"""
file_name: hnr
project_name: DBM
created: 2020-20-07
"""

import pandas as pd
import numpy as np
import os
import glob
import parselmouth
import librosa
from os.path import join
import logging

from dbm_lib.dbm_features.raw_features.util import util as ut

logging.basicConfig(level=logging.INFO)
logger=logging.getLogger()

hnr_dir = 'audio/harmonic_noise'
csv_ext = '_hnr_frame.csv'
error_txt = 'error: length less than 0.064'

def hnr_ratio(filepath):
    """
    Using parselmouth library fetching harmonic noise ratio ratio
    Args:
        path: (.wav) audio file location
    Returns:
        (list) list of hnr ratio for each voice frame, min,max and mean hnr
    """
    sound = parselmouth.Sound(filepath)
    harmonicity = sound.to_harmonicity_ac(time_step=.001)
    
    hnr_all_frames = harmonicity.values#[harmonicity.values != -200] nan it (****)
    hnr_all_frames = np.where(hnr_all_frames==-200, np.NaN, hnr_all_frames)
    return hnr_all_frames.transpose()

def calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config):
    """
    Preparing harmonic noise matrix
    Args:
        audio_file: (.wav) parsed audio file
        out_loc: (str) Output directory for csv's
    """
    
    hnr_all_frames = hnr_ratio(audio_file)
    df_hnr = pd.DataFrame(hnr_all_frames, columns=[r_config.aco_hnr])
    
    df_hnr['Frames'] = df_hnr.index
    df_hnr['dbm_master_url'] = video_uri
    df_hnr[r_config.err_reason] = 'Pass'# will replace with threshold in future release
    
    logger.info('Saving Output file {} '.format(out_loc))
    ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)
    
def empty_hnr(video_uri, out_loc, fl_name, r_config):
    """
    Preparing empty HNR matrix if something fails
    """
    cols = ['Frames', r_config.aco_hnr, r_config.err_reason]
    out_val = [[np.nan, np.nan, error_txt]]
    df_hnr = pd.DataFrame(out_val, columns = cols)
    df_hnr['dbm_master_url'] = video_uri
    
    logger.info('Saving Output file {} '.format(out_loc))
    ut.save_output(df_hnr, out_loc, fl_name, hnr_dir, csv_ext)

def run_hnr(video_uri, out_dir, r_config):
    """
    Processing all patient's for fetching harmonic noise ratio
    -------------------
    -------------------
    Args:
        video_uri: video path; r_config: raw variable config object
        out_dir: (str) Output directory for processed output
    """
    input_loc, out_loc, fl_name = ut.filter_path(video_uri, out_dir)
    aud_filter = glob.glob(join(input_loc, fl_name + '.wav'))
    if len(aud_filter)>0:

        audio_file = aud_filter[0]
        aud_dur = librosa.get_duration(filename=audio_file)

        if float(aud_dur) < 0.064:
            logger.info('Output file {} size is less than 0.064sec'.format(audio_file))
            
            empty_hnr(video_uri, out_loc, fl_name, r_config)
            return

        calc_hnr(video_uri, audio_file, out_loc, fl_name, r_config)