<gh_stars>1-10
# Copyright 2021 NREL

# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.


import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns

from floris.tools.uncertainty_interface import UncertaintyInterface
from flasc.wake_steering.lookup_table_tools import get_yaw_angles_interpolant
from _local_helper_functions import load_floris, optimize_yaw_angles, evaluate_optimal_yaw_angles


def load_floris_with_uncertainty(std_wd=0.0):
    fi = load_floris()  # Load nominal floris object
    if std_wd > 0.001:
        unc_options = {
            "std_wd": std_wd,  # 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 (-)
        }
        fi = UncertaintyInterface(fi, unc_options=unc_options)  # Load uncertainty object
    return fi


if __name__ == "__main__":
    # Define std_wd range
    std_wd_list = [0.0, 3.0, 5.0]
    result_list = []

    # Compare optimizing over different std_wd and evaluating over different std_wd values
    for std_wd_opt in std_wd_list:
        print("Optimizing yaw angles with std_wd={:.2f}".format(std_wd_opt))
        # Optimize yaw angles
        df_opt = optimize_yaw_angles(
            fi=load_floris_with_uncertainty(std_wd=std_wd_opt),
        )

        # Make an interpolant
        yaw_angle_interpolant = get_yaw_angles_interpolant(df_opt)  # Create yaw angle interpolant

        # Calculate AEP uplift
        for std_wd_eval in std_wd_list:
            print("Evalating AEP uplift with std_wd={:.2f}".format(std_wd_eval))
            AEP_baseline, AEP_opt, _ = evaluate_optimal_yaw_angles(
                fi=load_floris_with_uncertainty(std_wd=std_wd_eval),
                yaw_angle_interpolant=yaw_angle_interpolant,
            )

            # Calculate AEP uplift
            uplift = 100.0 * (AEP_opt - AEP_baseline) / AEP_baseline
            result_list.append(
                pd.DataFrame(
                    {
                        "std_wd_opt": [std_wd_opt],
                        "std_wd_eval": [std_wd_eval],
                        "AEP uplift (%)": [uplift]
                    },
                )
            )

    # Print all results to console
    df_result = pd.concat(result_list, axis=0, ignore_index=True)
    with pd.option_context('display.max_rows', None):
        print(df_result)

    # Plot as a table/colormap
    df_result = df_result.set_index(["std_wd_opt", "std_wd_eval"]).unstack()
    df_result.columns = ["std_wd_eval={:.2f}".format(p) for p in std_wd_list]
    ax = sns.heatmap(df_result, linecolor="black", linewidths=1, annot=True, fmt=".2f")
    ax.set_title("AEP uplift (%)")
    plt.tight_layout()
    plt.show()

# Copyright 2021 NREL

# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.


import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns

from floris.tools.uncertainty_interface import UncertaintyInterface
from flasc.wake_steering.lookup_table_tools import get_yaw_angles_interpolant
from _local_helper_functions import load_floris, optimize_yaw_angles, evaluate_optimal_yaw_angles


def load_floris_with_uncertainty(std_wd=0.0):
    fi = load_floris()  # Load nominal floris object
    if std_wd > 0.001:
        unc_options = {
            "std_wd": std_wd,  # 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 (-)
        }
        fi = UncertaintyInterface(fi, unc_options=unc_options)  # Load uncertainty object
    return fi


if __name__ == "__main__":
    # Define std_wd range
    std_wd_list = [0.0, 3.0, 5.0]
    result_list = []

    # Compare optimizing over different std_wd and evaluating over different std_wd values
    for std_wd_opt in std_wd_list:
        print("Optimizing yaw angles with std_wd={:.2f}".format(std_wd_opt))
        # Optimize yaw angles
        df_opt = optimize_yaw_angles(
            fi=load_floris_with_uncertainty(std_wd=std_wd_opt),
        )

        # Make an interpolant
        yaw_angle_interpolant = get_yaw_angles_interpolant(df_opt)  # Create yaw angle interpolant

        # Calculate AEP uplift
        for std_wd_eval in std_wd_list:
            print("Evalating AEP uplift with std_wd={:.2f}".format(std_wd_eval))
            AEP_baseline, AEP_opt, _ = evaluate_optimal_yaw_angles(
                fi=load_floris_with_uncertainty(std_wd=std_wd_eval),
                yaw_angle_interpolant=yaw_angle_interpolant,
            )

            # Calculate AEP uplift
            uplift = 100.0 * (AEP_opt - AEP_baseline) / AEP_baseline
            result_list.append(
                pd.DataFrame(
                    {
                        "std_wd_opt": [std_wd_opt],
                        "std_wd_eval": [std_wd_eval],
                        "AEP uplift (%)": [uplift]
                    },
                )
            )

    # Print all results to console
    df_result = pd.concat(result_list, axis=0, ignore_index=True)
    with pd.option_context('display.max_rows', None):
        print(df_result)

    # Plot as a table/colormap
    df_result = df_result.set_index(["std_wd_opt", "std_wd_eval"]).unstack()
    df_result.columns = ["std_wd_eval={:.2f}".format(p) for p in std_wd_list]
    ax = sns.heatmap(df_result, linecolor="black", linewidths=1, annot=True, fmt=".2f")
    ax.set_title("AEP uplift (%)")
    plt.tight_layout()
    plt.show()
# Copyright 2021 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # Load nominal floris object # Standard deviation for inflow wind direction (deg) # Resolution over which to calculate angles (deg) # Probability density function cut-off (-) # Load uncertainty object # Define std_wd range # Compare optimizing over different std_wd and evaluating over different std_wd values # Optimize yaw angles # Make an interpolant # Create yaw angle interpolant # Calculate AEP uplift # Calculate AEP uplift # Print all results to console # Plot as a table/colormap