# Source code for ipfml.utils

    """
    Utils functions of ipfml package (normalization, integral...)
    """

    # main imports
    import numpy as np
    import math
    import sys

    # computation imports
    from scipy.integrate import simps

    def normalize_arr(arr):
        """Normalize data of 1D array shape

        Args:
            arr: array data of 1D shape

        Returns:
            Normalized 1D array

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(5)
        >>> arr_normalized = utils.normalize_arr(arr)
        >>> arr_normalized[1]
        0.1
        """

        output_arr = []
        sum_value = sum(arr)

        for v in arr:
            # add of epsilon value in order to avoid Zero Division
            output_arr.append(v / sum_value)

        return output_arr

    def normalize_arr_with_range(arr, min, max):
        '''Normalize data of 1D array shape

        Args:
            arr: array data of 1D shape

        Returns:
            Normalized 1D Numpy array

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(11)
        >>> arr_normalized = utils.normalize_arr_with_range(arr, 0, 20)
        >>> arr_normalized[1]
        0.05
        '''

        output_arr = []

        for v in arr:
            # add of epsilon value in order to avoid Zero Division
            output_arr.append((v - min) / (max - min + sys.float_info.epsilon))

        return output_arr

    def normalize_2D_arr(arr):
        """Return array normalize from its min and max values

        Args:
            arr: 2D Numpy array

        Returns:
            Normalized 2D Numpy array

        Example:

        >>> from PIL import Image
        >>> from ipfml import utils
        >>> from ipfml.processing import transform
        >>> img = Image.open('./images/test_img.png')
        >>> img_mscn = transform.rgb_to_mscn(img)
        >>> img_normalized = utils.normalize_2D_arr(img_mscn)
        >>> img_normalized.shape
        (200, 200)
        """

        # getting min and max value from 2D array
        max_value = arr.max(axis=1).max()
        min_value = arr.min(axis=1).min()

        # normalize each row
        output_array = []
        _, height = arr.shape

        for row_index in range(0, height):
            values = arr[row_index, :]
            output_array.append(
                normalize_arr_with_range(values, min_value, max_value))

        return np.asarray(output_array)

    def integral_area_trapz(y_values, dx):
        """Returns area under curves from provided data points using Trapezium rule

        Args:
            y_values: y values of curve
            dx: number of unit for x axis

        Returns:
            Area under curves obtained from these points

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
        >>> area = utils.integral_area_trapz(y_values, dx=5)
        >>> area
        452.5
        """

        return np.trapz(y_values, dx=dx)

    def integral_area_simps(y_values, dx):
        """Returns area under curves from provided data points using Simpsons rule

        Args:
            y_values: y values of curve
            dx: number of unit for x axis

        Returns:
            Area under curves obtained from these points

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> y_values = np.array([5, 20, 4, 18, 19, 18, 7, 4])
        >>> area = utils.integral_area_simps(y_values, dx=5)
        >>> area
        460.0
        """

        return simps(y_values, dx=dx)

    def get_indices_of_highest_values(arr, n):
        """Returns indices of n highest values from list or 1D numpy array

        Args:
            arr: List of numpy array
            n: number of highest elements wanted

        Returns:
            `n` indices of highest values

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(10)
        >>> indices = utils.get_indices_of_highest_values(arr, 2)
        >>> indices
        array([9, 8])
        """
        return np.array(arr).argsort()[-n:][::-1]

    def get_indices_of_lowest_values(arr, n):
        """Returns indices of n highest values from list or 1D numpy array

        Args:
            arr: List of numpy array
            n: number of highest elements wanted

        Returns:
            `n` indices of highest values

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(10)
        >>> indices = utils.get_indices_of_lowest_values(arr, 2)
        >>> indices
        array([0, 1])
        """
        return np.array(arr).argsort()[::-1][-n:][::-1]

    def get_entropy(arr):
        """Returns the computed entropy from arr

        Args:
            arr: numpy array

        Returns:
            entropy score computed

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(10)
        >>> entropy = utils.get_entropy(arr)
        >>> int(entropy)
        0
        """

        arr = np.array(arr)
        eigen_values = []
        sum_eigen_values = (arr * arr).sum()

        for val in arr:
            eigen_values.append(val * val)

        v = []

        for val in eigen_values:
            v.append(val / sum_eigen_values)

        entropy = 0

        for val in v:
            if val > 0:
                entropy += val * math.log(val)

        entropy *= -1

        entropy /= math.log(len(v))

        return entropy

    def get_entropy_without_i(arr, i):
        """Returns the computed entropy from arr without contribution of i

        Args:
            arr: numpy array
            i: column index

        Returns:
            entropy score computed

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(10)
        >>> entropy = utils.get_entropy_without_i(arr, 3)
        >>> int(entropy)
        0
        """

        arr = np.array([v for index, v in enumerate(arr) if index != i])

        return get_entropy(arr)

    def get_entropy_contribution_of_i(arr, i):
        """Returns the entropy contribution i column

        Args:
            arr: numpy array
            i: column index

        Returns:
            entropy contribution score computed

        Example:

        >>> from ipfml import utils
        >>> import numpy as np
        >>> arr = np.arange(10)
        >>> entropy = utils.get_entropy_contribution_of_i(arr, 3)
        >>> int(entropy)
        0
        """

        return get_entropy(arr) - get_entropy_without_i(arr, i)