# -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# 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.
"""Outliers detection and remove."""
import numpy as np


def is_outlier(x, p25, p75):
    """Check if value is an outlier."""
    lower = p25 - 1.5 * (p75 - p25)
    upper = p75 + 1.5 * (p75 - p25)
    return x <= lower or x >= upper


def remove_outlier(x, p_bottom: int = 25, p_top: int = 75):
    """Remove outlier from x."""
    p_bottom = np.percentile(x, p_bottom)
    p_top = np.percentile(x, p_top)

    indices_of_outliers = []
    for ind, value in enumerate(x):
        if is_outlier(value, p_bottom, p_top):
            indices_of_outliers.append(ind)

    x[indices_of_outliers] = 0.0

    # replace by mean f0.
    x[indices_of_outliers] = np.max(x)
    return x