# -*- coding: utf-8 -*-
"""MedleyDB pitch Dataset Loader

MedleyDB is a dataset of annotated, royalty-free multitrack recordings.
MedleyDB was curated primarily to support research on melody extraction,
addressing important shortcomings of existing collections. For each song
we provide melody f0 annotations as well as instrument activations for
evaluating automatic instrument recognition.

For more details, please visit: https://medleydb.weebly.com

"""

import csv
import json
import librosa
import logging
import numpy as np
import os

from mirdata import download_utils
from mirdata import jams_utils
from mirdata import core
from mirdata import utils

BIBTEX = """@inproceedings{bittner2014medleydb,
    Author = {Bittner, Rachel M and Salamon, Justin and Tierney, Mike and Mauch, Matthias and Cannam, Chris and Bello, Juan P},
    Booktitle = {International Society of Music Information Retrieval (ISMIR)},
    Month = {October},
    Title = {Medley{DB}: A Multitrack Dataset for Annotation-Intensive {MIR} Research},
    Year = {2014}
}"""
DOWNLOAD_INFO = """
    To download this dataset, visit:
    https://zenodo.org/record/2620624#.XKZc7hNKh24
    and request access.

    Once downloaded, unzip the file MedleyDB-Pitch.zip
    and copy the result to:
    {data_home}
"""


def _load_metadata(data_home):
    metadata_path = os.path.join(data_home, "medleydb_pitch_metadata.json")

    if not os.path.exists(metadata_path):
        logging.info("Metadata file {} not found.".format(metadata_path))
        return None

    with open(metadata_path, "r") as fhandle:
        metadata = json.load(fhandle)

    metadata["data_home"] = data_home
    return metadata


DATA = utils.LargeData("medleydb_pitch_index.json", _load_metadata)


class Track(core.Track):
    """medleydb_pitch Track class

    Args:
        track_id (str): track id of the track

    Attributes:
        artist (str): artist
        audio_path (str): path to the audio file
        genre (str): genre
        instrument (str): instrument of the track
        pitch_path (str): path to the pitch annotation file
        title (str): title
        track_id (str): track id

    """

    def __init__(self, track_id, data_home):
        if track_id not in DATA.index:
            raise ValueError(
                "{} is not a valid track ID in MedleyDB-Pitch".format(track_id)
            )

        self.track_id = track_id

        self._data_home = data_home
        self._track_paths = DATA.index[track_id]
        self.pitch_path = os.path.join(self._data_home, self._track_paths["pitch"][0])

        metadata = DATA.metadata(data_home)
        if metadata is not None and track_id in metadata:
            self._track_metadata = metadata[track_id]
        else:
            self._track_metadata = {
                "instrument": None,
                "artist": None,
                "title": None,
                "genre": None,
            }

        self.audio_path = os.path.join(self._data_home, self._track_paths["audio"][0])
        self.instrument = self._track_metadata["instrument"]
        self.artist = self._track_metadata["artist"]
        self.title = self._track_metadata["title"]
        self.genre = self._track_metadata["genre"]

    @utils.cached_property
    def pitch(self):
        """F0Data: The human-annotated pitch"""
        return load_pitch(self.pitch_path)

    @property
    def audio(self):
        """(np.ndarray, float): audio signal, sample rate"""
        return load_audio(self.audio_path)

    def to_jams(self):
        """Jams: the track's data in jams format"""
        return jams_utils.jams_converter(
            audio_path=self.audio_path,
            f0_data=[(self.pitch, "annotated pitch")],
            metadata=self._track_metadata,
        )


def load_audio(audio_path):
    """Load a MedleyDB audio file.

    Args:
        audio_path (str): path to audio file

    Returns:
        y (np.ndarray): the mono audio signal
        sr (float): The sample rate of the audio file

    """
    if not os.path.exists(audio_path):
        raise IOError("audio_path {} does not exist".format(audio_path))

    return librosa.load(audio_path, sr=None, mono=True)


def load_pitch(pitch_path):
    if not os.path.exists(pitch_path):
        raise IOError("pitch_path {} does not exist".format(pitch_path))

    times = []
    freqs = []
    with open(pitch_path, "r") as fhandle:
        reader = csv.reader(fhandle, delimiter=",")
        for line in reader:
            times.append(float(line[0]))
            freqs.append(float(line[1]))

    times = np.array(times)
    freqs = np.array(freqs)
    confidence = (freqs > 0).astype(float)
    pitch_data = utils.F0Data(times, freqs, confidence)
    return pitch_data
