': 0.04916779975648167}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:56,163] Trial 18 finished with value: 148.78294600519268 and parameters: {'we_1': 0.007490060067322224}. Best is trial 11 with value: 145.76576121204877.\n", + "[I 2025-09-15 09:37:58,138] Trial 19 finished with value: 145.84648699465677 and parameters: {'we_1': 0.01836900578947348}. Best is trial 11 with value: 145.76576121204877.\n" + ] + } + ], + "source": [ + "# Run the optimization for n_trials\n", + "opt_result = opt_optuna(mf, timeout=None, n_trials=n_trials)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best we_1 from baseline tuning: 0.0168170410188809\n" + ] + } + ], + "source": [ + "# Print the best result\n", + "we_1_baseline = opt_result[\"optimized_parameter_values\"][0]\n", + "\n", + "print(f\"Best we_1 from baseline tuning: {we_1_baseline}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/zl/d69s6z796rs4fw08fnxrl0qxydms74/T/ipykernel_80827/2588499832.py:3: ExperimentalWarning: plot_slice is experimental (supported from v2.2.0). The interface can change in the future.\n", + " plot_slice(opt_result[\"optuna_study\"])\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Use the slice plot provided by optuna to visualize the values of the parameters\n", + "# considered within the optimization\n", + "plot_slice(opt_result[\"optuna_study\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tune the recovery to baseline day and night data\n", + "\n", + "Using the data split into day and night results, tune the recovery to each to see how the best fit changes under these different conditions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Daytime result" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:37:58,321] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:37:59,579] Trial 0 finished with value: 156.34623364294382 and parameters: {'we_1': 0.01836900578947348}. Best is trial 0 with value: 156.34623364294382.\n", + "[I 2025-09-15 09:38:00,646] Trial 1 finished with value: 157.49644104028576 and parameters: {'we_1': 0.028539042108595328}. Best is trial 0 with value: 156.34623364294382.\n", + "[I 2025-09-15 09:38:01,719] Trial 2 finished with value: 161.27188988721502 and parameters: {'we_1': 0.03844072292374351}. Best is trial 0 with value: 156.34623364294382.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:38:02,807] Trial 3 finished with value: 164.3899421263282 and parameters: {'we_1': 0.0025317934328297543}. Best is trial 0 with value: 156.34623364294382.\n", + "[I 2025-09-15 09:38:03,920] Trial 4 finished with value: 156.25039602854434 and parameters: {'we_1': 0.02051302904399372}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:04,991] Trial 5 finished with value: 156.2589816447109 and parameters: {'we_1': 0.01989588573470349}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:06,048] Trial 6 finished with value: 166.7450677124099 and parameters: {'we_1': 0.04879855408264258}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:07,185] Trial 7 finished with value: 166.53484970941483 and parameters: {'we_1': 0.04841878859449005}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:08,239] Trial 8 finished with value: 156.68006523473892 and parameters: {'we_1': 0.015843859488450945}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:09,322] Trial 9 finished with value: 157.3201814547293 and parameters: {'we_1': 0.01339438193730419}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:10,434] Trial 10 finished with value: 158.6873285485889 and parameters: {'we_1': 0.03231354207268599}. Best is trial 4 with value: 156.25039602854434.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:38:11,495] Trial 11 finished with value: 162.30334147542473 and parameters: {'we_1': 0.004951196677183446}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:12,591] Trial 12 finished with value: 156.3729869804682 and parameters: {'we_1': 0.02281229322532318}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:13,734] Trial 13 finished with value: 157.9534747369633 and parameters: {'we_1': 0.011727855345054328}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:14,783] Trial 14 finished with value: 156.53931004356798 and parameters: {'we_1': 0.024105898321544098}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:15,901] Trial 15 finished with value: 160.17588732498692 and parameters: {'we_1': 0.03605836879868042}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:16,971] Trial 16 finished with value: 159.8266766840192 and parameters: {'we_1': 0.008306065155565673}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:18,064] Trial 17 finished with value: 156.280025460809 and parameters: {'we_1': 0.019302118542134563}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:19,181] Trial 18 finished with value: 157.10228104977017 and parameters: {'we_1': 0.026990783461908495}. Best is trial 4 with value: 156.25039602854434.\n", + "[I 2025-09-15 09:38:20,324] Trial 19 finished with value: 163.54136970680702 and parameters: {'we_1': 0.042904268380222446}. Best is trial 4 with value: 156.25039602854434.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best we_1 for day: 0.02051302904399372\n" + ] + } + ], + "source": [ + "mf_day = ModelFit(\n", + " df_scada_baseline_day,\n", + " fm,\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + ")\n", + "\n", + "opt_result_day = opt_optuna(mf_day, timeout=None, n_trials=n_trials)\n", + "\n", + "we_1_baseline_day = opt_result_day[\"optimized_parameter_values\"][0]\n", + "\n", + "print(f\"Best we_1 for day: {we_1_baseline_day}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Night results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:38:20,343] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:38:21,422] Trial 0 finished with value: 152.91184441836586 and parameters: {'we_1': 0.042904268380222446}. Best is trial 0 with value: 152.91184441836586.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:38:22,425] Trial 1 finished with value: 142.69683390353217 and parameters: {'we_1': 0.0013622369997475293}. Best is trial 1 with value: 142.69683390353217.\n", + "[I 2025-09-15 09:38:23,442] Trial 2 finished with value: 147.0812505226484 and parameters: {'we_1': 0.03637757640035575}. Best is trial 1 with value: 142.69683390353217.\n", + "[I 2025-09-15 09:38:24,416] Trial 3 finished with value: 140.49596049232525 and parameters: {'we_1': 0.028099819734273654}. Best is trial 3 with value: 140.49596049232525.\n", + "[I 2025-09-15 09:38:25,415] Trial 4 finished with value: 150.53204345013333 and parameters: {'we_1': 0.040269978997943705}. Best is trial 3 with value: 140.49596049232525.\n", + "[I 2025-09-15 09:38:26,465] Trial 5 finished with value: 135.4313420789288 and parameters: {'we_1': 0.017690280109594066}. Best is trial 5 with value: 135.4313420789288.\n", + "[I 2025-09-15 09:38:27,573] Trial 6 finished with value: 138.46061581352618 and parameters: {'we_1': 0.024884408331232295}. Best is trial 5 with value: 135.4313420789288.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:38:28,576] Trial 7 finished with value: 140.9905748445735 and parameters: {'we_1': 0.003086985307952617}. Best is trial 5 with value: 135.4313420789288.\n", + "[I 2025-09-15 09:38:29,588] Trial 8 finished with value: 137.06968851011197 and parameters: {'we_1': 0.02225734278165056}. Best is trial 5 with value: 135.4313420789288.\n", + "[I 2025-09-15 09:38:30,609] Trial 9 finished with value: 135.55477384112712 and parameters: {'we_1': 0.011577190485724127}. Best is trial 5 with value: 135.4313420789288.\n", + "[I 2025-09-15 09:38:31,593] Trial 10 finished with value: 135.1679271295693 and parameters: {'we_1': 0.014357469585588735}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:32,559] Trial 11 finished with value: 135.2539783678549 and parameters: {'we_1': 0.01309573282065378}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:33,495] Trial 12 finished with value: 135.68521383749356 and parameters: {'we_1': 0.01113878509084191}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:34,480] Trial 13 finished with value: 135.18137404635456 and parameters: {'we_1': 0.014030731191904722}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:35,464] Trial 14 finished with value: 143.88989379878566 and parameters: {'we_1': 0.03258353708309794}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:36,515] Trial 15 finished with value: 137.11008118369733 and parameters: {'we_1': 0.008052238087025803}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:37,448] Trial 16 finished with value: 135.37356817131575 and parameters: {'we_1': 0.017414129697462834}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:38,406] Trial 17 finished with value: 135.7140002097328 and parameters: {'we_1': 0.018806195254971292}. Best is trial 10 with value: 135.1679271295693.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:38:39,357] Trial 18 finished with value: 138.6211020133817 and parameters: {'we_1': 0.005875940015448711}. Best is trial 10 with value: 135.1679271295693.\n", + "[I 2025-09-15 09:38:40,307] Trial 19 finished with value: 140.03904839047578 and parameters: {'we_1': 0.027422311575010155}. Best is trial 10 with value: 135.1679271295693.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best we_1 for night: 0.014357469585588735\n" + ] + } + ], + "source": [ + "mf_night = ModelFit(\n", + " df_scada_baseline_night,\n", + " fm,\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + ")\n", + "\n", + "opt_result_night = opt_optuna(mf_night, timeout=None, n_trials=n_trials)\n", + "\n", + "we_1_baseline_night = opt_result_night[\"optimized_parameter_values\"][0]\n", + "\n", + "print(f\"Best we_1 for night: {we_1_baseline_night}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing the results\n", + "\n", + "Notice the tuning parameters indicate less wake recovery in the night (smaller we_1) and more wake recovery in the day (larger we_1)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters:\n", + "All data: 0.017\n", + "Day data: 0.021\n", + "Night data: 0.014\n" + ] + } + ], + "source": [ + "# Print a small table with the best parameters\n", + "print(\"Best parameters:\")\n", + "print(f\"All data: {we_1_baseline:.3f}\")\n", + "print(f\"Day data: {we_1_baseline_day:.3f}\")\n", + "print(f\"Night data: {we_1_baseline_night:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the results for we_1 in a single plot\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "\n", + "# Get data for each study\n", + "all_data_params, all_data_costs, best_all_param, best_all_cost = extract_optuna_trial_data(\n", + " opt_result[\"optuna_study\"], \"we_1\"\n", + ")\n", + "day_params, day_costs, best_day_param, best_day_cost = extract_optuna_trial_data(\n", + " opt_result_day[\"optuna_study\"], \"we_1\"\n", + ")\n", + "night_params, night_costs, best_night_param, best_night_cost = extract_optuna_trial_data(\n", + " opt_result_night[\"optuna_study\"], \"we_1\"\n", + ")\n", + "\n", + "# Create plots\n", + "ax.plot(all_data_params, all_data_costs, label=\"All data\", marker=\"o\", color=\"black\")\n", + "ax.plot(day_params, day_costs, label=\"Day data\", marker=\"o\", color=\"orange\")\n", + "ax.plot(night_params, night_costs, label=\"Night data\", marker=\"o\", color=\"violet\")\n", + "\n", + "ax.axvline(best_all_param, color=\"black\", ls=\"--\")\n", + "ax.axvline(best_day_param, color=\"orange\", ls=\"--\")\n", + "ax.axvline(best_night_param, color=\"violet\", ls=\"--\")\n", + "\n", + "ax.set_title(\"we_1 Parameter Tuning Comparison\")\n", + "ax.set_ylabel(\"Cost / min(Cost)\")\n", + "ax.set_xlabel(\"we_1\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tune the deflection gain to the baseline data\n", + "\n", + "The deflection gain in the emgauss model defines the gain in the deflection of the wake at the downstream turbine following a yaw angle misalignment. A higher gain indicates more deflection of the wake." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Update the FLORIS model with the best values of we_1 for each case from the baseline data" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "fm_baseline = fm.copy()\n", + "fm_baseline_day = fm.copy()\n", + "fm_baseline_night = fm.copy()\n", + "\n", + "fm_baseline.set_param(parameter_list[0], we_1_baseline, 0)\n", + "fm_baseline_day.set_param(parameter_list[0], we_1_baseline_day, 0)\n", + "fm_baseline_night.set_param(parameter_list[0], we_1_baseline_night, 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tune the deflection parameter\n", + "\n", + "First define the deflection parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Just tune the first wake expansion parameter\n", + "parameter_list = [\n", + " (\n", + " \"wake\",\n", + " \"wake_deflection_parameters\",\n", + " \"empirical_gauss\",\n", + " \"horizontal_deflection_gain_D\",\n", + " )\n", + "]\n", + "\n", + "parameter_name_list = [\n", + " \"deflection_gain\",\n", + "]\n", + "\n", + "parameter_range_list = [\n", + " (0.0, 5.0),\n", + "]\n", + "\n", + "parameter_index_list = [None]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Also define the yaw angles to be resimulated in each case" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# Set the yaw angle matrix for when using all the controlled data\n", + "yaw_vec = df_scada_controlled.wind_vane_005\n", + "yaw_angles_controlled = np.zeros((yaw_vec.shape[0], 7))\n", + "yaw_angles_controlled[:, 5] = yaw_vec # Turbine 005 is the turbine implementing wake steering\n", + "\n", + "# Set the yaw angles for day and night data\n", + "yaw_vec_day = df_scada_controlled_day.wind_vane_005\n", + "yaw_angles_controlled_day = np.zeros((yaw_vec_day.shape[0], 7))\n", + "yaw_angles_controlled_day[:, 5] = yaw_vec_day\n", + "\n", + "yaw_vec_night = df_scada_controlled_night.wind_vane_005\n", + "yaw_angles_controlled_night = np.zeros((yaw_vec_night.shape[0], 7))\n", + "yaw_angles_controlled_night[:, 5] = yaw_vec_night" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tune the deflection parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:38:41,627] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:38:43,396] Trial 0 finished with value: 156.17705915506147 and parameters: {'deflection_gain': 3.0}. Best is trial 0 with value: 156.17705915506147.\n", + "[I 2025-09-15 09:38:45,216] Trial 1 finished with value: 156.15727664246447 and parameters: {'deflection_gain': 0.5978842667769285}. Best is trial 1 with value: 156.15727664246447.\n", + "[I 2025-09-15 09:38:47,049] Trial 2 finished with value: 156.26411842323694 and parameters: {'deflection_gain': 3.049247755303089}. Best is trial 1 with value: 156.15727664246447.\n", + "[I 2025-09-15 09:38:48,860] Trial 3 finished with value: 159.61140546461354 and parameters: {'deflection_gain': 4.370087375604924}. Best is trial 1 with value: 156.15727664246447.\n", + "[I 2025-09-15 09:38:50,677] Trial 4 finished with value: 155.12664008274623 and parameters: {'deflection_gain': 1.8996118334784184}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:38:52,466] Trial 5 finished with value: 155.57069083104608 and parameters: {'deflection_gain': 2.5765422938053417}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:38:54,259] Trial 6 finished with value: 155.26156133247713 and parameters: {'deflection_gain': 2.2230977217267474}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:38:56,079] Trial 7 finished with value: 158.98883246612482 and parameters: {'deflection_gain': 4.164973865898298}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:38:57,872] Trial 8 finished with value: 157.01744112121767 and parameters: {'deflection_gain': 0.1596158895243044}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:38:59,672] Trial 9 finished with value: 159.1536017654925 and parameters: {'deflection_gain': 4.220244066366448}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:39:01,463] Trial 10 finished with value: 155.3663265338881 and parameters: {'deflection_gain': 1.2240827171392659}. Best is trial 4 with value: 155.12664008274623.\n", + "[I 2025-09-15 09:39:03,266] Trial 11 finished with value: 155.11698339468836 and parameters: {'deflection_gain': 1.7380520985962469}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:05,067] Trial 12 finished with value: 155.12382655002932 and parameters: {'deflection_gain': 1.669964761479769}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:06,946] Trial 13 finished with value: 155.30120018542397 and parameters: {'deflection_gain': 1.3040498275441514}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:08,797] Trial 14 finished with value: 155.18725636849126 and parameters: {'deflection_gain': 1.4795312982739885}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:10,621] Trial 15 finished with value: 155.79940727791126 and parameters: {'deflection_gain': 0.8353279292538385}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:12,413] Trial 16 finished with value: 157.03711549848634 and parameters: {'deflection_gain': 3.4308723355773134}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:14,199] Trial 17 finished with value: 155.20585422190493 and parameters: {'deflection_gain': 2.1287319382812195}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:15,990] Trial 18 finished with value: 155.12587402108343 and parameters: {'deflection_gain': 1.8955696805447073}. Best is trial 11 with value: 155.11698339468836.\n", + "[I 2025-09-15 09:39:17,794] Trial 19 finished with value: 157.30686570025983 and parameters: {'deflection_gain': 3.5458138394492966}. Best is trial 11 with value: 155.11698339468836.\n" + ] + } + ], + "source": [ + "# Tune to all controlled data\n", + "mf_deflection = ModelFit(\n", + " df_scada_controlled,\n", + " fm_baseline, # Use the model tuned to all baseline data\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + " yaw_angles=yaw_angles_controlled,\n", + ")\n", + "\n", + "opt_result_deflection = opt_optuna(mf_deflection, timeout=None, n_trials=n_trials)\n", + "def_gain = opt_result_deflection[\"optimized_parameter_values\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:39:17,849] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:39:18,804] Trial 0 finished with value: 173.2959494567273 and parameters: {'deflection_gain': 3.0}. Best is trial 0 with value: 173.2959494567273.\n", + "[I 2025-09-15 09:39:19,736] Trial 1 finished with value: 172.59074272404135 and parameters: {'deflection_gain': 2.743170066924105}. Best is trial 1 with value: 172.59074272404135.\n", + "[I 2025-09-15 09:39:20,649] Trial 2 finished with value: 174.15138259176004 and parameters: {'deflection_gain': 3.2839420390158214}. Best is trial 1 with value: 172.59074272404135.\n", + "[I 2025-09-15 09:39:21,708] Trial 3 finished with value: 172.92507447188362 and parameters: {'deflection_gain': 2.8675370239995726}. Best is trial 1 with value: 172.59074272404135.\n", + "[I 2025-09-15 09:39:22,639] Trial 4 finished with value: 173.95517815563494 and parameters: {'deflection_gain': 3.220839036201478}. Best is trial 1 with value: 172.59074272404135.\n", + "[I 2025-09-15 09:39:23,628] Trial 5 finished with value: 170.32451271696348 and parameters: {'deflection_gain': 1.6846990357681308}. Best is trial 5 with value: 170.32451271696348.\n", + "[I 2025-09-15 09:39:24,561] Trial 6 finished with value: 169.27882646723455 and parameters: {'deflection_gain': 0.3114687450399023}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:25,466] Trial 7 finished with value: 174.61601966553752 and parameters: {'deflection_gain': 3.429367730239167}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:26,430] Trial 8 finished with value: 175.75066220147485 and parameters: {'deflection_gain': 3.7714737721489944}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:27,369] Trial 9 finished with value: 169.29994359805784 and parameters: {'deflection_gain': 0.1541403614525233}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:28,287] Trial 10 finished with value: 169.27972546586952 and parameters: {'deflection_gain': 0.2973008236317187}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:29,231] Trial 11 finished with value: 169.3376836168237 and parameters: {'deflection_gain': 0.04330299668671944}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:30,132] Trial 12 finished with value: 169.5485578154142 and parameters: {'deflection_gain': 1.0399143738175953}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:31,062] Trial 13 finished with value: 169.8965186815844 and parameters: {'deflection_gain': 1.3805682301604854}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:32,004] Trial 14 finished with value: 179.46988570531462 and parameters: {'deflection_gain': 4.77819724892195}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:32,970] Trial 15 finished with value: 169.3925360090303 and parameters: {'deflection_gain': 0.8198411529326021}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:33,907] Trial 16 finished with value: 170.71743068199436 and parameters: {'deflection_gain': 1.9168144806171608}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:34,814] Trial 17 finished with value: 169.28997633518824 and parameters: {'deflection_gain': 0.5403904471341138}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:35,720] Trial 18 finished with value: 171.19914997960984 and parameters: {'deflection_gain': 2.1656333321494756}. Best is trial 6 with value: 169.27882646723455.\n", + "[I 2025-09-15 09:39:36,625] Trial 19 finished with value: 169.7348715413786 and parameters: {'deflection_gain': 1.238216035106806}. Best is trial 6 with value: 169.27882646723455.\n" + ] + } + ], + "source": [ + "# Tune to the day data\n", + "mf_day = ModelFit(\n", + " df_scada_controlled_day,\n", + " fm_baseline_day, # Use the model tuned to day-time baseline data\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + " yaw_angles=yaw_angles_controlled_day,\n", + ")\n", + "\n", + "opt_result_deflection_day = opt_optuna(mf_day, timeout=None, n_trials=n_trials)\n", + "def_gain_day = opt_result_deflection_day[\"optimized_parameter_values\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:39:36,642] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:39:37,615] Trial 0 finished with value: 140.1960859678203 and parameters: {'deflection_gain': 3.0}. Best is trial 0 with value: 140.1960859678203.\n", + "[I 2025-09-15 09:39:38,547] Trial 1 finished with value: 140.1843428947465 and parameters: {'deflection_gain': 2.981308462311717}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:39,570] Trial 2 finished with value: 142.55625488464923 and parameters: {'deflection_gain': 1.079297590160015}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:40,507] Trial 3 finished with value: 140.19760500017213 and parameters: {'deflection_gain': 3.0023592969438995}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:41,478] Trial 4 finished with value: 141.90416474490556 and parameters: {'deflection_gain': 4.115983468127084}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:42,570] Trial 5 finished with value: 145.10056445873875 and parameters: {'deflection_gain': 0.3170729055970051}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:43,566] Trial 6 finished with value: 140.38822571557125 and parameters: {'deflection_gain': 3.2407402874216484}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:44,505] Trial 7 finished with value: 141.9562529306359 and parameters: {'deflection_gain': 4.13836340125696}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:45,504] Trial 8 finished with value: 141.74580941952883 and parameters: {'deflection_gain': 1.3862516273199073}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:46,492] Trial 9 finished with value: 140.19541570725028 and parameters: {'deflection_gain': 2.42571980389327}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:47,478] Trial 10 finished with value: 144.00961928486285 and parameters: {'deflection_gain': 4.916541746234775}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:48,435] Trial 11 finished with value: 140.61492890523868 and parameters: {'deflection_gain': 1.9902393765028181}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:49,426] Trial 12 finished with value: 140.3780960409932 and parameters: {'deflection_gain': 2.1888075772307407}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:50,404] Trial 13 finished with value: 140.89550802775167 and parameters: {'deflection_gain': 3.625426532727069}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:51,435] Trial 14 finished with value: 140.24183776375583 and parameters: {'deflection_gain': 2.3559726384155275}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:52,419] Trial 15 finished with value: 141.19122782586967 and parameters: {'deflection_gain': 1.6400851276767445}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:53,464] Trial 16 finished with value: 143.46678907836568 and parameters: {'deflection_gain': 0.7832736508427112}. Best is trial 1 with value: 140.1843428947465.\n", + "[I 2025-09-15 09:39:54,459] Trial 17 finished with value: 140.11252985757952 and parameters: {'deflection_gain': 2.6716654154832367}. Best is trial 17 with value: 140.11252985757952.\n", + "[I 2025-09-15 09:39:55,430] Trial 18 finished with value: 140.73428331649686 and parameters: {'deflection_gain': 3.523692395340974}. Best is trial 17 with value: 140.11252985757952.\n", + "[I 2025-09-15 09:39:56,434] Trial 19 finished with value: 140.1131131860334 and parameters: {'deflection_gain': 2.701428860098234}. Best is trial 17 with value: 140.11252985757952.\n" + ] + } + ], + "source": [ + "# Tune to the night data\n", + "mf_night = ModelFit(\n", + " df_scada_controlled_night,\n", + " fm_baseline_night, # Use the model tuned to night-time baseline data\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + " yaw_angles=yaw_angles_controlled_night,\n", + ")\n", + "opt_result_deflection_night = opt_optuna(mf_night, timeout=None, n_trials=n_trials)\n", + "def_gain_night = opt_result_deflection_night[\"optimized_parameter_values\"][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compare the results for the deflection gain" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results indicate that the wake deflection gain is larger at night than at day. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters:\n", + "All data: 1.738\n", + "Day data: 0.311\n", + "Night data: 2.672\n" + ] + } + ], + "source": [ + "# Print a table with the best parameters\n", + "print(\"Best parameters:\")\n", + "print(f\"All data: {def_gain:.3f}\")\n", + "print(f\"Day data: {def_gain_day:.3f}\")\n", + "print(f\"Night data: {def_gain_night:.3f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make comparison plot of the results\n", + "\n", + "# Compare the results for deflection gain in a single plot\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "\n", + "# Get data for each study\n", + "all_data_params, all_data_costs, best_all_param, best_all_cost = extract_optuna_trial_data(\n", + " opt_result_deflection[\"optuna_study\"], \"deflection_gain\"\n", + ")\n", + "day_params, day_costs, best_day_param, best_day_cost = extract_optuna_trial_data(\n", + " opt_result_deflection_day[\"optuna_study\"], \"deflection_gain\"\n", + ")\n", + "night_params, night_costs, best_night_param, best_night_cost = extract_optuna_trial_data(\n", + " opt_result_deflection_night[\"optuna_study\"], \"deflection_gain\"\n", + ")\n", + "\n", + "# Create plots\n", + "ax.plot(all_data_params, all_data_costs, label=\"All data\", marker=\"o\", color=\"black\")\n", + "ax.plot(day_params, day_costs, label=\"Day data\", marker=\"o\", color=\"orange\")\n", + "ax.plot(night_params, night_costs, label=\"Night data\", marker=\"o\", color=\"violet\")\n", + "\n", + "ax.axvline(best_all_param, color=\"black\", ls=\"--\")\n", + "ax.axvline(best_day_param, color=\"orange\", ls=\"--\")\n", + "ax.axvline(best_night_param, color=\"violet\", ls=\"--\")\n", + "\n", + "ax.set_title(\"Deflection Gain Tuning Comparison\")\n", + "ax.set_ylabel(\"Cost / min(Cost)\")\n", + "ax.set_xlabel(\"Deflection Gain\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simultaneously tune the we_1 and deflection gain parameters\n", + "\n", + "As a final analysis, see what results would have been for all data case if we_1 and deflection were tuned simulaneously to the data\n", + "\n", + "Note results would not be expected to be exactly the same because the data used in training is now not the same (instead of tuning expansion to baseline data and deflection_gain to controlled data, we are tuning both to all data)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Redo parameter list to include both we_1 and deflection_gain\n", + "parameter_list = [\n", + " (\n", + " \"wake\",\n", + " \"wake_velocity_parameters\",\n", + " \"empirical_gauss\",\n", + " \"wake_expansion_rates\",\n", + " ),\n", + " (\n", + " \"wake\",\n", + " \"wake_deflection_parameters\",\n", + " \"empirical_gauss\",\n", + " \"horizontal_deflection_gain_D\",\n", + " ),\n", + "]\n", + "\n", + "parameter_name_list = [\n", + " \"we_1\",\n", + " \"deflection_gain\",\n", + "]\n", + "\n", + "parameter_range_list = [\n", + " (0.0, 0.05),\n", + " (0.0, 5.0),\n", + "]\n", + "\n", + "parameter_index_list = [0, None]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Redo yaw angle matrix to all data\n", + "yaw_vec = df_scada.wind_vane_005\n", + "yaw_angles_all = np.zeros((yaw_vec.shape[0], 7))\n", + "yaw_angles_all[:, 5] = yaw_vec" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:39:56,599] A new study created in memory with name: ModelFit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost object already has df_scada assigned. Overwriting.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[I 2025-09-15 09:40:00,493] Trial 0 finished with value: 155.25876129494173 and parameters: {'we_1': 0.027422311575010155, 'deflection_gain': 3.0}. Best is trial 0 with value: 155.25876129494173.\n", + "[I 2025-09-15 09:40:05,448] Trial 1 finished with value: 151.5032527786563 and parameters: {'we_1': 0.018807122096767673, 'deflection_gain': 0.9970356480769443}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:10,086] Trial 2 finished with value: 152.79551251384316 and parameters: {'we_1': 0.02411221036275259, 'deflection_gain': 1.5068449964624993}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:14,773] Trial 3 finished with value: 156.9870091495833 and parameters: {'we_1': 0.032081942449144, 'deflection_gain': 0.13687779319699}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:19,552] Trial 4 finished with value: 152.77698924914648 and parameters: {'we_1': 0.01911841159509305, 'deflection_gain': 3.1991760016016664}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:24,283] Trial 5 finished with value: 165.1510088507491 and parameters: {'we_1': 0.04489842874761582, 'deflection_gain': 0.9107386275094792}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:29,046] Trial 6 finished with value: 165.2718228976435 and parameters: {'we_1': 0.04400260078034057, 'deflection_gain': 2.728747467766203}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:33,758] Trial 7 finished with value: 155.85217741428372 and parameters: {'we_1': 0.01356186563309379, 'deflection_gain': 4.688740173835415}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:38,483] Trial 8 finished with value: 155.65710292557029 and parameters: {'we_1': 0.01467424883193032, 'deflection_gain': 4.67783335003628}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:43,141] Trial 9 finished with value: 154.83787965109616 and parameters: {'we_1': 0.01141985019366989, 'deflection_gain': 4.060820824084238}. Best is trial 1 with value: 151.5032527786563.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:40:47,858] Trial 10 finished with value: 158.90158657035676 and parameters: {'we_1': 0.000694653286781026, 'deflection_gain': 1.790792007791422}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:52,497] Trial 11 finished with value: 153.4426528991272 and parameters: {'we_1': 0.02047940064670033, 'deflection_gain': 3.4536205658757364}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:40:57,164] Trial 12 finished with value: 157.71740339547904 and parameters: {'we_1': 0.03357614465361406, 'deflection_gain': 1.8959736551913866}. Best is trial 1 with value: 151.5032527786563.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:41:01,967] Trial 13 finished with value: 157.09255251459962 and parameters: {'we_1': 0.004340224532106635, 'deflection_gain': 0.19929878162173387}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:06,628] Trial 14 finished with value: 153.03572275263082 and parameters: {'we_1': 0.018814273874823723, 'deflection_gain': 3.3936761498764207}. Best is trial 1 with value: 151.5032527786563.\n", + "\u001b[34mfloris.floris_model.FlorisModel\u001b[0m \u001b[1;30mWARNING\u001b[0m \u001b[33mSome velocities at the rotor are negative.\u001b[0m\n", + "[I 2025-09-15 09:41:11,315] Trial 15 finished with value: 154.39130717147512 and parameters: {'we_1': 0.006543300448793123, 'deflection_gain': 2.2406108736486354}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:15,963] Trial 16 finished with value: 154.40720178597658 and parameters: {'we_1': 0.027906467524508434, 'deflection_gain': 1.0562320446459146}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:20,595] Trial 17 finished with value: 161.77391887783065 and parameters: {'we_1': 0.036512073210358716, 'deflection_gain': 3.9826062600831222}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:25,305] Trial 18 finished with value: 151.74194069313455 and parameters: {'we_1': 0.019750799961473695, 'deflection_gain': 0.8025074530770181}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:29,939] Trial 19 finished with value: 153.8151714639216 and parameters: {'we_1': 0.00786430565293372, 'deflection_gain': 0.848054760153163}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:34,615] Trial 20 finished with value: 161.24165246487115 and parameters: {'we_1': 0.03917202461139395, 'deflection_gain': 0.5318243666436484}. Best is trial 1 with value: 151.5032527786563.\n", + "[I 2025-09-15 09:41:39,251] Trial 21 finished with value: 151.279283550613 and parameters: {'we_1': 0.017864758210372374, 'deflection_gain': 1.4710677157799985}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:41:43,889] Trial 22 finished with value: 152.336327738641 and parameters: {'we_1': 0.022742976218488958, 'deflection_gain': 1.2472519696882478}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:41:48,509] Trial 23 finished with value: 151.45501675680183 and parameters: {'we_1': 0.017230183155291366, 'deflection_gain': 2.2054675990504315}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:41:53,163] Trial 24 finished with value: 151.45435253429963 and parameters: {'we_1': 0.015978808454565008, 'deflection_gain': 2.2603309289290507}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:41:57,788] Trial 25 finished with value: 151.66860198513217 and parameters: {'we_1': 0.014451103236987515, 'deflection_gain': 2.427214868797242}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:02,397] Trial 26 finished with value: 152.02203911844327 and parameters: {'we_1': 0.011366772219036952, 'deflection_gain': 2.021269467180727}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:07,056] Trial 27 finished with value: 154.2241385893037 and parameters: {'we_1': 0.027488827620034254, 'deflection_gain': 1.5622625156532308}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:11,685] Trial 28 finished with value: 169.31252702937374 and parameters: {'we_1': 0.0498102831997255, 'deflection_gain': 2.7154877272125324}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:16,319] Trial 29 finished with value: 151.41581933507175 and parameters: {'we_1': 0.016151204132002883, 'deflection_gain': 2.20538819180779}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:20,955] Trial 30 finished with value: 154.0144777288761 and parameters: {'we_1': 0.008022836666069446, 'deflection_gain': 2.7985355576477806}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:25,865] Trial 31 finished with value: 151.63176564929964 and parameters: {'we_1': 0.015204569140331758, 'deflection_gain': 2.453014092033874}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:30,514] Trial 32 finished with value: 151.39659686711326 and parameters: {'we_1': 0.01683003668787947, 'deflection_gain': 2.149592516887154}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:35,208] Trial 33 finished with value: 152.456798206529 and parameters: {'we_1': 0.02314520232858679, 'deflection_gain': 1.4984243122323875}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:40,055] Trial 34 finished with value: 152.25685562573167 and parameters: {'we_1': 0.010471436136081903, 'deflection_gain': 1.5045799547054743}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:44,805] Trial 35 finished with value: 154.52171605783022 and parameters: {'we_1': 0.025797888693148797, 'deflection_gain': 2.98870039273858}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:49,616] Trial 36 finished with value: 155.81397806967664 and parameters: {'we_1': 0.030283883030159983, 'deflection_gain': 1.94364771506151}. Best is trial 21 with value: 151.279283550613.\n", + "[I 2025-09-15 09:42:54,272] Trial 37 finished with value: 151.23345963332213 and parameters: {'we_1': 0.01658283023716416, 'deflection_gain': 1.2810213851330092}. Best is trial 37 with value: 151.23345963332213.\n", + "[I 2025-09-15 09:42:58,894] Trial 38 finished with value: 152.11733036234213 and parameters: {'we_1': 0.02201987153148998, 'deflection_gain': 1.2725198679679988}. Best is trial 37 with value: 151.23345963332213.\n", + "[I 2025-09-15 09:43:03,534] Trial 39 finished with value: 151.22868497491865 and parameters: {'we_1': 0.017136896080162556, 'deflection_gain': 1.6331838901203573}. Best is trial 39 with value: 151.22868497491865.\n" + ] + } + ], + "source": [ + "mf_simultaneous = ModelFit(\n", + " df_scada,\n", + " fm,\n", + " t004_cost_function,\n", + " parameter_list=parameter_list,\n", + " parameter_name_list=parameter_name_list,\n", + " parameter_range_list=parameter_range_list,\n", + " parameter_index_list=parameter_index_list,\n", + " yaw_angles=yaw_angles_all,\n", + ")\n", + "\n", + "# Double the number of trials since tuning 2 parameters\n", + "opt_result_simultaneous = opt_optuna(mf_simultaneous, timeout=None, n_trials=n_trials * 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/zl/d69s6z796rs4fw08fnxrl0qxydms74/T/ipykernel_80827/1680281793.py:2: ExperimentalWarning: plot_contour is experimental (supported from v2.2.0). The interface can change in the future.\n", + " plot_contour(opt_result_simultaneous[\"optuna_study\"])\n", + "[W 2025-09-15 09:43:03,539] Output figures of this Matplotlib-based `plot_contour` function would be different from those of the Plotly-based `plot_contour`.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the contour plot using the study object\n", + "plot_contour(opt_result_simultaneous[\"optuna_study\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential tuning results:\n", + "Sequential tuning: 0.017, 1.738\n", + "Simultaneous tuning: 0.017, 1.633\n" + ] + } + ], + "source": [ + "# Get the best parameters\n", + "we_1_simultaneous = opt_result_simultaneous[\"optimized_parameter_values\"][0]\n", + "def_gain_simultaneous = opt_result_simultaneous[\"optimized_parameter_values\"][1]\n", + "\n", + "\n", + "# Compare to the values computed sequentially\n", + "print(\"Sequential tuning results:\")\n", + "print(f\"Sequential tuning: {we_1_baseline:.3f}, {def_gain:.3f}\")\n", + "print(f\"Simultaneous tuning: {we_1_simultaneous:.3f}, {def_gain_simultaneous:.3f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples_smarteole/floris_input_smarteole/emgauss.yaml b/examples_smarteole/floris_input_smarteole/emgauss.yaml index d7c8f9a0..c12d5dd7 100644 --- a/examples_smarteole/floris_input_smarteole/emgauss.yaml +++ b/examples_smarteole/floris_input_smarteole/emgauss.yaml @@ -98,7 +98,7 @@ wake: - 10 sigma_0_D: 0.28 smoothing_length_D: 2.0 - mixing_gain_velocity: 2.0 + mixing_gain_velocity: 0.0 # Note difference from defaults in FLORIS v4.5 wake_turbulence_parameters: crespo_hernandez: initial: 0.1 diff --git a/flasc/data_processing/dataframe_manipulations.py b/flasc/data_processing/dataframe_manipulations.py index df9a1584..2c121451 100644 --- a/flasc/data_processing/dataframe_manipulations.py +++ b/flasc/data_processing/dataframe_manipulations.py @@ -138,7 +138,8 @@ def get_column_mean( return mean_out -def _set_col_by_turbines(col_out, col_prefix, df, turbine_numbers, circular_mean): +def set_col_by_turbines(col_out, col_prefix, df, turbine_numbers, circular_mean): + """Average over specified turbines and add as a new column.""" if isinstance(turbine_numbers, str): if turbine_numbers.lower() == "all": turbine_numbers = range(get_num_turbines(df=df)) @@ -246,7 +247,7 @@ def _set_col_by_radius_from_turbine( logger.warn("No turbines within proximity. Try to increase radius.") return None - return _set_col_by_turbines( + return set_col_by_turbines( col_out=col_out, col_prefix=col_prefix, df=df, @@ -346,7 +347,7 @@ def set_wd_by_turbines( df (pd.DataFrame | FlascDataFrame): Dataframe which equals the inserted dataframe plus the additional column called 'wd'. """ - return _set_col_by_turbines("wd", "wd", df, turbine_numbers, True) + return set_col_by_turbines("wd", "wd", df, turbine_numbers, True) def set_wd_by_all_turbines( @@ -367,7 +368,7 @@ def set_wd_by_all_turbines( pd.Dataframe | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'wd'. """ - return _set_col_by_turbines("wd", "wd", df, "all", True) + return set_col_by_turbines("wd", "wd", df, "all", True) def set_wd_by_upstream_turbines( @@ -552,7 +553,7 @@ def set_ws_by_turbines( pd.DataFrame | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'ws'. """ - return _set_col_by_turbines("ws", "ws", df, turbine_numbers, False) + return set_col_by_turbines("ws", "ws", df, turbine_numbers, False) def set_ws_by_all_turbines( @@ -575,7 +576,7 @@ def set_ws_by_all_turbines( pd.Dataframe | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'ws'. """ - return _set_col_by_turbines("ws", "ws", df, "all", False) + return set_col_by_turbines("ws", "ws", df, "all", False) def set_ws_by_upstream_turbines( @@ -740,7 +741,7 @@ def set_ti_by_turbines( pd.Dataframe | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'ti'. """ - return _set_col_by_turbines("ti", "ti", df, turbine_numbers, False) + return set_col_by_turbines("ti", "ti", df, turbine_numbers, False) def set_ti_by_all_turbines( @@ -763,7 +764,7 @@ def set_ti_by_all_turbines( pd.Dataframe | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'ti'. """ - return _set_col_by_turbines("ti", "ti", df, "all", False) + return set_col_by_turbines("ti", "ti", df, "all", False) def set_ti_by_upstream_turbines( @@ -880,7 +881,7 @@ def set_pow_ref_by_turbines( pd.Dataframe | FlascDataFrame: Dataframe which equals the inserted dataframe plus the additional column called 'ti'. """ - return _set_col_by_turbines("pow_ref", "pow", df, turbine_numbers, False) + return set_col_by_turbines("pow_ref", "pow", df, turbine_numbers, False) def set_pow_ref_by_upstream_turbines( diff --git a/flasc/flasc_dataframe.py b/flasc/flasc_dataframe.py index c0502eeb..0333f5e1 100644 --- a/flasc/flasc_dataframe.py +++ b/flasc/flasc_dataframe.py @@ -67,14 +67,15 @@ def __init__(self, *args, channel_name_map=None, long_data_columns=None, **kwarg raise ValueError("long_data_columns must be a dictionary") if not all(col in long_data_columns for col in ["variable_column", "value_column"]): raise ValueError( - "long_data_columns must contain keys 'variable_column', " "and 'value_column'" + "long_data_columns must contain keys 'variable_column', and 'value_column'" ) self._long_data_columns = long_data_columns @property def in_flasc_format(self): """Return True if the data is in FLASC format, False otherwise.""" - if ("time" in self.columns) and ("pow_000" in self.columns): + pow_cols = [c for c in self.columns if c[:4] == "pow_" and c[4:].isdigit()] + if "time" in self.columns and len(pow_cols) > 0: return True else: return False diff --git a/flasc/model_fitting/__init__.py b/flasc/model_fitting/__init__.py index d613fd12..819b5663 100644 --- a/flasc/model_fitting/__init__.py +++ b/flasc/model_fitting/__init__.py @@ -1,11 +1,8 @@ # -*- coding: utf-8 -*- -"""Model estimation module for FLORIS SCADA Analysis repository.""" +"""Modular model fitting for FLASC.""" -__author__ = """Bart Doekemeijer, Paul Fleming""" +__author__ = """Paul Fleming, Michael Sinner, Bart Doekemeijer""" __email__ = "paul.fleming@nrel.gov, michael.sinner@nrel.gov" from pathlib import Path - -# from . import floris_sensitivity_analysis, turbulence_estimator -# from . import floris_tuning, tuning_utils, yaw_pow_fitting diff --git a/flasc/model_fitting/cost_library.py b/flasc/model_fitting/cost_library.py new file mode 100644 index 00000000..f2817390 --- /dev/null +++ b/flasc/model_fitting/cost_library.py @@ -0,0 +1,308 @@ +"""Library of cost functions for the model fitting optimization.""" + +from __future__ import annotations + +from abc import ABCMeta, abstractmethod +from typing import List + +import pandas as pd + +from flasc.data_processing.dataframe_manipulations import ( + set_col_by_turbines, + set_pow_ref_by_turbines, +) +from flasc.flasc_dataframe import FlascDataFrame + + +class CostFunctionBase(metaclass=ABCMeta): + """Base class for cost functions.""" + + def __init__(self, df_scada: pd.DataFrame | FlascDataFrame | None = None): + """Initialize the cost function class. + + Args: + df_scada (dataframe): The SCADA data to use in the cost function. + """ + self.assign_df_scada(df_scada) + self._is_initialized_for_evaluation = False + + @property + def df_scada(self) -> pd.DataFrame | FlascDataFrame | None: + """Get the SCADA dataframe.""" + if self._df_scada is None: + raise AttributeError("SCADA dataframe has not been assigned to cost object.") + return self._df_scada + + def assign_df_scada(self, df_scada: pd.DataFrame | FlascDataFrame | None): + """Assign the SCADA dataframe.""" + if ( + hasattr(self, "_df_scada") + and self._df_scada is not None + and not self._df_scada.equals(df_scada) + ): + print("Cost object already has df_scada assigned. Overwriting.") + if df_scada is not None: + self._df_scada = FlascDataFrame(df_scada).convert_to_flasc_format() + else: + self._df_scada = None + + @property + def is_initialized_for_evaluation(self) -> bool: + """Check if the cost function is ready for evaluation.""" + return self._is_initialized_for_evaluation + + @is_initialized_for_evaluation.setter + def is_initialized_for_evaluation(self, value: bool): + self._is_initialized_for_evaluation = value + + def initialize_for_evaluation(self): + """Initialize the cost function for evaluation. Called before the first evaluation. + + This method will be called before evaluating the cost function for the first time, and + should set the `initialized_for_evaluation` property to True. + + Subclasses may override this method to perform additional setup before evaluation. + """ + self.is_initialized_for_evaluation = True + + def prepare_df_floris_for_evaluation(self, df_floris: pd.DataFrame | FlascDataFrame): + """Prepare the cost function for evaluation. Called each time before evaluation.""" + return df_floris + + def __call__(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Call the instantiated object to evaluate the cost function. + + Abstract method to be implemented by subclasses. + """ + if not self.is_initialized_for_evaluation: + self.initialize_for_evaluation() + df_floris = self.prepare_df_floris_for_evaluation(df_floris) + + return self.cost(df_floris) + + @abstractmethod + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate the cost function. + + All subclasses must implement this method. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The cost value. + """ + raise NotImplementedError( + "Subclasses of CostFunctionBase must implement a cost() method. " + "This method should take a dataframe (df_floris) as an input and return a float." + ) + + +class TurbinePowerErrorBase(CostFunctionBase): + """Base class for cost functions based on the error between SCADA and FLORIS turbine powers.""" + + def __init__( + self, + df_scada: pd.DataFrame | FlascDataFrame | None = None, + turbine_power_subset: list | None = None, + ): + """Initialize the cost function class. + + Args: + df_scada (dataframe): The SCADA data to use in the cost function. + turbine_power_subset (list | None): List of turbine indices to use in the cost function. + If None, all turbines will be used. + """ + super().__init__(df_scada) + + # Save other parameters for now. These will be processed in the prepare method. + self._turbine_power_subset = turbine_power_subset + + def initialize_for_evaluation(self): + """Prepare the cost function for evaluation.""" + self._turbine_power_subset = self.process_turbine_powers_subset( + self.df_scada, self._turbine_power_subset + ) + + def compute_errors(self, df_floris: pd.DataFrame | FlascDataFrame) -> pd.DataFrame: + """Compute the errors between the SCADA and FLORIS turbine powers. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + pd.DataFrame: DataFrame of errors between SCADA and FLORIS turbine powers. + """ + return self.df_scada[self._turbine_power_subset] - df_floris[self._turbine_power_subset] + + @staticmethod + def process_turbine_powers_subset(df_scada, turbine_power_subset): + """Process the turbine_power_subset parameter.""" + if not isinstance(turbine_power_subset, list) and turbine_power_subset is not None: + raise TypeError("turbine_power_subset must be a list or None.") + + if turbine_power_subset is None: + turbine_power_subset = ["pow_{0:03d}".format(t) for t in range(df_scada.n_turbines)] + elif isinstance(turbine_power_subset[0], str): + if not all([c[:4] == "pow_" and c[4:].isdigit() for c in turbine_power_subset]): + turbine_power_subset = [df_scada.channel_name_map[c] for c in turbine_power_subset] + elif isinstance(turbine_power_subset[0], int): + turbine_power_subset = ["pow_{0:03d}".format(t) for t in turbine_power_subset] + else: + raise TypeError( + "turbine_power_subset must be a list of strings or integers and must", + " match the turbine names in df_scada.", + ) + + return turbine_power_subset + + +class TurbinePowerMeanAbsoluteError(TurbinePowerErrorBase): + """Cost function for mean absolute error over all turbines and all times.""" + + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate the mean absolute error of the turbine powers over all turbines and times. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The cost value. + """ + df_error = self.compute_errors(df_floris) + + return df_error.abs().mean().mean() + + +class TurbinePowerRootMeanSquaredError(TurbinePowerErrorBase): + """Cost function for root mean squared error over all turbines and all times.""" + + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate the mean squared error of the turbine powers over all turbines and times. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The cost value. + """ + df_error = self.compute_errors(df_floris) + + return (df_error**2).mean().mean() ** 0.5 + + +class FarmPowerErrorBase(CostFunctionBase): + """Base class for cost functions based on the error between SCADA and FLORIS farm powers.""" + + def __init__(self, df_scada: pd.DataFrame | FlascDataFrame | None = None): + """Initialize the cost function class. + + Args: + df_scada (dataframe): The SCADA data to use in the cost function. + """ + super().__init__(df_scada) + + def compute_errors(self, df_floris: pd.DataFrame | FlascDataFrame) -> pd.Series: + """Compute the errors between the SCADA and FLORIS farm powers. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + pd.DataFrame: DataFrame of errors between SCADA and FLORIS farm powers. + """ + pow_columns = ["pow_{0:03d}".format(t) for t in range(self.df_scada.n_turbines)] + pow_farm_scada = self.df_scada[pow_columns].sum(axis=1) + pow_farm_floris = df_floris[pow_columns].sum(axis=1) + + return pow_farm_scada - pow_farm_floris + + +class FarmPowerMeanAbsoluteError(FarmPowerErrorBase): + """Cost function for mean absolute error of farm power over all times.""" + + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate cost function. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The cost value. + """ + return self.compute_errors(df_floris).abs().mean() + + +class FarmPowerRootMeanSquaredError(FarmPowerErrorBase): + """Cost function for root mean squared error of farm power over all times.""" + + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate cost function. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The cost value. + """ + return (self.compute_errors(df_floris) ** 2).mean() ** 0.5 + + +class WakeLossRootMeanSquaredError(CostFunctionBase): + """Cost function for the overall wake loss RMSE between SCADA and FLORIS data.""" + + def __init__( + self, + df_scada: pd.DataFrame | FlascDataFrame | None = None, + reference_turbines: List[List[int]] | None = None, + test_turbines: List[List[int]] | None = None, + ): + """Initialize the cost function class. + + Args: + df_scada (dataframe): The SCADA data to use in the cost function. + reference_turbines (List[List[int]] | None): List of lists of turbine indices to use as + reference (free stream) turbines for wake loss calculations + test_turbines (List[List[int]] | None): List of lists of turbine indices to use as test + (waked) turbines for wake loss calculations. + """ + super().__init__(df_scada) + + if reference_turbines is None or test_turbines is None: + raise ValueError( + "Both reference_turbines and test_turbines must be provided as lists of lists." + ) + self.reference_turbines = reference_turbines + self.test_turbines = test_turbines + + def initialize_for_evaluation(self): + """Apply the reference and test turbines to the SCADA dataframe.""" + self.assign_df_scada(set_pow_ref_by_turbines(self.df_scada, self.reference_turbines)) + self.assign_df_scada( + set_col_by_turbines("pow_test", "pow", self.df_scada, self.test_turbines, False) + ) + + self.is_initialized_for_evaluation = True + + def prepare_df_floris_evaluation(self, df_floris: pd.DataFrame | FlascDataFrame): + """Apply the reference and test turbines to the FLORIS dataframe.""" + df_floris = set_pow_ref_by_turbines(df_floris, self.reference_turbines) + df_floris = set_col_by_turbines("pow_test", "pow", df_floris, self.test_turbines, False) + + return df_floris + + def cost(self, df_floris: pd.DataFrame | FlascDataFrame) -> float: + """Evaluate the overall wake loss error. + + Args: + df_floris (pd.DataFrame | FlascDataFrame): The FLORIS data to use in the cost function. + + Returns: + float: The overall wake loss error. + """ + df_floris = self.prepare_df_floris_evaluation(df_floris) + + scada_wake_loss = self.df_scada["pow_ref"].values - self.df_scada["pow_test"].values + floris_wake_loss = df_floris["pow_ref"].values - df_floris["pow_test"].values + + return ((scada_wake_loss - floris_wake_loss) ** 2).sum() diff --git a/flasc/model_fitting/floris_tuning.py b/flasc/model_fitting/floris_tuning.py index 6596b674..2010d328 100644 --- a/flasc/model_fitting/floris_tuning.py +++ b/flasc/model_fitting/floris_tuning.py @@ -7,6 +7,8 @@ # Doekemeijer at Shell, as well as discussions with Diederik van Binsbergen at # NTNU. Please see readme.txt for more information. +import warnings + import numpy as np import polars as pl @@ -16,6 +18,11 @@ from flasc.utilities.energy_ratio_utilities import add_power_ref, add_power_test from flasc.utilities.tuner_utilities import replicate_nan_values, resim_floris +floris_tuning_deprecation_message = ( + "The floris_tuning package is deprecated as of FLASC v2.4. " + "Please see the ModelFit package for tuning FLORIS models to SCADA data. " +) + def evaluate_overall_wake_loss(df_, df_freq=None): """Evaluate the overall wake loss from pow_ref to pow_test as percent reductions. @@ -28,6 +35,7 @@ def evaluate_overall_wake_loss(df_, df_freq=None): float: Overall wake losses """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) # Not sure yet if we want to figure out how to use df_freq here return 100 * (df_["pow_ref"].sum() - df_["pow_test"].sum()) / df_["pow_ref"].sum() @@ -71,6 +79,7 @@ def sweep_velocity_model_parameter_for_overall_wake_losses( A tuple (np.ndarray, np.ndarray) where the first element is the FLORIS wake losses and the second element is the SCADA wake losses """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) # Currently assuming pow_ref and pow_test already assigned # Also assuming limit to ws/wd range accomplished but could revisit? @@ -137,6 +146,7 @@ def select_best_wake_model_parameter(floris_results, scada_results, value_candid Returns: float: best fit parameter value """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) error_values = (floris_results - scada_results) ** 2 best_param = value_candidates[np.argmin(error_values)] @@ -198,6 +208,7 @@ def sweep_wd_std_for_er( and the second element is the dataframes. """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) # Currently assuming pow_ref and pow_test already assigned # Also assuming limit to ws/wd range accomplished but could revisit? @@ -289,6 +300,7 @@ def select_best_wd_std(er_results, value_candidates, ax=None): Returns: float: The best parameter value """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) error_sq = er_results**2 best_param = value_candidates[np.argmin(error_sq)] @@ -355,6 +367,7 @@ def sweep_deflection_parameter_for_total_uplift( A typle (np.ndarray, np.ndarray) where the first element is the FLORIS total uplifts and the second element is the SCADA total uplifts """ + warnings.warn(floris_tuning_deprecation_message, DeprecationWarning) # Currently assuming pow_ref and pow_test already assigned # Also assuming limit to ws/wd range accomplished but could revisit? diff --git a/flasc/model_fitting/model_fit.py b/flasc/model_fitting/model_fit.py new file mode 100644 index 00000000..93447a2f --- /dev/null +++ b/flasc/model_fitting/model_fit.py @@ -0,0 +1,407 @@ +"""Modular Class for computing a fitness evaluation of a FLORIS model to SCADA data.""" + +from __future__ import annotations + +from typing import List, Tuple + +import numpy as np +import pandas as pd +from floris import FlorisModel, ParFlorisModel, UncertainFlorisModel + +from flasc.data_processing import dataframe_manipulations as dfm +from flasc.flasc_dataframe import FlascDataFrame +from flasc.model_fitting.cost_library import CostFunctionBase +from flasc.utilities.tuner_utilities import replicate_nan_values + + +class ModelFit: + """Fit a FlorisModel to SCADA data. + + A modular class for computing fitness evaluation of a FLORIS model to SCADA data. + Provides methods to run FLORIS simulations, evaluate cost functions, and manage + parameter optimization for model calibration. + """ + + def __init__( + self, + df: pd.DataFrame | FlascDataFrame, + fmodel: FlorisModel | ParFlorisModel | UncertainFlorisModel, + cost_function: CostFunctionBase, + parameter_list: List[List] | List[Tuple] = [], + parameter_name_list: List[str] = [], + parameter_range_list: List[List] | List[Tuple] = [], + parameter_index_list: List[int] = [], + yaw_angles: np.ndarray | None = None, + use_non_default_wd_sample_points: bool = False, + ): + """Initialize the ModelFit class. + + Args: + df (pd.DataFrame | FlascDataFrame): DataFrame containing SCADA data. + fmodel (FlorisModel | ParFlorisModel | UncertainFlorisModel): + FLORIS model to calibrate. + cost_function (CostFunctionBase): Instance of a cost function class that inherits from + CostFunctionBase. + parameter_list (List[List] | List[Tuple], optional): List of FLORIS parameters to + calibrate. If empty, no parameters are calibrated. Defaults to []. + parameter_name_list (List[str], optional): List of names for the parameters. + If empty, no names are provided. Defaults to []. + parameter_range_list (List[List] | List[Tuple], optional): List of parameter ranges. + If empty, no ranges are provided. Defaults to []. + parameter_index_list (List[int], optional): List of parameter indices. Defaults to []. + yaw_angles (np.ndarray | None, optional): Array of yaw angles. Defaults to None. + use_non_default_wd_sample_points (bool, optional): Whether to use non-default wind + direction sample points. If True, the wind direction sample points will be set to + the sample points in the FLORIS model will be recorded in units of wd_std and + rescaled with the new wd_std. If False, the default wind direction sample points + will be used ([-2 * wd_std, -1 * wd_std, 0, wd_std, 2 * wd_std]). + Defaults to False. + """ + # Save the dataframe as a FlascDataFrame + self.df = FlascDataFrame(df) + + # Make sure the dataframe index is simple + self.df = self.df.reset_index(drop=True) + + # Check the dataframe + self._check_flasc_dataframe(self.df) + + # Check if fmodel if FlorisModel or ParallelFlorisModel + if not isinstance(fmodel, (FlorisModel, ParFlorisModel, UncertainFlorisModel)): + raise ValueError( + "fmodel must be a FlorisModel, ParallelFlorisModel or UncertainFlorisModel." + ) + + # Check that cost_function is an instance of CostFunctionBase + if not isinstance(cost_function, CostFunctionBase): + raise TypeError("cost_function must be an instantiated subclass of CostFunctionBase.") + if not hasattr(cost_function, "cost"): + raise NotImplementedError( + "The cost_function must have a cost() method implemented that takes a dataframe " + "(df_floris) as an input and returns a float." + ) + + # Save the fmodel + self.fmodel = fmodel + + # Get the number of turbines and confirm that + # the dataframe and floris model have the same number of turbines + n_turbines_data = dfm.get_num_turbines(self.df) + + if n_turbines_data != self.fmodel.n_turbines: + print( + "WARNING: The number of turbines in the dataframe and the " + "Floris model do not match." + ) + + # Store the use_non_default_wd_sample_points flag + self.use_non_default_wd_sample_points = use_non_default_wd_sample_points + + # If the user requests to use non-default wind direction sample points, + # learn what these should be from the floris model in units of wd_std + if use_non_default_wd_sample_points: + self.wd_sample_points_in_wd_std = [ + wdsp / fmodel.wd_std for wdsp in fmodel.wd_sample_points + ] + + # If yaw angles are provided, check that they are the same length as the number of turbines + if yaw_angles is not None: + if yaw_angles.ndim != 2: + raise ValueError("yaw_angles must be a 2D array.") + if yaw_angles.shape[0] != len(self.df): + raise ValueError( + "yaw_angles must have the same length as the number of rows in df." + ) + if yaw_angles.shape[1] != self.df.n_turbines: + raise ValueError( + "yaw_angles must have the same num cols as the number of turbines." + ) + self.yaw_angles = yaw_angles + else: + self.yaw_angles = None + + # Assign the dataframe to the cost object + cost_function.assign_df_scada(self.df) + + # Save the cost function handle + self.cost_function = cost_function + + # Confirm that parameter_list, parameter_name_list, and parameter_range_list and + # parameter_index_list are lists + if not isinstance(parameter_list, list): + raise ValueError("parameter_list must be a list.") + if not isinstance(parameter_name_list, list): + raise ValueError("parameter_name_list must be a list.") + if not isinstance(parameter_range_list, list): + raise ValueError("parameter_range_list must be a list.") + if not isinstance(parameter_index_list, list): + raise ValueError("parameter_index_list must be a list.") + + # Confirm that parameter_list, parameter_name_list, + # and parameter_range_list are the same length + if len(parameter_list) != len(parameter_name_list) or len(parameter_list) != len( + parameter_range_list + ): + raise ValueError( + "parameter_list, parameter_name_list, and parameter_range_list" + " must be the same length." + ) + + # If any of parameter_list, parameter_name_list, or parameter_range_list are provided, + # (in that they have lengths greater than 0) then all must be provided + if len(parameter_list) > 0 or len(parameter_name_list) > 0 or len(parameter_range_list) > 0: + if ( + len(parameter_list) == 0 + or len(parameter_name_list) == 0 + or len(parameter_range_list) == 0 + ): + raise ValueError( + "If any of parameter_list, parameter_name_list, or parameter_range_list" + " are provided, all must be provided." + ) + + # Save the parameter list, name list, and range list + self.parameter_list = parameter_list + self.parameter_name_list = parameter_name_list + self.parameter_range_list = parameter_range_list + + # Save the number of parameters + self.n_parameters = len(parameter_list) + + # If parameter_index_list is empty, set as a list of None equal to the number of parameters + if len(parameter_index_list) == 0: + self.parameter_index_list = [None] * self.n_parameters + + # Else ensure it is the same length as parameter_list + else: + if len(parameter_index_list) != self.n_parameters: + raise ValueError("parameter_index_list must be the same length as parameter_list.") + self.parameter_index_list = parameter_index_list + + # Initialize the initial parameter values + self.initial_parameter_values = self.get_parameter_values() + + def _check_flasc_dataframe(self, df: FlascDataFrame) -> None: + """Check that the provided FlascDataFrame is valid. + + Args: + df (FlascDataFrame): DataFrame to check. + """ + # Data frame must contain a 'ws' and 'wd' column + if "ws" not in df.columns or "wd" not in df.columns: + raise ValueError("DataFrame must contain 'ws' and 'wd' columns.") + + def _form_flasc_dataframe( + self, + time: np.ndarray, + wind_directions: np.ndarray, + wind_speeds: np.ndarray, + powers: np.ndarray, + ) -> FlascDataFrame: + """Form a FlascDataFrame from wind directions, wind speeds, and powers. + + Args: + time (np.ndarray): Array of time values. + wind_directions (np.ndarray): Array of wind directions. + wind_speeds (np.ndarray): Array of wind speeds. + powers (np.ndarray): Array of powers. Must be (n_findex, n_turbines). + + Returns: + FlascDataFrame: FlascDataFrame containing the wind directions, wind speeds, and powers. + """ + # Check that lengths of time, wind directions + if time.shape[0] != wind_directions.shape[0]: + raise ValueError("time and wind_directions must have the same length.") + + # Check that the shapes of the arrays are correct + if wind_directions.shape[0] != wind_speeds.shape[0]: + raise ValueError("wind_directions and wind_speeds must have the same length.") + + if wind_directions.shape[0] != powers.shape[0]: + raise ValueError("wind_directions and powers (0th axis) must have the same length.") + + if powers.ndim != 2: + raise ValueError("powers must be a 2D array.") + + # Name the power columns + pow_cols = [f"pow_{i:>03}" for i in range(powers.shape[1])] + + # Assign the powers + _df = pd.DataFrame(data=powers, columns=pow_cols) + # Assign the wind directions and wind speeds + _df = _df.assign(time=time, wd=wind_directions, ws=wind_speeds) + + # Re-order the columns + _df = _df[["time", "wd", "ws"] + pow_cols] + + return FlascDataFrame(_df) + + def run_floris_model(self, **kwargs) -> FlascDataFrame: + """Run the FLORIS model with the current parameter values. + + Given the provided FLORIS model and SCADA data, run the FLORIS model + and generate a FlascDataFrame of FLORIS values. Note **kwargs are + provided to allow additional settings to be passed to the + set method. + + Args: + **kwargs: Additional keyword arguments to pass to the + set method. + + Returns: + FlascDataFrame: FlascDataFrame containing FLORIS simulation results with wind + directions, wind speeds, and turbine powers. + """ + # Get the wind speeds, wind directions and turbulence intensities + time = self.df["time"].values + wind_speeds = self.df["ws"].values + wind_directions = self.df["wd"].values + + # TI is used direcly if included in the dataframe + # Else the first value of the current model is used + if "ti" in self.df.columns: + turbulence_intensities = self.df["ti"].values + else: + turbulence_intensities = ( + np.ones_like(wind_speeds) * self.fmodel.turbulence_intensities[0] + ) + + # Set the FlorisModel + self.fmodel.set( + wind_speeds=wind_speeds, + wind_directions=wind_directions, + turbulence_intensities=turbulence_intensities, + **kwargs, + ) + + # if yaw angles are not None, set the yaw angles + if self.yaw_angles is not None: + self.fmodel.set(yaw_angles=self.yaw_angles) + + # Run the model + self.fmodel.run() + + # Get the turbines in kW + turbine_powers = self.fmodel.get_turbine_powers() / 1000 + + # Generate FLORIS dataframe + df_floris = self._form_flasc_dataframe(time, wind_directions, wind_speeds, turbine_powers) + + # Make sure the NaN values in the SCADA data appear in the same locations in the + # FLORIS data + df_floris = replicate_nan_values(self.df, df_floris) + + # Make sure floris dataframe has an index identical to the SCADA dataframe + df_floris.index = self.df.index + + # Save the floris result frame for debugging + self._df_floris = df_floris + + # Return df_floris + return df_floris + + def set_wd_std(self, wd_std: float) -> None: + """Set the standard deviation of the wind direction within the UncertainFlorisModel. + + Args: + wd_std (float): Standard deviation of the wind direction. + """ + # Check if the model is an UncertainFlorisModel + if not isinstance(self.fmodel, UncertainFlorisModel): + raise ValueError("The floris model must be an UncertainFlorisModel.") + + # If the user requests to use non-default wind direction sample points, + # learn what these should be from the floris model in units of wd_std + if self.use_non_default_wd_sample_points: + wd_sample_points = np.array(self.wd_sample_points_in_wd_std) * wd_std + else: + # Default to None which will use the default wd_sample_points + # [-2 * wd_std, -1 * wd_std, 0, wd_std, 2 * wd_std] + wd_sample_points = None + + # Update the UncertainFlorisModel + self.fmodel = UncertainFlorisModel( + self.fmodel.fmodel_unexpanded, + wd_resolution=self.fmodel.wd_resolution, + ws_resolution=self.fmodel.ws_resolution, + ti_resolution=self.fmodel.ti_resolution, + yaw_resolution=self.fmodel.yaw_resolution, + power_setpoint_resolution=self.fmodel.power_setpoint_resolution, + awc_amplitude_resolution=self.fmodel.awc_amplitude_resolution, + wd_std=wd_std, + wd_sample_points=wd_sample_points, + fix_yaw_to_nominal_direction=self.fmodel.fix_yaw_to_nominal_direction, + verbose=self.fmodel.verbose, + ) + + def evaluate_floris(self, **kwargs) -> float: + """Evaluate the FLORIS model. + + Given the current parameter values, run the FLORIS model and evaluate the cost function. + + Args: + **kwargs: Additional keyword arguments to pass to the run_floris_model method. + + Returns: + float: Cost value. + """ + # Run the FLORIS model + df_floris = self.run_floris_model(**kwargs) + + # Evaluate the cost function passing the FlorisModel + return self.cost_function(df_floris) + + def set_parameter_and_evaluate( + self, + parameter_values: np.ndarray, + **kwargs, + ) -> float: + """Internal function to evaluate the cost function with a given set of parameters. + + Args: + parameter_values (np.ndarray): Array of parameter values. + **kwargs: Additional keyword arguments to pass to the optimization algorithm. + + Returns: + float: Cost value. + """ + # Set the parameter values + self.set_parameter_values(parameter_values) + + # Evaluate the cost function + return self.evaluate_floris(**kwargs) + + def get_parameter_values( + self, + ) -> np.ndarray: + """Get the current parameter values from the FLORIS model. + + Returns: + np.ndarray: Array of parameter values. + """ + parameter_values = np.zeros(self.n_parameters) + + for i, (parameter, parameter_index) in enumerate( + zip(self.parameter_list, self.parameter_index_list) + ): + parameter_values[i] = self.fmodel.get_param(parameter, parameter_index) + + return parameter_values + + def set_parameter_values( + self, + parameter_values: np.ndarray, + ) -> None: + """Set the parameter values in the FLORIS model. + + Args: + parameter_values (np.ndarray): Array of parameter values. + """ + # Check that parameters values is len(parameter_list) long + if len(parameter_values) != self.n_parameters: + raise ValueError("parameter_values must have length equal to the number of parameters.") + + for i, (parameter, parameter_index) in enumerate( + zip(self.parameter_list, self.parameter_index_list) + ): + self.fmodel.set_param(parameter, parameter_values[i], parameter_index) diff --git a/flasc/model_fitting/opt_library.py b/flasc/model_fitting/opt_library.py new file mode 100644 index 00000000..91c6a5b7 --- /dev/null +++ b/flasc/model_fitting/opt_library.py @@ -0,0 +1,317 @@ +"""This module contains the optimization algorithms for the model fitting.""" + +from __future__ import annotations + +import itertools +from typing import Dict, Tuple + +import numpy as np +import optuna + +from flasc.model_fitting.model_fit import ModelFit + + +def opt_optuna( + mf: ModelFit, + n_trials: int = 100, + timeout: float | None = None, + seed: int | None = None, + verbose: bool = True, +) -> Dict: + """Optimize the model parameters using Optuna. + + Args: + mf (ModelFit): ModelFit object containing the model and parameters to optimize. + n_trials (int): Number of trials to run. Defaults to 100. + timeout (float | None): Timeout for the optimization in seconds. + Defaults to None. + seed (int | None): Seed for the random number generator. Defaults to None, + in which case a random seed will be used. + verbose (bool): Whether to print out the optimization process. Defaults to True which + gives optuna INFO logging. + + Returns: + Dict: Dictionary containing the optimal parameter values and + the Optuna study object. All optimizers must contain keys "optimized_parameter_values" + and "optimized_cost", and may optionally contain other optimizers-specific key-value + pairs. + """ + + # Set up the objective function for optuna + def objective(trial): + parameter_values = [] + for p_idx in range(mf.n_parameters): + parameter_name = mf.parameter_name_list[p_idx] + parameter_range = mf.parameter_range_list[p_idx] + parameter_values.append( + trial.suggest_float(parameter_name, parameter_range[0], parameter_range[1]) + ) + + return mf.set_parameter_and_evaluate(parameter_values) + + # Run the optimization + study = optuna.create_study( + sampler=optuna.samplers.TPESampler(seed=seed), study_name="ModelFit" + ) + + # If not verbose + if not verbose: + optuna.logging.set_verbosity(optuna.logging.WARNING) + + # Seed the initial value + init_dict = {} + for pname, pval in zip(mf.parameter_name_list, mf.get_parameter_values()): + init_dict[pname] = pval + study.enqueue_trial(init_dict) + study.optimize(objective, n_trials=n_trials, timeout=timeout) + + # Make a list of the best parameter values + best_params = [] + for parameter_name in mf.parameter_name_list: + best_params.append(study.best_params[parameter_name]) + + # Return results as dictionary + result_dict = { + "optimized_parameter_values": best_params, + "optimized_cost": study.best_value, + "optuna_study": study, + } + + # Returns results + return result_dict + + +def opt_optuna_with_wd_std( + mf: ModelFit, + n_trials: int = 100, + timeout: float | None = None, + verbose: bool = True, +) -> Tuple[Dict, optuna.Study]: + """Optimize the model parameters using Optuna including wd_std. + + This version includes the wind direction standard deviation of the UncertainFlorisModel + as a parameter to optimize. + + Args: + mf (ModelFit): ModelFit object containing the model and parameters to optimize. + n_trials (int): Number of trials to run. Defaults to 100. + timeout (float | None): Timeout for the optimization in seconds. + Defaults to None. + verbose (bool): Whether to print out the optimization process. Defaults to True which + gives optuna INFO logging. + + Returns: + Dict: Dictionary containing the optimal parameter values and + the Optuna study object. All optimizers must contain keys "optimized_parameter_values" + and "optimized_cost", and may optionally contain other optimizers-specific key-value + pairs. + """ + + # Set up the objective function for optuna + def objective(trial): + # Set wd_std + mf.set_wd_std(wd_std=trial.suggest_float("wd_std", 0.1, 6.0)) + + parameter_values = [] + for p_idx in range(mf.n_parameters): + parameter_name = mf.parameter_name_list[p_idx] + parameter_range = mf.parameter_range_list[p_idx] + parameter_values.append( + trial.suggest_float(parameter_name, parameter_range[0], parameter_range[1]) + ) + + return mf.set_parameter_and_evaluate(parameter_values) + + # Run the optimization + study = optuna.create_study() + + # If not verbose + if not verbose: + optuna.logging.set_verbosity(optuna.logging.WARNING) + + # Seed the initial value + init_dict = {"wd_std": 3.0} + for pname, pval in zip(mf.parameter_name_list, mf.get_parameter_values()): + init_dict[pname] = pval + study.enqueue_trial(init_dict) + study.optimize(objective, n_trials=n_trials, timeout=timeout) + + # Make a list of the best parameter values + best_params = [] + for parameter_name in mf.parameter_name_list + ["wd_std"]: + best_params.append(study.best_params[parameter_name]) + + # Return results as dictionary + result_dict = { + "optimized_parameter_values": best_params, + "optimized_cost": study.best_value, + "optuna_study": study, + } + + # Returns results + return result_dict + + +def extract_optuna_trial_data(study_obj, param_name): + """Extract parameter values and costs from study trials.""" + param_values = [trial.params[param_name] for trial in study_obj.trials] + cost_values = [trial.value for trial in study_obj.trials] + + # Sort both by parameter values + param_values, cost_values = zip(*sorted(zip(param_values, cost_values))) + + # Get the best parameter value and cost + best_param_value = study_obj.best_trial.params[param_name] + best_cost = study_obj.best_trial.value + + # Normalize the cost values + cost_values = np.array(cost_values) / np.min(cost_values) + + return param_values, cost_values, best_param_value, best_cost + + +def opt_sweep( + mf: ModelFit, + n_grid: int | list[int] = 10, + verbose: bool = False, +) -> Tuple[Dict, optuna.Study]: + """Optimize the model parameters using a grid sweep. + + Args: + mf (ModelFit): ModelFit object containing the model and parameters to optimize. + n_grid (int | list[int] | None): Number of grid points to use for each parameter. + If an integer is provided, the same number of grid points will be used for + each parameter. If a list is provided, it must have the same length as the + number of parameters. Defaults to None, in which case 10 grid points will + be used for each parameter. + verbose (bool): Whether to print out the optimization process. Defaults to False. + + Returns: + Dict: Dictionary containing the optimal parameter values. All optimizers must contain keys + "optimized_parameter_values" and "optimized_cost", and may optionally contain other + optimizers-specific key-value pairs. + """ + # Handle n_grid parameter + if isinstance(n_grid, int): + n_grid = [n_grid] * mf.n_parameters + elif len(n_grid) != mf.n_parameters: + raise ValueError( + f"Length of n_grid ({len(n_grid)}) must match number of parameters ({mf.n_parameters})" + ) + + # Create parameter arrays for each parameter + parameter_arrays = [] + for p_idx in range(mf.n_parameters): + param_array = np.linspace( + mf.parameter_range_list[p_idx][0], mf.parameter_range_list[p_idx][1], n_grid[p_idx] + ) + parameter_arrays.append(param_array) + + # Generate all combinations using itertools.product + all_combinations = np.array(list(itertools.product(*parameter_arrays))) + all_costs = np.zeros(all_combinations.shape[0]) + + # Initialize tracking variables + best_cost = float("inf") + best_params = None + + # Evaluate each combination + for i, param_combination in enumerate(all_combinations): + if verbose: + print(f"Evaluating combination {i + 1}/{len(all_combinations)}: {param_combination}") + + cost = mf.set_parameter_and_evaluate(param_combination) + all_costs[i] = cost + + if cost < best_cost: + best_cost = cost + best_params = list(param_combination) + + results_dict = { + "optimized_parameter_values": best_params, + "optimized_cost": best_cost, + "all_parameter_combinations": all_combinations, + "all_costs": all_costs, + } + + return results_dict + + +def opt_sweep_with_wd_std( + mf: ModelFit, + n_grid: int | list[int] = 10, + wd_std_range: Tuple[float, float] = (0.1, 6.0), + verbose: bool = False, +) -> Tuple[Dict, optuna.Study]: + """Optimize the model parameters using a grid sweep including wd_std. + + This version includes the wind direction standard deviation of the UncertainFlorisModel + as a parameter to optimize. + + Args: + mf (ModelFit): ModelFit object containing the model and parameters to optimize. + n_grid (int | list[int] | None): Number of grid points to use for each parameter. + If an integer is provided, the same number of grid points will be used for + each parameter. If a list is provided, it must have the same length as the + number of parameters. Defaults to 10 (used for each parameter) + wd_std_range (Tuple[float, float]): Range of wind direction standard deviation to sweep. + Defaults to (0.1, 6.0). + verbose (bool): Whether to print out the optimization process. Defaults to False. + + Returns: + Dict: Dictionary containing the optimal parameter values. All optimizers must contain keys + "optimized_parameter_values" and "optimized_cost", and may optionally contain other + optimizers-specific key-value pairs. + """ + # Handle n_grid parameter + if isinstance(n_grid, int): + n_grid = [n_grid] * (mf.n_parameters + 1) + elif len(n_grid) != mf.n_parameters + 1: + raise ValueError( + f"Length of n_grid ({len(n_grid)}) must match number of parameters " + f"({mf.n_parameters + 1})" + ) + + # Create parameter arrays for each parameter + parameter_arrays = [] + for p_idx in range(mf.n_parameters): + param_array = np.linspace( + mf.parameter_range_list[p_idx][0], mf.parameter_range_list[p_idx][1], n_grid[p_idx] + ) + parameter_arrays.append(param_array) + + # Add wd_std parameter array + wd_std_array = np.linspace(wd_std_range[0], wd_std_range[1], n_grid[-1]) + parameter_arrays.append(wd_std_array) + + # Generate all combinations using itertools.product + all_combinations = np.array(list(itertools.product(*parameter_arrays))) + all_costs = np.zeros(all_combinations.shape[0]) + + # Initialize tracking variables + best_cost = float("inf") + best_params = None + + # Evaluate each combination + for i, param_combination in enumerate(all_combinations): + if verbose: + print(f"Evaluating combination {i + 1}/{len(all_combinations)}: {param_combination}") + + # Set wd_std + mf.set_wd_std(wd_std=param_combination[-1]) + + cost = mf.set_parameter_and_evaluate(param_combination[:-1]) + all_costs[i] = cost + + if cost < best_cost: + best_cost = cost + best_params = list(param_combination) + + results_dict = { + "optimized_parameter_values": best_params, + "optimized_cost": best_cost, + "all_parameter_combinations": all_combinations, + "all_costs": all_costs, + } + + return results_dict diff --git a/flasc/model_fitting/readme.txt b/flasc/model_fitting/readme.txt index 3c0128e9..fdcecc19 100644 --- a/flasc/model_fitting/readme.txt +++ b/flasc/model_fitting/readme.txt @@ -2,21 +2,7 @@ See https://nrel.github.io/flasc/ for documentation ____ model_fitting ____ -This contains a preliminary implementation of tuning methods for FLORIS to SCADA (floris_tuning.py). -The code is focused on methods for the Empirical Guassian wake model and is -based on contributions from Elizabeth Eyeson, Paul Fleming (paul.fleming@nrel.gov) -Misha Sinner (michael.sinner@nrel.gov) and Eric Simley at NREL, and Bart -Doekemeijer at Shell, as well as discussions with Diederik van Binsbergen at -NTNU. - -Please treat this module as a beta implementation. - -We are planning to extend the capabilities of the model_tuning module in coming -version releases. If you are interested in contributing to this effort, please -reach out to Paul or Misha via email. Planned improvements include: -- Streamlining of processes and code -- Added flexibility for implementing other loss functions -- Consolidation and alignment with cosine power loss exponent fitting - (see estimate_cos_pp_fit method in turbine_analysis/yaw_pow_fitting.py) -- Possible accelerated model fitting by refinement of swept parameters -- Methods for fitting multiple parameters simultaneously +This contains methods for calibrating FLORIS models to SCADA (model_fit.py). The ModelFit approach +supersedes the previous beta version provided in floris_tuning.py. The `floris_tuning.py` module is +deprecated and will be removed in a future release. In accordance with semantic versioning, it will +remain available until the next major version bump; please migrate to `model_fit.py`. diff --git a/flasc/utilities/utilities_examples.py b/flasc/utilities/utilities_examples.py index f54540b5..ebb8bd1d 100644 --- a/flasc/utilities/utilities_examples.py +++ b/flasc/utilities/utilities_examples.py @@ -94,12 +94,7 @@ def load_floris_artificial(wake_model="gch", wd_std=0.0, cosine_exponent=None): # Add uncertainty if wd_std > 0.01: - unc_options = { - "std_wd": wd_std, # Standard deviation for inflow wind direction (deg) - "pmf_res": 1.0, # Resolution over which to calculate angles (deg) - "pdf_cutoff": 0.995, # Probability density function cut-off (-) - } - fm = UncertainFlorisModel(fm, unc_options=unc_options) + fm = UncertainFlorisModel(fm.core.as_dict(), wd_std=wd_std) # Add turbine weighing terms. These are typically used to distinguish # between turbines of interest and neighboring farms. This is particularly diff --git a/pyproject.toml b/pyproject.toml index 3a6ca824..4284220a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ "ephem", "coloredlogs~=15.0", "res-wind-up~=0.1", + "optuna~=4.0", "scikit-learn~=1.5", ] diff --git a/tests/cost_library_test.py b/tests/cost_library_test.py new file mode 100644 index 00000000..969af20f --- /dev/null +++ b/tests/cost_library_test.py @@ -0,0 +1,121 @@ +import numpy as np +import pandas as pd + +from flasc import FlascDataFrame +from flasc.model_fitting.cost_library import ( + FarmPowerMeanAbsoluteError, + FarmPowerRootMeanSquaredError, + TurbinePowerMeanAbsoluteError, + TurbinePowerRootMeanSquaredError, + WakeLossRootMeanSquaredError, +) + + +def setup_data(): + # Create a simple dataframe for SCADA data + df_scada = FlascDataFrame( + pd.DataFrame( + { + "time": np.array([0, 1, 2]), + "pow_000": np.array([1000.0, 1100.0, 1200.0]), + "pow_001": np.array([900.0, 950.0, 1000.0]), + } + ) + ) + + # Create a simple dataframe for FLORIS data + df_floris = FlascDataFrame( + pd.DataFrame( + { + "time": np.array([0, 1, 2]), + "pow_000": np.array([1050.0, 1150.0, 1250.0]), + "pow_001": np.array([950.0, 1000.0, 1050.0]), + } + ) + ) + + return df_scada, df_floris + + +def test_TurbinePowerRootMeanSquaredError(): + df_scada, df_floris = setup_data() + cf = TurbinePowerRootMeanSquaredError(df_scada) + + error = cf(df_floris) + expected_error = np.sqrt( + ( + ((df_scada["pow_000"] - df_floris["pow_000"]) ** 2).mean() + + ((df_scada["pow_001"] - df_floris["pow_001"]) ** 2).mean() + ) + / 2 + ) + + assert error == expected_error + + +def test_TurbinePowerMeanAbsoluteError(): + df_scada, df_floris = setup_data() + cf = TurbinePowerMeanAbsoluteError(df_scada) + + error = cf(df_floris) + expected_error = ( + (df_scada["pow_000"] - df_floris["pow_000"]).abs().mean() + + (df_scada["pow_001"] - df_floris["pow_001"]).abs().mean() + ) / 2 + + assert error == expected_error + + +def test_FarmPowerRootMeanSquaredError(): + df_scada, df_floris = setup_data() + cf = FarmPowerRootMeanSquaredError(df_scada) + + error = cf(df_floris) + expected_error = np.sqrt( + ( + ( + df_scada["pow_000"] + + df_scada["pow_001"] + - (df_floris["pow_000"] + df_floris["pow_001"]) + ) + ** 2 + ).mean() + ) + + assert error == expected_error + + +def test_FarmPowerMeanAbsoluteError(): + df_scada, df_floris = setup_data() + cf = FarmPowerMeanAbsoluteError(df_scada) + + error = cf(df_floris) + expected_error = ( + (df_scada["pow_000"] + df_scada["pow_001"] - (df_floris["pow_000"] + df_floris["pow_001"])) + .abs() + .mean() + ) + + assert error == expected_error + + +def test_WakeLossRootMeanSquaredError(): + df_scada, df_floris = setup_data() + cf = WakeLossRootMeanSquaredError( + df_scada, + test_turbines=[1], + reference_turbines=[0], + ) + + error = cf(df_floris) + expected_error = np.sqrt( + ( + ( + (df_scada["pow_000"] - df_scada["pow_001"]) + - (df_floris["pow_000"] - df_floris["pow_001"]) + ) + ** 2 + ).mean() + ) + + assert error == expected_error diff --git a/tests/model_fit_opt_test.py b/tests/model_fit_opt_test.py new file mode 100644 index 00000000..41425228 --- /dev/null +++ b/tests/model_fit_opt_test.py @@ -0,0 +1,292 @@ +import numpy as np +import pandas as pd +import pytest +from floris import UncertainFlorisModel + +from flasc.model_fitting.cost_library import ( + CostFunctionBase, + TurbinePowerMeanAbsoluteError, +) +from flasc.model_fitting.model_fit import ModelFit +from flasc.model_fitting.opt_library import ( + opt_optuna, + opt_optuna_with_wd_std, + opt_sweep, + opt_sweep_with_wd_std, +) +from flasc.utilities.utilities_examples import load_floris_artificial + + +def get_simple_inputs_gch(): + # TODO: share this between multiple test files? + # Create a simple dataframe + df = pd.DataFrame( + { + "time": np.array([0, 1, 2]), + "pow_000": np.array([1000.0, 1100.0, 1200.0]), + "ws_000": np.array([8.0, 9.0, 10.0]), + "wd_000": np.array([270.0, 270.0, 270.0]), + "pow_001": np.array([950.0, 1100.0, 1150.0]), + "ws_001": np.array([7.5, 8.5, 9.5]), + "wd_001": np.array([270.0, 270.0, 270.0]), + } + ) + + # Assign ws_000 to ws and wd_000 to wd using the assign function + df = df.assign(ws=df["ws_000"], wd=df["wd_000"]) + + # Load floris and set to single turbine layout + fm, _ = load_floris_artificial(wake_model="gch") + fm.set(layout_x=[0.0, 1000.0], layout_y=[0.0, 0.0]) + + # Define cost_function as a simple function + class CostFunctionTest(CostFunctionBase): + def cost(self, df_floris): + return None + + cost_function = CostFunctionTest(df) + + # Define the parameters to tune the kA parameter of GCH + parameter_list = [("wake", "wake_velocity_parameters", "gauss", "ka")] + parameter_name_list = ["kA"] + parameter_range_list = [(0.1, 0.5)] + parameter_index_list = [] + + return ( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + +def test_opt_sweep(): + # Get simple inputs + ( + df, + fm, + _, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Single parameter + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + results = opt_sweep( + mf=mf, + n_grid=5, + ) + + sweep_best = results["optimized_parameter_values"] + test_best = results["all_parameter_combinations"][np.argmin(results["all_costs"])] + + assert np.allclose(sweep_best, test_best) + + # Multiple parameters + parameter_list.append(("wake", "wake_velocity_parameters", "gauss", "kb")) + parameter_name_list.append("kB") + parameter_range_list.append((0.001, 0.005)) + + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + results = opt_sweep( + mf=mf, + n_grid=[5, 4], + ) + + sweep_best = results["optimized_parameter_values"] + test_best = results["all_parameter_combinations"][np.argmin(results["all_costs"])] + + assert np.allclose(sweep_best, test_best) + + +def test_opt_sweep_with_wd_std(): + # Get simple inputs + ( + df, + fm, + _, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Single parameter + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + # Should raise value error because fm is not an UncertainFlorisModel + with pytest.raises(ValueError): + opt_sweep_with_wd_std(mf, 5) + + mf.fmodel = UncertainFlorisModel(fm) + results = opt_sweep_with_wd_std( + mf=mf, + n_grid=[5, 3], # 5 for parameter, 3 for wd_std + wd_std_range=(1.0, 3.0), + ) + + sweep_best = results["optimized_parameter_values"] + test_best = results["all_parameter_combinations"][np.argmin(results["all_costs"])] + + assert np.allclose(sweep_best, test_best) + + +def test_opt_optuna(): + # Get simple inputs + ( + df, + fm, + _, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Single parameter + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + results = opt_optuna( + mf=mf, + n_trials=5, # Use small number for fast testing + ) + + # Check that results contain expected keys + assert "optimized_parameter_values" in results + assert "optimized_cost" in results + assert "optuna_study" in results + + # Check that optimized_parameter_values is a list with correct length + assert isinstance(results["optimized_parameter_values"], list) + assert len(results["optimized_parameter_values"]) == len(parameter_name_list) + + # Check that optimized_cost is a number + assert isinstance(results["optimized_cost"], (int, float)) + + # Reconstruct best from study and compare + study = results["optuna_study"] + optuna_best = [study.best_params[name] for name in parameter_name_list] + assert np.allclose(results["optimized_parameter_values"], optuna_best) + + # Multiple parameters + parameter_list.append(("wake", "wake_velocity_parameters", "gauss", "kb")) + parameter_name_list.append("kB") + parameter_range_list.append((0.001, 0.005)) + + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + results = opt_optuna( + mf=mf, + n_trials=5, # Use small number for fast testing + ) + + # Check that results contain expected keys + assert "optimized_parameter_values" in results + assert "optimized_cost" in results + assert "optuna_study" in results + + # Check that optimized_parameter_values is a list with correct length + assert isinstance(results["optimized_parameter_values"], list) + assert len(results["optimized_parameter_values"]) == len(parameter_name_list) + + # Check that optimized_cost is a number + assert isinstance(results["optimized_cost"], (int, float)) + + +def test_opt_optuna_with_wd_std(): + # Get simple inputs + ( + df, + fm, + _, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Single parameter + mf = ModelFit( + df, + fm, + TurbinePowerMeanAbsoluteError(), + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + # Should raise value error because fm is not an UncertainFlorisModel + with pytest.raises(ValueError): + opt_optuna_with_wd_std(mf, n_trials=5) + + mf.fmodel = UncertainFlorisModel(fm) + results = opt_optuna_with_wd_std( + mf=mf, + n_trials=5, # Use small number for fast testing + ) + + # Check that results contain expected keys + assert "optimized_parameter_values" in results + assert "optimized_cost" in results + assert "optuna_study" in results + + # Check that optimized_parameter_values is a list with correct length + # Should include the wd_std parameter as well + assert isinstance(results["optimized_parameter_values"], list) + assert len(results["optimized_parameter_values"]) == len(parameter_name_list) + 1 + + # Check that optimized_cost is a number + assert isinstance(results["optimized_cost"], (int, float)) + + # Reconstruct best (including wd_std) and compare + study = results["optuna_study"] + optuna_best = [study.best_params[name] for name in (parameter_name_list + ["wd_std"])] + assert np.allclose(results["optimized_parameter_values"], optuna_best) diff --git a/tests/model_fit_test.py b/tests/model_fit_test.py new file mode 100644 index 00000000..6d0a4db4 --- /dev/null +++ b/tests/model_fit_test.py @@ -0,0 +1,334 @@ +import numpy as np +import pandas as pd +import pytest +from floris import UncertainFlorisModel + +from flasc.model_fitting.cost_library import CostFunctionBase +from flasc.model_fitting.model_fit import ModelFit +from flasc.utilities.utilities_examples import load_floris_artificial + + +def get_simple_inputs_gch(): + # Create a simple dataframe + df = pd.DataFrame( + { + "time": np.array([0, 1, 2]), + "pow_000": np.array([1000.0, np.nan, 1200.0]), + "ws_000": np.array([8.0, 9.0, 10.0]), + "wd_000": np.array([270.0, 270.0, 270.0]), + } + ) + + # Assign ws_000 to ws and wd_000 to wd using the assign function + df = df.assign(ws=df["ws_000"], wd=df["wd_000"]) + + # Load floris and set to single turbine layout + fm, _ = load_floris_artificial(wake_model="gch") + fm.set(layout_x=[0.0], layout_y=[0.0]) + + # Define cost_function as a simple function + class CostFunctionTest(CostFunctionBase): + def cost(self, df_floris): + return None + + cost_function = CostFunctionTest(df) + + # Define the parameters to tune the kA parameter of GCH + parameter_list = [("wake", "wake_velocity_parameters", "gauss", "ka")] + parameter_name_list = ["kA"] + parameter_range_list = [(0.1, 0.5)] + parameter_index_list = [] + + return ( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + +def test_instantiate_model(): + # Get simple inputs + ( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Instantiate the ModelFit object without parameters + ModelFit( + df, + fm, + cost_function, + ) + + # Instantiate the ModelFit object with parameters + ModelFit( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + +def test_df(): + # Get simple inputs + ( + df, + fm, + cost_function, + _, + _, + _, + _, + ) = get_simple_inputs_gch() + + # Remove the wd column from the dataframe + df = df.drop(columns=["wd"]) + + # Instantiate the ModelFit object without parameters + with pytest.raises(ValueError): + ModelFit( + df, + fm, + cost_function, + ) + + +def test_get_set_param_no_params(): + # Get simple inputs + ( + df, + fm, + cost_function, + _, + _, + _, + _, + ) = get_simple_inputs_gch() + + # Instantiate the ModelFit object without parameters + model_fit = ModelFit( + df, + fm, + cost_function, + ) + + # Assert that parameter_index_list is an empty list + assert model_fit.parameter_index_list == [] + + # Assert number parameters = 0 + assert model_fit.n_parameters == 0 + + # Assert that initial_parameter_values is a numpy array with length 0 + np.testing.assert_array_equal(model_fit.initial_parameter_values, np.array([])) + + # Get that get_parameter_values returns an empty numpy array + np.testing.assert_array_equal(model_fit.get_parameter_values(), np.array([])) + + +def test_get_set_param_with_params(): + # Get simple inputs + ( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Instantiate the ModelFit object with parameters + model_fit = ModelFit( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + # Check the initialization of the initial parameter values + np.testing.assert_array_equal(model_fit.initial_parameter_values, np.array([0.38])) + np.testing.assert_array_equal(model_fit.get_parameter_values(), np.array([0.38])) + + # Change the model parameter values + model_fit.set_parameter_values(np.array([10.0])) + + # Check the set value + np.testing.assert_array_equal(model_fit.get_parameter_values(), np.array([10.0])) + + # Assert that parameter_index_list is a list with one element, and that element is None + assert model_fit.parameter_index_list == [None] + + +def test_run_floris(): + # Get simple inputs + ( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) = get_simple_inputs_gch() + + # Instantiate the ModelFit object with parameters + model_fit = ModelFit( + df, + fm, + cost_function, + parameter_list, + parameter_name_list, + parameter_range_list, + parameter_index_list, + ) + + df_floris = model_fit.run_floris_model() + + # df_floris is a FlaskDataFrame + assert isinstance(df_floris, pd.DataFrame) + + # df and df_floris have the same number of rows + assert df.shape[0] == df_floris.shape[0] + + # df['ws] == df_floris['ws'] + np.testing.assert_array_equal(df["ws"].values, df_floris["ws"].values) + + # The second element of df_floris['pow_000'] is a NaN + assert np.isnan(df_floris.loc[1, "pow_000"]) + + # Check that the first element in power corresponds to power of 8 m/s + # for default NREL 5MW turbine + assert np.isclose(df_floris.loc[0, "pow_000"], 1753.9, atol=10) + + +def test_cost_function(): + # Get simple inputs + df, fm = get_simple_inputs_gch()[0:2] + + # Cost function has to be subclass of CostFunctionBase + # Cost function has wrong number of inputs + class Cost1: + def __init__(self): + pass + + def cost(self, df_floris): + return 0 + + # Cost function must be an subclass of CostFunctionBase + with pytest.raises(TypeError): + ModelFit( + df, + fm, + Cost1(), + ) + + # Wrong number of arguments in cost function + class Cost2(CostFunctionBase): + def cost(self, df_floris, extra_arg): + return 0 + + # Uninstantiated cost function raises TypeError + with pytest.raises(TypeError): + ModelFit(df, fm, Cost2) + + # Wrong number of arguments in cost function. + with pytest.raises(TypeError): + cf = Cost2() + cf(df) + + # Valid cost function (extra argument in init, but not in cost) + class Cost3(CostFunctionBase): + def __init__(self, df_scada, temp_arg): + super().__init__(df_scada) + self.temp_arg = temp_arg + + def cost(self, df_floris): + self.temp_arg += 1 + return 0 + + # Instantiate without providing the extra argument raises TypeError + with pytest.raises(TypeError): + Cost3(df) + + # Instantiate with providing the extra argument works + ModelFit(df, fm, Cost3(df, temp_arg=5)) + + +def test_use_non_default_wd_sample_points(): + """Test that use_non_default_wd_sample_points correctly scales wd_sample_points with wd_std.""" + # Create a simple dataframe + df = pd.DataFrame( + { + "time": np.array([0, 1, 2]), + "pow_000": np.array([1000.0, np.nan, 1200.0]), + "ws_000": np.array([8.0, 9.0, 10.0]), + "wd_000": np.array([270.0, 270.0, 270.0]), + } + ) + df = df.assign(ws=df["ws_000"], wd=df["wd_000"]) + + # Load floris and set to single turbine layout + fm, _ = load_floris_artificial(wake_model="gch") + fm.set(layout_x=[0.0], layout_y=[0.0]) + + # Create an UncertainFlorisModel with initial wd_std and custom wd_sample_points + initial_wd_std = 5.0 + custom_wd_sample_points = np.array([-3.0, -1.5, 0.0, 1.5, 3.0]) # in degrees + fm_uncertain = UncertainFlorisModel( + fm.core.as_dict(), wd_std=initial_wd_std, wd_sample_points=custom_wd_sample_points + ) + + # Define cost_function as a simple function + class CostFunctionTest(CostFunctionBase): + def cost(self, df_floris): + return None + + cost_function = CostFunctionTest(df) + + # Create ModelFit with use_non_default_wd_sample_points=True + model_fit = ModelFit(df, fm_uncertain, cost_function, use_non_default_wd_sample_points=True) + + # Test with first wd_std value + wd_std_1 = 3.0 + model_fit.set_wd_std(wd_std_1) + + # Expected wd_sample_points should be the original custom points scaled by the ratio + expected_wd_sample_points_1 = custom_wd_sample_points * (wd_std_1 / initial_wd_std) + np.testing.assert_allclose( + model_fit.fmodel.wd_sample_points, expected_wd_sample_points_1, rtol=1e-10 + ) + + # Test with second wd_std value + wd_std_2 = 7.0 + model_fit.set_wd_std(wd_std_2) + + # Expected wd_sample_points should be the original custom points scaled by the new ratio + expected_wd_sample_points_2 = custom_wd_sample_points * (wd_std_2 / initial_wd_std) + np.testing.assert_allclose( + model_fit.fmodel.wd_sample_points, expected_wd_sample_points_2, rtol=1e-10 + ) + + # Verify that the scaling is different for the two wd_std values + assert not np.allclose(expected_wd_sample_points_1, expected_wd_sample_points_2) + + # Test that if wd_std_3 is 5.0, the wd_sample_points are the same as the original custom points + wd_std_3 = 5.0 + model_fit.set_wd_std(wd_std_3) + np.testing.assert_allclose( + model_fit.fmodel.wd_sample_points, custom_wd_sample_points, rtol=1e-10 + ) diff --git a/tests/tuning_utilities_test.py b/tests/tuning_utilities_test.py new file mode 100644 index 00000000..6790e378 --- /dev/null +++ b/tests/tuning_utilities_test.py @@ -0,0 +1,62 @@ +import numpy as np +import pandas as pd + +from flasc.utilities.tuner_utilities import replicate_nan_values + + +def test_replicate_nan_values(): + # Sample dataframes + data_1 = {"A": [1, 2, np.nan, 4], "B": [5, np.nan, 7, 8], "C": [np.nan, 1, 1, 1]} + data_2 = {"A": [10, 20, 30, 40], "B": [50, 60, 70, 80]} + df_1 = pd.DataFrame(data_1) + df_2 = pd.DataFrame(data_2) + + # Call the function to replicate NaN values + result_df = replicate_nan_values(df_1, df_2) + + # Expected output + expected_df_1 = pd.DataFrame( + {"A": [1, 2, np.nan, 4], "B": [5, np.nan, 7, 8], "C": [np.nan, 1, 1, 1]} + ) + expected_df_2 = pd.DataFrame({"A": [10, 20, np.nan, 40], "B": [50, np.nan, 70, 80]}) + + # Check if the result matches the expected output + assert result_df.equals(expected_df_2) + assert df_1.equals(expected_df_1) + + +def test_replicate_nan_values_with_time(): + # Sample dataframes + data_1 = {"A": [1, 2, np.nan, 4], "B": [5, np.nan, 7, 8], "C": [np.nan, 1, 1, 1]} + data_2 = {"A": [10, 20, 30, 40], "B": [50, 60, 70, 80]} + df_1 = pd.DataFrame(data_1) + df_2 = pd.DataFrame(data_2) + + df_1["time"] = pd.date_range("2021-01-01", periods=4) + df_2["time"] = df_1["time"] + + # Call the function to replicate NaN values + result_df = replicate_nan_values(df_1, df_2) + + print(result_df) + + # Expected output + expected_df_1 = pd.DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [5, np.nan, 7, 8], + "C": [np.nan, 1, 1, 1], + "time": pd.date_range("2021-01-01", periods=4), + } + ) + expected_df_2 = pd.DataFrame( + { + "A": [10, 20, np.nan, 40], + "B": [50, np.nan, 70, 80], + "time": pd.date_range("2021-01-01", periods=4), + } + ) + + # Check if the result matches the expected output + assert result_df.equals(expected_df_2) + assert df_1.equals(expected_df_1)