from abc import ABC, abstractmethod

import numpy as np
import random

import librosa
import pyroomacoustics as pra


class DataAugmenter(ABC):
    """
    Abstract class for data augmentations
    """

    @abstractmethod
    def augment(self, recording):
        pass


class DanSpeechAugmenter(DataAugmenter):
    """
    Class that implements the DanSpeech Augmentation scheme
    """

    def __init__(self, sampling_rate, augmentation_list=None):
        self.sampling_rate = sampling_rate

        # Allow user to specify a list of ordered augmentations
        if not augmentation_list:
            # Ordered list of augmentation policies
            self.augmentations_list = [speed_perturb, room_reverb, volume_perturb, add_wn, shift_perturb]

    def augment(self, recording):

        # toDo: Make augment probability depend on length of the augmentation list
        augment_gate = random.uniform(0, 1)

        # there are 30 possible unique permutations of the augmentation list, so we choose a random augmentation
        # with chances 1-1/30.
        if augment_gate < 0.96666666666:
            scheme = self.choose_augmentation_scheme()
            # Apply the chosen augmentations
            for augmentation_function in scheme:
                recording = augmentation_function(recording, self.sampling_rate)

        return recording

    def choose_augmentation_scheme(self):
        """
        Chooses a valid danspeech augmentation based on the ordered list of augmentations

        :param list_of_augmentations: Ordered list of augmentation functions
        :return: A valid danspeech augmentation scheme
        """
        augmentations_to_apply = []
        # pick a random number between 0 and len(list), the number must be picked uniformly and is the index of
        # the first augmentation to run.
        index_zero = random.randint(0, len(self.augmentations_list) - 1)
        augmentations_to_apply.append(self.augmentations_list[index_zero])

        # "slice" the list index_zero and select a random number of additional augmentations to be applied in range
        # {0;len(slice)}.
        num_augmentations = random.randint(0, len(self.augmentations_list[index_zero:]) - 1)
        index_infected = random.sample(range(index_zero + 1, len(self.augmentations_list)), num_augmentations)
        index_infected.sort()
        for index in index_infected:
            augmentations_to_apply.append(self.augmentations_list[index])

        return augmentations_to_apply


def speed_perturb(recording, sampling_rate, *args):
    """
    Select up/down-sampling randomly between 90% and 110% of original sample rate

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    new_sample_rate = sampling_rate * random.choice([0.9, 1.1])
    return librosa.core.resample(recording, sampling_rate, new_sample_rate)


def shift_perturb(recording, sampling_rate, *args):
    """
    Shifts the audio recording randomly in time.

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    shift_ms = np.random.randint(low=-50, high=50)
    shift_samples = int(shift_ms * sampling_rate / 1000)

    if shift_samples > 0:
        # time advance
        recording[:-shift_samples] = recording[shift_samples:]
        recording[-shift_samples:] = 0
    elif shift_samples < 0:
        # time delay
        recording[-shift_samples:] = recording[:shift_samples]
        recording[:-shift_samples] = 0
    return recording


def room_reverb(recording, sampling_rate, *args):
    """
    Perturb signal with room reverberations in a randomly generated shoebox room.

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    # generate random room specifications, including absorption factor.
    alpha = random.uniform(0, 0.4)

    room_length = np.random.uniform(2, 12)
    room_width = np.random.uniform(2, 6)
    room_height = 3.0 + np.random.uniform(-0.5, 0.5)

    microphone_x = np.random.uniform(0.5, room_width - 0.5)
    microphone_y = np.random.uniform(0.5, room_length - 0.5)
    microphone_height = 1.50 + np.random.uniform(-0.5, 0.5)

    r = 0.5 * np.sqrt(np.random.uniform(0, 1))
    theta = np.random.uniform(0, 1) * 2 * np.pi
    source_x = microphone_x + r * np.cos(theta)
    source_y = microphone_y + r * np.sin(theta)
    source_height = 1.80 + np.random.uniform(-0.25, 0.25)

    # create the room based on the specifications simulated above
    room = pra.ShoeBox([room_width, room_length, room_height],
                       fs=sampling_rate,
                       max_order=17,
                       absorption=alpha)

    # add recording at source, and a random microphone to room
    room.add_source([source_x, source_y, source_height], signal=recording)
    R = np.array([[microphone_x], [microphone_y], [microphone_height]])
    room.add_microphone_array(pra.MicrophoneArray(R, room.fs))
    room.image_source_model(use_libroom=True)
    room.simulate()

    # return the reverberation convolved signal
    return room.mic_array.signals[0, :]


def volume_perturb(recording, *args):
    """
    Select a gain in decibels randomly and add to recording

    :param recording:
    :return: Augmented recording
    """
    gain = np.random.randint(low=5, high=30)
    recording *= 10. ** (gain / 20.)
    return recording


def add_wn(recording, *args):
    """
    Add wn white noise with random variance to recording

    :param recording:
    :return: Augmented recording
    """
    # Normalize recording before adding wn
    mean = np.mean(recording)
    std = np.std(recording)

    recording = (recording - mean) / std

    variance = np.random.uniform(low=0.5, high=1.8)
    noise = np.random.normal(0, random.uniform(0, variance), len(recording))

    # append noise to signal
    return recording + noise

from abc import ABC, abstractmethod

import numpy as np
import random

import librosa
import pyroomacoustics as pra


class DataAugmenter(ABC):
    """
    Abstract class for data augmentations
    """

    @abstractmethod
    def augment(self, recording):
        pass


class DanSpeechAugmenter(DataAugmenter):
    """
    Class that implements the DanSpeech Augmentation scheme
    """

    def __init__(self, sampling_rate, augmentation_list=None):
        self.sampling_rate = sampling_rate

        # Allow user to specify a list of ordered augmentations
        if not augmentation_list:
            # Ordered list of augmentation policies
            self.augmentations_list = [speed_perturb, room_reverb, volume_perturb, add_wn, shift_perturb]

    def augment(self, recording):

        # toDo: Make augment probability depend on length of the augmentation list
        augment_gate = random.uniform(0, 1)

        # there are 30 possible unique permutations of the augmentation list, so we choose a random augmentation
        # with chances 1-1/30.
        if augment_gate < 0.96666666666:
            scheme = self.choose_augmentation_scheme()
            # Apply the chosen augmentations
            for augmentation_function in scheme:
                recording = augmentation_function(recording, self.sampling_rate)

        return recording

    def choose_augmentation_scheme(self):
        """
        Chooses a valid danspeech augmentation based on the ordered list of augmentations

        :param list_of_augmentations: Ordered list of augmentation functions
        :return: A valid danspeech augmentation scheme
        """
        augmentations_to_apply = []
        # pick a random number between 0 and len(list), the number must be picked uniformly and is the index of
        # the first augmentation to run.
        index_zero = random.randint(0, len(self.augmentations_list) - 1)
        augmentations_to_apply.append(self.augmentations_list[index_zero])

        # "slice" the list index_zero and select a random number of additional augmentations to be applied in range
        # {0;len(slice)}.
        num_augmentations = random.randint(0, len(self.augmentations_list[index_zero:]) - 1)
        index_infected = random.sample(range(index_zero + 1, len(self.augmentations_list)), num_augmentations)
        index_infected.sort()
        for index in index_infected:
            augmentations_to_apply.append(self.augmentations_list[index])

        return augmentations_to_apply


def speed_perturb(recording, sampling_rate, *args):
    """
    Select up/down-sampling randomly between 90% and 110% of original sample rate

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    new_sample_rate = sampling_rate * random.choice([0.9, 1.1])
    return librosa.core.resample(recording, sampling_rate, new_sample_rate)


def shift_perturb(recording, sampling_rate, *args):
    """
    Shifts the audio recording randomly in time.

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    shift_ms = np.random.randint(low=-50, high=50)
    shift_samples = int(shift_ms * sampling_rate / 1000)

    if shift_samples > 0:
        # time advance
        recording[:-shift_samples] = recording[shift_samples:]
        recording[-shift_samples:] = 0
    elif shift_samples < 0:
        # time delay
        recording[-shift_samples:] = recording[:shift_samples]
        recording[:-shift_samples] = 0
    return recording


def room_reverb(recording, sampling_rate, *args):
    """
    Perturb signal with room reverberations in a randomly generated shoebox room.

    :param recording: Recording to be augmented
    :param sampling_rate: Sampling rate of the recording
    :return: Augmented recording
    """

    # generate random room specifications, including absorption factor.
    alpha = random.uniform(0, 0.4)

    room_length = np.random.uniform(2, 12)
    room_width = np.random.uniform(2, 6)
    room_height = 3.0 + np.random.uniform(-0.5, 0.5)

    microphone_x = np.random.uniform(0.5, room_width - 0.5)
    microphone_y = np.random.uniform(0.5, room_length - 0.5)
    microphone_height = 1.50 + np.random.uniform(-0.5, 0.5)

    r = 0.5 * np.sqrt(np.random.uniform(0, 1))
    theta = np.random.uniform(0, 1) * 2 * np.pi
    source_x = microphone_x + r * np.cos(theta)
    source_y = microphone_y + r * np.sin(theta)
    source_height = 1.80 + np.random.uniform(-0.25, 0.25)

    # create the room based on the specifications simulated above
    room = pra.ShoeBox([room_width, room_length, room_height],
                       fs=sampling_rate,
                       max_order=17,
                       absorption=alpha)

    # add recording at source, and a random microphone to room
    room.add_source([source_x, source_y, source_height], signal=recording)
    R = np.array([[microphone_x], [microphone_y], [microphone_height]])
    room.add_microphone_array(pra.MicrophoneArray(R, room.fs))
    room.image_source_model(use_libroom=True)
    room.simulate()

    # return the reverberation convolved signal
    return room.mic_array.signals[0, :]


def volume_perturb(recording, *args):
    """
    Select a gain in decibels randomly and add to recording

    :param recording:
    :return: Augmented recording
    """
    gain = np.random.randint(low=5, high=30)
    recording *= 10. ** (gain / 20.)
    return recording


def add_wn(recording, *args):
    """
    Add wn white noise with random variance to recording

    :param recording:
    :return: Augmented recording
    """
    # Normalize recording before adding wn
    mean = np.mean(recording)
    std = np.std(recording)

    recording = (recording - mean) / std

    variance = np.random.uniform(low=0.5, high=1.8)
    noise = np.random.normal(0, random.uniform(0, variance), len(recording))

    # append noise to signal
    return recording + noise

Abstract class for data augmentations Class that implements the DanSpeech Augmentation scheme # Allow user to specify a list of ordered augmentations # Ordered list of augmentation policies # toDo: Make augment probability depend on length of the augmentation list # there are 30 possible unique permutations of the augmentation list, so we choose a random augmentation # with chances 1-1/30. # Apply the chosen augmentations Chooses a valid danspeech augmentation based on the ordered list of augmentations          :param list_of_augmentations: Ordered list of augmentation functions         :return: A valid danspeech augmentation scheme # pick a random number between 0 and len(list), the number must be picked uniformly and is the index of # the first augmentation to run. # "slice" the list index_zero and select a random number of additional augmentations to be applied in range # {0;len(slice)}. Select up/down-sampling randomly between 90% and 110% of original sample rate      :param recording: Recording to be augmented     :param sampling_rate: Sampling rate of the recording     :return: Augmented recording Shifts the audio recording randomly in time.      :param recording: Recording to be augmented     :param sampling_rate: Sampling rate of the recording     :return: Augmented recording # time advance # time delay Perturb signal with room reverberations in a randomly generated shoebox room.      :param recording: Recording to be augmented     :param sampling_rate: Sampling rate of the recording     :return: Augmented recording # generate random room specifications, including absorption factor. # create the room based on the specifications simulated above # add recording at source, and a random microphone to room # return the reverberation convolved signal Select a gain in decibels randomly and add to recording      :param recording:     :return: Augmented recording Add wn white noise with random variance to recording      :param recording:     :return: Augmented recording # Normalize recording before adding wn # append noise to signal