"""Lambda handler for defining audio track similarity."""

import os
import time
import uuid

import ffmpy
import soundfile as sf
import numpy as np


DEFAULT_TMP_DIR = os.environ.get('DEFAULT_TMP_DIR', '/Users/maximov/Desktop/tmp')  # without ending backslash
FFMPEG_PATH = os.environ.get('FFMPEG', '/usr/local/bin/ffmpeg')
PCM_S16LE_CODEC = '-acodec pcm_s16le -ar 16000 -ac 1'
DELTA = 0.000000000000001

assert DEFAULT_TMP_DIR


def time_usage(func):
    """Print elapsed execution time."""
    def wrapper(*args, **kwargs):
        beg_ts = time.time()
        retval = func(*args, **kwargs)
        end_ts = time.time()
        print("elapsed time: %f" % (end_ts - beg_ts))
        return retval
    return wrapper


def transcode_to_pcm(input_path):
    """Transcode input file to pcm encoded audio file by ffmpeg.

    Args:
        input_path (str): input file path

    Result:
        str: output wav file path. File has unique filename.
    """
    output_path = '{}/{}.wav'.format(DEFAULT_TMP_DIR, uuid.uuid4())
    ff = ffmpy.FFmpeg(
        executable=FFMPEG_PATH,  # add path to executable ffmpeg
        inputs={input_path: None},
        outputs={output_path: PCM_S16LE_CODEC}
    )
    ff.run()
    return output_path


def get_fft(file_path):
    """Calculate Fast Fourier transform.

    Args:
        file_path (str): file path to standardized pcm.

    Result:
        np.array: Normalized frames.
    """
    normalized_amplitudes, samplerate = sf.read(file_path, always_2d=False)
    number_of_subarrays = int(normalized_amplitudes.size / 1000)
    frames = np.array_split(normalized_amplitudes, number_of_subarrays)
    transcoded_fft = []
    for f in frames:
        transcoded_frame = np.fft.fft(f)
        result = [np.complex(DELTA, DELTA) if f == np.complex(0, 0) else f
                  for f in transcoded_frame]
        transcoded_fft.append(result)
    return transcoded_fft


@time_usage
def handler(event, context):
    """Handler method."""
    transcoded_fft = get_fft(transcode_to_pcm(event['output_path']))
    source_fft = get_fft(transcode_to_pcm(event['source_path']))
    result = []
    for s, t in zip(transcoded_fft, source_fft):
        result.append(np.corrcoef(s, t)[1, 0])

    return {event['output_path']: np.std(result)}


if __name__ == '__main__':
    events = [
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-flac.flac',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-mp3.mp3',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-m4.m4a',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-wav.wav',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-1-muted.wav',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-5-muted.wav',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-001-muted.wav',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        },
        {
            'output_path': '/Users/maximov/Desktop/tmp/b-005-muted.wav',
            'source_path': '/Users/maximov/Desktop/tmp/b.wav'
        }
    ]
    result_correlation = []
    for e in events:
        result_correlation.append(handler(e, None))

    for item in result_correlation:
        print(item)