#!/usr/bin/env python
# encoding: utf-8
"""
OnsetDetectorLL online onset detection algorithm.

"""

from __future__ import absolute_import, division, print_function

import argparse

from madmom.processors import IOProcessor, io_arguments
from madmom.audio.signal import SignalProcessor
from madmom.features import ActivationsProcessor
from madmom.features.onsets import RNNOnsetProcessor, PeakPickingProcessor


def main():
    """OnsetDetectorLL"""

    # define parser
    p = argparse.ArgumentParser(
        formatter_class=argparse.RawDescriptionHelpFormatter, description='''
    The OnsetDetectorLL Program detects all onsets in an audio file according
    to the algorithm described in:

    "Online Real-time Onset Detection with Recurrent Neural Networks"
    Sebastian Böck, Andreas Arzt, Florian Krebs and Markus Schedl.
    Proceedings of the 15th International Conference on Digital Audio Effects
    (DAFx), 2012.

    The paper includes an error in Section 2.2.1, 2nd paragraph:
    The targets of the training examples have been annotated 1 frame shifted to
    the future, thus the results given in Table 2 are off by 10ms. Given the
    fact that the delayed reporting (as outlined in Section 2.3) is not
    needed, an extra shift of 5ms needs to be added to the mean errors given in
    Table 2.

    This implementation takes care of this error is is modified in this way:
    - a logarithmic frequency spacing is used for the spectrograms instead of
      the Bark scale
    - targets are annotated at the next frame for neural network training
    - post processing reports the onset instantaneously instead of delayed.

    This program can be run in 'single' file mode to process a single audio
    file and write the detected onsets to STDOUT or the given output file.

    $ OnsetDetectorLL single INFILE [-o OUTFILE]

    If multiple audio files should be processed, the program can also be run
    in 'batch' mode to save the detected onsets to files with the given suffix.

    $ OnsetDetectorLL batch [-o OUTPUT_DIR] [-s OUTPUT_SUFFIX] LIST OF FILES

    If no output directory is given, the program writes the files with the
    detected onsets to same location as the audio files.

    The 'pickle' mode can be used to store the used parameters to be able to
    exactly reproduce experiments.

    ''')
    # version
    p.add_argument('--version', action='version',
                   version='OnsetDetectorLL.2013')
    # input/output options
    io_arguments(p, output_suffix='.onsets.txt')
    ActivationsProcessor.add_arguments(p)
    # signal processing arguments
    SignalProcessor.add_arguments(p, norm=False, gain=0)
    # peak picking arguments
    PeakPickingProcessor.add_arguments(p, threshold=0.23)

    # parse arguments
    args = p.parse_args()

    # set immutable defaults
    args.online = True
    args.fps = 100
    args.pre_max = 1. / args.fps
    args.post_max = 0
    args.post_avg = 0

    # print arguments
    if args.verbose:
        print(args)

    # input processor
    if args.load:
        # load the activations from file
        in_processor = ActivationsProcessor(mode='r', **vars(args))
    else:
        # use a RNN to predict the onsets
        in_processor = RNNOnsetProcessor(online=args.online)

    # output processor
    if args.save:
        # save the RNN onset activations to file
        out_processor = ActivationsProcessor(mode='w', **vars(args))
    else:
        # perform peak picking on the onset activations
        peak_picking = PeakPickingProcessor(**vars(args))
        # output handler
        from madmom.utils import write_events as writer
        # sequentially process them
        out_processor = [peak_picking, writer]

    # create an IOProcessor
    processor = IOProcessor(in_processor, out_processor)

    # and call the processing function
    args.func(processor, **vars(args))


if __name__ == '__main__':
    main()