# Copyright 2021 NREL

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

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

# See https://floris.readthedocs.io for documentation


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def gaussian(x, mu, sig):
    """
    Compute gaussian function, from
    https://stackoverflow.com/questions/14873203/plotting-of-1-dimensional-gaussian-distribution-function.

    Args:
        x (np.array): Input variable to Gaussian.
        mu (float): Mean value.
        sig (float): Standard deviation.

    Returns:
        np.array: The resulting Gaussian distribution.
    """
    return np.exp(-np.power(x - mu, 2.0) / (2 * np.power(sig, 2.0)))


def _convert_to_numpy_array(series):
    """
    Convert an input series to NumPy array. Currently, this function
    checks if an object has a `values` attribute and returns that if it does.
    Otherwise, it returns the given input if that input is a `np.ndarray`.

    Args:
        series (pd.Series): Series to convert.

    Returns:
        np.array: Converted Series.
    """
    if hasattr(series, "values"):
        return series.values
    elif isinstance(series, np.ndarray):
        return series


def _calculate_bootstrap_iterations(n):
    """
    Calculate number of bootstrap iterations given length.
    # TODO: What are `bootstrap iterations`?

    Args:
        n (int): Number of points.

    Returns:
        int: Number of bootstrap iterations.
    """
    maximum = 10000
    minimum = 2000
    return int(np.round(max(min(n * np.log10(n), maximum), minimum)))


def _calculate_lower_and_upper_bound(
    bootstrap_array, percentiles, central_estimate=None, method="simple_percentile"
):
    """
    Given resultant bootstrap output array, compute lower and upper bound
    of confidence interval.

    Args:
        bootstrap_array (np.array): array of bootrapped results
            percentiles (np.array): percentile values
            central_estimate (float, optional): if not using simple percentile,
            need to provide the central estimated result. Defaults to None.
        method (str, optional): method for computing bounds. Defaults to
            'simple_percentile'.

    Returns:
        float, float:

            -   lower ci bound
            -   upper ci bound
    """
    if method == "simple_percentile":
        upper, lower = np.percentile(bootstrap_array, percentiles)
    else:
        lower, upper = 2 * central_estimate - np.percentile(
            bootstrap_array, percentiles
        )
    return lower, upper


def _get_confidence_bounds(confidence):
    """

    Get the upper and lower confidence bounds given a desired confidence level.

    Args:
        confidence (float): [description]
        # TODO: ^^

    Returns:
        float, float:

            -   upper confidence bound
            -   lower confidence bound
    """
    return [50 + 0.5 * confidence, 50 - 0.5 * confidence]


def energy_ratio(ref_pow_base, test_pow_base):
    """
    Compute the balanced energy ratio for a single binned wind direction

    Single version, not comparing controller on controller off

    This function is called to compute a single balanced
    energy ratio calculation for a particular wind direction bin.  Note
    the reference turbine should not be the turbine implementing
    control, but should be an unaffected nearby turbine, or a synthetic
    power estimate from a measurement.  See :cite:`ers-fleming2019initial` and
    :cite:`ers-fleming2019continued` for more information.

    References:
        .. bibliography:: /source/zrefs.bib
            :style: unsrt
            :filter: docname in docnames
            :keyprefix: ers-

    Args:
        ref_pow_base (np.array): Array of baseline reference turbine
            power.
        test_pow_base (np.array): Array of baseline test turbine power.


    Returns:
        tuple: tuple containing:

            -   **ratio_base** (*float*): Baseline energy ratio.
            -   **counts_base** (*float*): Number of points in baseline.
    """

    if len(ref_pow_base) == 0:
        return np.nan, np.nan

    # Weighted sums
    weight_sum_ref_base = np.sum(ref_pow_base)
    weight_sum_test_base = np.sum(test_pow_base)

    if (weight_sum_ref_base == 0) or (weight_sum_test_base == 0):
        return np.nan, np.nan

    ratio_base = weight_sum_test_base / weight_sum_ref_base

    # Get the counts
    counts_base = len(ref_pow_base)

    return ratio_base, counts_base


def calculate_balanced_energy_ratio(
    reference_power_baseline,
    test_power_baseline,
    wind_direction_array_baseline,
    wind_direction_bins,
    confidence=95,
    n_boostrap=None,
    wind_direction_bin_p_overlap=None,
):
    """
    Calculate a balanced energy ratio for each wind direction bin.

    Single version, not divided into baseline and controlled

    Calculate a balanced energy ratio for each wind direction bin.  A
    reference and test turbine are provided for the ratio, as well as
    wind speed and wind directions. These data are further divided into
    baseline and controlled conditions.  The balanced energy ratio
    function is called and used to ensure a similar distribution of
    wind speeds is used in the computation, per wind direction bin, for
    baseline and controlled results.  Resulting arrays, including upper
    and lower uncertainty bounds computed through bootstrapping, are
    returned.  Note the reference turbine should not be the turbine
    implementing control, but should be an unaffected nearby turbine,
    or a synthetic power estimate from a measurement  See
    :cite:`ers-fleming2019initial` and
    :cite:`ers-fleming2019continued` for more information.

    References:
        .. bibliography:: /source/zrefs.bib
            :style: unsrt
            :filter: docname in docnames
            :keyprefix: ers-

    Args:
        reference_power_baseline (np.array): Array of power of
            reference turbine in baseline conditions.
        test_power_baseline (np.array): Array of power of test turbine
            in baseline conditions.
        wind_speed_array_baseline (np.array): Array of wind speeds in
            baseline conditions.
        wind_direction_array_baseline (np.array): Array of wind
            directions in baseline case.
        wind_direction_bins (np.array): Wind directions bins.
        confidence (int, optional): Confidence level to use.  Defaults
            to 95.
        n_boostrap (int, optional): Number of bootstaps, if none,
            _calculate_bootstrap_iterations is called.  Defaults to
            None.
        wind_direction_bin_p_overlap (np.array, optional): Percentage
            overlap between wind direction bin. Defaults to None.

    Returns:
        tuple: tuple containing:

            **ratio_array_base** (*np.array*): Baseline energy ratio at each wind direction bin.
            **lower_ratio_array_base** (*np.array*): Lower confidence bound of baseline energy ratio at each wind direction bin.
            **upper_ratio_array_base** (*np.array*): Upper confidence bound of baseline energy ratio at each wind direction bin.
            **counts_ratio_array_base** (*np.array*): Counts per wind direction bin in baseline.
    """

    # Ensure that input arrays are np.ndarray
    reference_power_baseline = _convert_to_numpy_array(reference_power_baseline)
    test_power_baseline = _convert_to_numpy_array(test_power_baseline)
    wind_direction_array_baseline = _convert_to_numpy_array(
        wind_direction_array_baseline
    )

    # Handle no overlap specificed (assume non-overlap)
    if wind_direction_bin_p_overlap is None:
        wind_direction_bin_p_overlap = 0

    # Compute binning radius (is this right?)
    wind_direction_bin_radius = (
        (1.0 + wind_direction_bin_p_overlap / 100.0)
        * (wind_direction_bins[1] - wind_direction_bins[0])
        / 2.0
    )

    ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    lower_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    upper_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    counts_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan

    for i, wind_direction_bin in enumerate(wind_direction_bins):

        wind_dir_mask_baseline = (
            wind_direction_array_baseline
            >= wind_direction_bin - wind_direction_bin_radius
        ) & (
            wind_direction_array_baseline
            < wind_direction_bin + wind_direction_bin_radius
        )

        reference_power_baseline_wd = reference_power_baseline[wind_dir_mask_baseline]
        test_power_baseline_wd = test_power_baseline[wind_dir_mask_baseline]
        wind_dir_array_baseline_wd = wind_direction_array_baseline[
            wind_dir_mask_baseline
        ]
        baseline_weight = gaussian(
            wind_dir_array_baseline_wd,
            wind_direction_bin,
            wind_direction_bin_radius / 2.0,
        )
        baseline_weight = baseline_weight / np.sum(baseline_weight)

        if len(reference_power_baseline_wd) == 0:
            continue

        # compute the energy ratio
        # ratio_array_base[i], counts_ratio_array_base[i] = energy_ratio(reference_power_baseline_wd, test_power_baseline_wd)

        # Get the bounds through boot strapping
        # determine the number of bootstrap iterations if not given
        if n_boostrap is None:
            n_boostrap = _calculate_bootstrap_iterations(
                len(reference_power_baseline_wd)
            )

        ratio_base_bs = np.zeros(n_boostrap)
        for i_bs in range(n_boostrap):

            # random resampling w/ replacement
            # ind_bs = np.random.randint(
            #     len(reference_power_baseline_wd), size=len(reference_power_baseline_wd))
            ind_bs = np.random.choice(
                len(reference_power_baseline_wd),
                size=len(reference_power_baseline_wd),
                p=baseline_weight,
            )
            reference_power_binned_baseline = reference_power_baseline_wd[ind_bs]
            test_power_binned_baseline = test_power_baseline_wd[ind_bs]

            # compute the energy ratio
            ratio_base_bs[i_bs], _ = energy_ratio(
                reference_power_binned_baseline, test_power_binned_baseline
            )

        # Get the confidence bounds
        percentiles = _get_confidence_bounds(confidence)

        # Compute the central over from the bootstrap runs
        ratio_array_base[i] = np.mean(ratio_base_bs)

        (
            lower_ratio_array_base[i],
            upper_ratio_array_base[i],
        ) = _calculate_lower_and_upper_bound(
            ratio_base_bs,
            percentiles,
            central_estimate=ratio_array_base[i],
            method="simple_percentile",
        )

    return (
        ratio_array_base,
        lower_ratio_array_base,
        upper_ratio_array_base,
        counts_ratio_array_base,
    )


def plot_energy_ratio(
    reference_power_baseline,
    test_power_baseline,
    wind_speed_array_baseline,
    wind_direction_array_baseline,
    wind_direction_bins,
    confidence=95,
    n_boostrap=None,
    wind_direction_bin_p_overlap=None,
    ax=None,
    base_color="b",
    label_array=None,
    label_pchange=None,
    plot_simple=False,
    plot_ratio_scatter=False,
    marker_scale=1.0,
    show_count=True,
    hide_controlled_case=False,
):
    """
    Plot the single energy ratio.

    Function mainly acts as a wrapper to call
    calculate_balanced_energy_ratio and plot the results.

    Args:
        reference_power_baseline (np.array): Array of power
            of reference turbine in baseline conditions.
        test_power_baseline (np.array): Array of power of
            test turbine in baseline conditions.
        wind_speed_array_baseline (np.array): Array of wind
            speeds in baseline conditions.
        wind_direction_array_baseline (np.array): Array of
            wind directions in baseline case.
        wind_direction_bins (np.array): Wind directions bins.
        confidence (int, optional): Confidence level to use.
            Defaults to 95.
        n_boostrap (int, optional): Number of bootstaps, if
            none, _calculate_bootstrap_iterations is called.  Defaults
            to None.
        wind_direction_bin_p_overlap (np.array, optional):
            Percentage overlap between wind direction bin. Defaults to
            None.
        axarr ([axes], optional): list of axes to plot to.
            Defaults to None.
        base_color (str, optional): Color of baseline in
            plots. Defaults to 'b'.
        label_array ([str], optional): List of labels to
            apply Defaults to None.
        label_pchange ([type], optional): Label for
            percentage change. Defaults to None.
        plot_simple (bool, optional): Plot only the ratio, no
            confidence. Defaults to False.
        plot_ratio_scatter (bool, optional): Include scatter
            plot of values, sized to indicate counts. Defaults to False.
        marker_scale ([type], optional): Marker scale.
            Defaults to 1.
        show_count (bool, optional): Show the counts as scatter plot
        hide_controlled_case (bool, optional): Option to hide the control case from plots, for demonstration

    """

    if ax is None:
        fig, ax = plt.subplots(1, 1, sharex=True)

    if label_array is None:
        label_array = ["Baseline"]

    if label_pchange is None:
        label_pchange = "Energy Gain"

    (
        ratio_array_base,
        lower_ratio_array_base,
        upper_ratio_array_base,
        counts_ratio_array_base,
    ) = calculate_balanced_energy_ratio(
        reference_power_baseline,
        test_power_baseline,
        wind_direction_array_baseline,
        wind_direction_bins,
        confidence=95,
        n_boostrap=None,
        wind_direction_bin_p_overlap=wind_direction_bin_p_overlap,
    )

    if plot_simple:
        ax.plot(
            wind_direction_bins,
            ratio_array_base,
            label=label_array[0],
            color=base_color,
            ls="--",
        )
        ax.axhline(1, color="k")
        ax.set_ylabel("Energy Ratio (-)")

    else:

        ax.plot(
            wind_direction_bins,
            ratio_array_base,
            label=label_array[0],
            color=base_color,
            ls="-",
            marker=".",
        )
        ax.fill_between(
            wind_direction_bins,
            lower_ratio_array_base,
            upper_ratio_array_base,
            alpha=0.3,
            color=base_color,
            label="_nolegend_",
        )
        if show_count:
            ax.scatter(
                wind_direction_bins,
                ratio_array_base,
                s=counts_ratio_array_base,
                label="_nolegend_",
                color=base_color,
                marker="o",
                alpha=0.2,
            )
        ax.axhline(1, color="k")
        ax.set_ylabel("Energy Ratio (-)")

        ax.grid(True)
        ax.set_xlabel("Wind Direction (Deg)")

# Copyright 2021 NREL

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

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

# See https://floris.readthedocs.io for documentation


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


def gaussian(x, mu, sig):
    """
    Compute gaussian function, from
    https://stackoverflow.com/questions/14873203/plotting-of-1-dimensional-gaussian-distribution-function.

    Args:
        x (np.array): Input variable to Gaussian.
        mu (float): Mean value.
        sig (float): Standard deviation.

    Returns:
        np.array: The resulting Gaussian distribution.
    """
    return np.exp(-np.power(x - mu, 2.0) / (2 * np.power(sig, 2.0)))


def _convert_to_numpy_array(series):
    """
    Convert an input series to NumPy array. Currently, this function
    checks if an object has a `values` attribute and returns that if it does.
    Otherwise, it returns the given input if that input is a `np.ndarray`.

    Args:
        series (pd.Series): Series to convert.

    Returns:
        np.array: Converted Series.
    """
    if hasattr(series, "values"):
        return series.values
    elif isinstance(series, np.ndarray):
        return series


def _calculate_bootstrap_iterations(n):
    """
    Calculate number of bootstrap iterations given length.
    # TODO: What are `bootstrap iterations`?

    Args:
        n (int): Number of points.

    Returns:
        int: Number of bootstrap iterations.
    """
    maximum = 10000
    minimum = 2000
    return int(np.round(max(min(n * np.log10(n), maximum), minimum)))


def _calculate_lower_and_upper_bound(
    bootstrap_array, percentiles, central_estimate=None, method="simple_percentile"
):
    """
    Given resultant bootstrap output array, compute lower and upper bound
    of confidence interval.

    Args:
        bootstrap_array (np.array): array of bootrapped results
            percentiles (np.array): percentile values
            central_estimate (float, optional): if not using simple percentile,
            need to provide the central estimated result. Defaults to None.
        method (str, optional): method for computing bounds. Defaults to
            'simple_percentile'.

    Returns:
        float, float:

            -   lower ci bound
            -   upper ci bound
    """
    if method == "simple_percentile":
        upper, lower = np.percentile(bootstrap_array, percentiles)
    else:
        lower, upper = 2 * central_estimate - np.percentile(
            bootstrap_array, percentiles
        )
    return lower, upper


def _get_confidence_bounds(confidence):
    """

    Get the upper and lower confidence bounds given a desired confidence level.

    Args:
        confidence (float): [description]
        # TODO: ^^

    Returns:
        float, float:

            -   upper confidence bound
            -   lower confidence bound
    """
    return [50 + 0.5 * confidence, 50 - 0.5 * confidence]


def energy_ratio(ref_pow_base, test_pow_base):
    """
    Compute the balanced energy ratio for a single binned wind direction

    Single version, not comparing controller on controller off

    This function is called to compute a single balanced
    energy ratio calculation for a particular wind direction bin.  Note
    the reference turbine should not be the turbine implementing
    control, but should be an unaffected nearby turbine, or a synthetic
    power estimate from a measurement.  See :cite:`ers-fleming2019initial` and
    :cite:`ers-fleming2019continued` for more information.

    References:
        .. bibliography:: /source/zrefs.bib
            :style: unsrt
            :filter: docname in docnames
            :keyprefix: ers-

    Args:
        ref_pow_base (np.array): Array of baseline reference turbine
            power.
        test_pow_base (np.array): Array of baseline test turbine power.


    Returns:
        tuple: tuple containing:

            -   **ratio_base** (*float*): Baseline energy ratio.
            -   **counts_base** (*float*): Number of points in baseline.
    """

    if len(ref_pow_base) == 0:
        return np.nan, np.nan

    # Weighted sums
    weight_sum_ref_base = np.sum(ref_pow_base)
    weight_sum_test_base = np.sum(test_pow_base)

    if (weight_sum_ref_base == 0) or (weight_sum_test_base == 0):
        return np.nan, np.nan

    ratio_base = weight_sum_test_base / weight_sum_ref_base

    # Get the counts
    counts_base = len(ref_pow_base)

    return ratio_base, counts_base


def calculate_balanced_energy_ratio(
    reference_power_baseline,
    test_power_baseline,
    wind_direction_array_baseline,
    wind_direction_bins,
    confidence=95,
    n_boostrap=None,
    wind_direction_bin_p_overlap=None,
):
    """
    Calculate a balanced energy ratio for each wind direction bin.

    Single version, not divided into baseline and controlled

    Calculate a balanced energy ratio for each wind direction bin.  A
    reference and test turbine are provided for the ratio, as well as
    wind speed and wind directions. These data are further divided into
    baseline and controlled conditions.  The balanced energy ratio
    function is called and used to ensure a similar distribution of
    wind speeds is used in the computation, per wind direction bin, for
    baseline and controlled results.  Resulting arrays, including upper
    and lower uncertainty bounds computed through bootstrapping, are
    returned.  Note the reference turbine should not be the turbine
    implementing control, but should be an unaffected nearby turbine,
    or a synthetic power estimate from a measurement  See
    :cite:`ers-fleming2019initial` and
    :cite:`ers-fleming2019continued` for more information.

    References:
        .. bibliography:: /source/zrefs.bib
            :style: unsrt
            :filter: docname in docnames
            :keyprefix: ers-

    Args:
        reference_power_baseline (np.array): Array of power of
            reference turbine in baseline conditions.
        test_power_baseline (np.array): Array of power of test turbine
            in baseline conditions.
        wind_speed_array_baseline (np.array): Array of wind speeds in
            baseline conditions.
        wind_direction_array_baseline (np.array): Array of wind
            directions in baseline case.
        wind_direction_bins (np.array): Wind directions bins.
        confidence (int, optional): Confidence level to use.  Defaults
            to 95.
        n_boostrap (int, optional): Number of bootstaps, if none,
            _calculate_bootstrap_iterations is called.  Defaults to
            None.
        wind_direction_bin_p_overlap (np.array, optional): Percentage
            overlap between wind direction bin. Defaults to None.

    Returns:
        tuple: tuple containing:

            **ratio_array_base** (*np.array*): Baseline energy ratio at each wind direction bin.
            **lower_ratio_array_base** (*np.array*): Lower confidence bound of baseline energy ratio at each wind direction bin.
            **upper_ratio_array_base** (*np.array*): Upper confidence bound of baseline energy ratio at each wind direction bin.
            **counts_ratio_array_base** (*np.array*): Counts per wind direction bin in baseline.
    """

    # Ensure that input arrays are np.ndarray
    reference_power_baseline = _convert_to_numpy_array(reference_power_baseline)
    test_power_baseline = _convert_to_numpy_array(test_power_baseline)
    wind_direction_array_baseline = _convert_to_numpy_array(
        wind_direction_array_baseline
    )

    # Handle no overlap specificed (assume non-overlap)
    if wind_direction_bin_p_overlap is None:
        wind_direction_bin_p_overlap = 0

    # Compute binning radius (is this right?)
    wind_direction_bin_radius = (
        (1.0 + wind_direction_bin_p_overlap / 100.0)
        * (wind_direction_bins[1] - wind_direction_bins[0])
        / 2.0
    )

    ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    lower_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    upper_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan
    counts_ratio_array_base = np.zeros(len(wind_direction_bins)) * np.nan

    for i, wind_direction_bin in enumerate(wind_direction_bins):

        wind_dir_mask_baseline = (
            wind_direction_array_baseline
            >= wind_direction_bin - wind_direction_bin_radius
        ) & (
            wind_direction_array_baseline
            < wind_direction_bin + wind_direction_bin_radius
        )

        reference_power_baseline_wd = reference_power_baseline[wind_dir_mask_baseline]
        test_power_baseline_wd = test_power_baseline[wind_dir_mask_baseline]
        wind_dir_array_baseline_wd = wind_direction_array_baseline[
            wind_dir_mask_baseline
        ]
        baseline_weight = gaussian(
            wind_dir_array_baseline_wd,
            wind_direction_bin,
            wind_direction_bin_radius / 2.0,
        )
        baseline_weight = baseline_weight / np.sum(baseline_weight)

        if len(reference_power_baseline_wd) == 0:
            continue

        # compute the energy ratio
        # ratio_array_base[i], counts_ratio_array_base[i] = energy_ratio(reference_power_baseline_wd, test_power_baseline_wd)

        # Get the bounds through boot strapping
        # determine the number of bootstrap iterations if not given
        if n_boostrap is None:
            n_boostrap = _calculate_bootstrap_iterations(
                len(reference_power_baseline_wd)
            )

        ratio_base_bs = np.zeros(n_boostrap)
        for i_bs in range(n_boostrap):

            # random resampling w/ replacement
            # ind_bs = np.random.randint(
            #     len(reference_power_baseline_wd), size=len(reference_power_baseline_wd))
            ind_bs = np.random.choice(
                len(reference_power_baseline_wd),
                size=len(reference_power_baseline_wd),
                p=baseline_weight,
            )
            reference_power_binned_baseline = reference_power_baseline_wd[ind_bs]
            test_power_binned_baseline = test_power_baseline_wd[ind_bs]

            # compute the energy ratio
            ratio_base_bs[i_bs], _ = energy_ratio(
                reference_power_binned_baseline, test_power_binned_baseline
            )

        # Get the confidence bounds
        percentiles = _get_confidence_bounds(confidence)

        # Compute the central over from the bootstrap runs
        ratio_array_base[i] = np.mean(ratio_base_bs)

        (
            lower_ratio_array_base[i],
            upper_ratio_array_base[i],
        ) = _calculate_lower_and_upper_bound(
            ratio_base_bs,
            percentiles,
            central_estimate=ratio_array_base[i],
            method="simple_percentile",
        )

    return (
        ratio_array_base,
        lower_ratio_array_base,
        upper_ratio_array_base,
        counts_ratio_array_base,
    )


def plot_energy_ratio(
    reference_power_baseline,
    test_power_baseline,
    wind_speed_array_baseline,
    wind_direction_array_baseline,
    wind_direction_bins,
    confidence=95,
    n_boostrap=None,
    wind_direction_bin_p_overlap=None,
    ax=None,
    base_color="b",
    label_array=None,
    label_pchange=None,
    plot_simple=False,
    plot_ratio_scatter=False,
    marker_scale=1.0,
    show_count=True,
    hide_controlled_case=False,
):
    """
    Plot the single energy ratio.

    Function mainly acts as a wrapper to call
    calculate_balanced_energy_ratio and plot the results.

    Args:
        reference_power_baseline (np.array): Array of power
            of reference turbine in baseline conditions.
        test_power_baseline (np.array): Array of power of
            test turbine in baseline conditions.
        wind_speed_array_baseline (np.array): Array of wind
            speeds in baseline conditions.
        wind_direction_array_baseline (np.array): Array of
            wind directions in baseline case.
        wind_direction_bins (np.array): Wind directions bins.
        confidence (int, optional): Confidence level to use.
            Defaults to 95.
        n_boostrap (int, optional): Number of bootstaps, if
            none, _calculate_bootstrap_iterations is called.  Defaults
            to None.
        wind_direction_bin_p_overlap (np.array, optional):
            Percentage overlap between wind direction bin. Defaults to
            None.
        axarr ([axes], optional): list of axes to plot to.
            Defaults to None.
        base_color (str, optional): Color of baseline in
            plots. Defaults to 'b'.
        label_array ([str], optional): List of labels to
            apply Defaults to None.
        label_pchange ([type], optional): Label for
            percentage change. Defaults to None.
        plot_simple (bool, optional): Plot only the ratio, no
            confidence. Defaults to False.
        plot_ratio_scatter (bool, optional): Include scatter
            plot of values, sized to indicate counts. Defaults to False.
        marker_scale ([type], optional): Marker scale.
            Defaults to 1.
        show_count (bool, optional): Show the counts as scatter plot
        hide_controlled_case (bool, optional): Option to hide the control case from plots, for demonstration

    """

    if ax is None:
        fig, ax = plt.subplots(1, 1, sharex=True)

    if label_array is None:
        label_array = ["Baseline"]

    if label_pchange is None:
        label_pchange = "Energy Gain"

    (
        ratio_array_base,
        lower_ratio_array_base,
        upper_ratio_array_base,
        counts_ratio_array_base,
    ) = calculate_balanced_energy_ratio(
        reference_power_baseline,
        test_power_baseline,
        wind_direction_array_baseline,
        wind_direction_bins,
        confidence=95,
        n_boostrap=None,
        wind_direction_bin_p_overlap=wind_direction_bin_p_overlap,
    )

    if plot_simple:
        ax.plot(
            wind_direction_bins,
            ratio_array_base,
            label=label_array[0],
            color=base_color,
            ls="--",
        )
        ax.axhline(1, color="k")
        ax.set_ylabel("Energy Ratio (-)")

    else:

        ax.plot(
            wind_direction_bins,
            ratio_array_base,
            label=label_array[0],
            color=base_color,
            ls="-",
            marker=".",
        )
        ax.fill_between(
            wind_direction_bins,
            lower_ratio_array_base,
            upper_ratio_array_base,
            alpha=0.3,
            color=base_color,
            label="_nolegend_",
        )
        if show_count:
            ax.scatter(
                wind_direction_bins,
                ratio_array_base,
                s=counts_ratio_array_base,
                label="_nolegend_",
                color=base_color,
                marker="o",
                alpha=0.2,
            )
        ax.axhline(1, color="k")
        ax.set_ylabel("Energy Ratio (-)")

        ax.grid(True)
        ax.set_xlabel("Wind Direction (Deg)")

# Copyright 2021 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # See https://floris.readthedocs.io for documentation Compute gaussian function, from     https://stackoverflow.com/questions/14873203/plotting-of-1-dimensional-gaussian-distribution-function.      Args:         x (np.array): Input variable to Gaussian.         mu (float): Mean value.         sig (float): Standard deviation.      Returns:         np.array: The resulting Gaussian distribution. Convert an input series to NumPy array. Currently, this function     checks if an object has a `values` attribute and returns that if it does.     Otherwise, it returns the given input if that input is a `np.ndarray`.      Args:         series (pd.Series): Series to convert.      Returns:         np.array: Converted Series. Calculate number of bootstrap iterations given length.     # TODO: What are `bootstrap iterations`?      Args:         n (int): Number of points.      Returns:         int: Number of bootstrap iterations. Given resultant bootstrap output array, compute lower and upper bound     of confidence interval.      Args:         bootstrap_array (np.array): array of bootrapped results             percentiles (np.array): percentile values             central_estimate (float, optional): if not using simple percentile,             need to provide the central estimated result. Defaults to None.         method (str, optional): method for computing bounds. Defaults to             'simple_percentile'.      Returns:         float, float:              -   lower ci bound             -   upper ci bound Get the upper and lower confidence bounds given a desired confidence level.      Args:         confidence (float): [description]         # TODO: ^^      Returns:         float, float:              -   upper confidence bound             -   lower confidence bound Compute the balanced energy ratio for a single binned wind direction      Single version, not comparing controller on controller off      This function is called to compute a single balanced     energy ratio calculation for a particular wind direction bin.  Note     the reference turbine should not be the turbine implementing     control, but should be an unaffected nearby turbine, or a synthetic     power estimate from a measurement.  See :cite:`ers-fleming2019initial` and     :cite:`ers-fleming2019continued` for more information.      References:         .. bibliography:: /source/zrefs.bib             :style: unsrt             :filter: docname in docnames             :keyprefix: ers-      Args:         ref_pow_base (np.array): Array of baseline reference turbine             power.         test_pow_base (np.array): Array of baseline test turbine power.       Returns:         tuple: tuple containing:              -   **ratio_base** (*float*): Baseline energy ratio.             -   **counts_base** (*float*): Number of points in baseline. # Weighted sums # Get the counts Calculate a balanced energy ratio for each wind direction bin.      Single version, not divided into baseline and controlled      Calculate a balanced energy ratio for each wind direction bin.  A     reference and test turbine are provided for the ratio, as well as     wind speed and wind directions. These data are further divided into     baseline and controlled conditions.  The balanced energy ratio     function is called and used to ensure a similar distribution of     wind speeds is used in the computation, per wind direction bin, for     baseline and controlled results.  Resulting arrays, including upper     and lower uncertainty bounds computed through bootstrapping, are     returned.  Note the reference turbine should not be the turbine     implementing control, but should be an unaffected nearby turbine,     or a synthetic power estimate from a measurement  See     :cite:`ers-fleming2019initial` and     :cite:`ers-fleming2019continued` for more information.      References:         .. bibliography:: /source/zrefs.bib             :style: unsrt             :filter: docname in docnames             :keyprefix: ers-      Args:         reference_power_baseline (np.array): Array of power of             reference turbine in baseline conditions.         test_power_baseline (np.array): Array of power of test turbine             in baseline conditions.         wind_speed_array_baseline (np.array): Array of wind speeds in             baseline conditions.         wind_direction_array_baseline (np.array): Array of wind             directions in baseline case.         wind_direction_bins (np.array): Wind directions bins.         confidence (int, optional): Confidence level to use.  Defaults             to 95.         n_boostrap (int, optional): Number of bootstaps, if none,             _calculate_bootstrap_iterations is called.  Defaults to             None.         wind_direction_bin_p_overlap (np.array, optional): Percentage             overlap between wind direction bin. Defaults to None.      Returns:         tuple: tuple containing:              **ratio_array_base** (*np.array*): Baseline energy ratio at each wind direction bin.             **lower_ratio_array_base** (*np.array*): Lower confidence bound of baseline energy ratio at each wind direction bin.             **upper_ratio_array_base** (*np.array*): Upper confidence bound of baseline energy ratio at each wind direction bin.             **counts_ratio_array_base** (*np.array*): Counts per wind direction bin in baseline. # Ensure that input arrays are np.ndarray # Handle no overlap specificed (assume non-overlap) # Compute binning radius (is this right?) # compute the energy ratio # ratio_array_base[i], counts_ratio_array_base[i] = energy_ratio(reference_power_baseline_wd, test_power_baseline_wd) # Get the bounds through boot strapping # determine the number of bootstrap iterations if not given # random resampling w/ replacement # ind_bs = np.random.randint( #     len(reference_power_baseline_wd), size=len(reference_power_baseline_wd)) # compute the energy ratio # Get the confidence bounds # Compute the central over from the bootstrap runs Plot the single energy ratio.      Function mainly acts as a wrapper to call     calculate_balanced_energy_ratio and plot the results.      Args:         reference_power_baseline (np.array): Array of power             of reference turbine in baseline conditions.         test_power_baseline (np.array): Array of power of             test turbine in baseline conditions.         wind_speed_array_baseline (np.array): Array of wind             speeds in baseline conditions.         wind_direction_array_baseline (np.array): Array of             wind directions in baseline case.         wind_direction_bins (np.array): Wind directions bins.         confidence (int, optional): Confidence level to use.             Defaults to 95.         n_boostrap (int, optional): Number of bootstaps, if             none, _calculate_bootstrap_iterations is called.  Defaults             to None.         wind_direction_bin_p_overlap (np.array, optional):             Percentage overlap between wind direction bin. Defaults to             None.         axarr ([axes], optional): list of axes to plot to.             Defaults to None.         base_color (str, optional): Color of baseline in             plots. Defaults to 'b'.         label_array ([str], optional): List of labels to             apply Defaults to None.         label_pchange ([type], optional): Label for             percentage change. Defaults to None.         plot_simple (bool, optional): Plot only the ratio, no             confidence. Defaults to False.         plot_ratio_scatter (bool, optional): Include scatter             plot of values, sized to indicate counts. Defaults to False.         marker_scale ([type], optional): Marker scale.             Defaults to 1.         show_count (bool, optional): Show the counts as scatter plot         hide_controlled_case (bool, optional): Option to hide the control case from plots, for demonstration