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

import pandas as pd
import os
import glob
import parselmouth
import librosa
import numpy as np
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()

mfcc_dir = 'audio/mfcc'
csv_ext = '_mfcc.csv'
error_txt = 'error: length less than 0.064'

def empty_mfcc(video_uri, out_loc, fl_name, r_config):
    
    """
    Preparing empty empty_mfcc matrix if something fails
    """
    cols = ['Frames', r_config.aco_mfcc1, r_config.aco_mfcc2, r_config.aco_mfcc3, r_config.aco_mfcc4, r_config.aco_mfcc5,
            r_config.aco_mfcc6, r_config.aco_mfcc7, r_config.aco_mfcc8, r_config.aco_mfcc9, r_config.aco_mfcc10, 
            r_config.aco_mfcc11, r_config.aco_mfcc12, r_config.err_reason]
    out_val = [[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 
                error_txt]]
    df_mfcc = pd.DataFrame(out_val, columns = cols)
    df_mfcc['dbm_master_url'] = video_uri
    
    logger.info('Saving Output file {} '.format(out_loc))
    ut.save_output(df_mfcc, out_loc, fl_name, mfcc_dir, csv_ext)

def audio_mfcc(path):
    """
    Using parselmouth library fetching mfccs
    Args:
        path: (.wav) audio file location
    Returns:
        (list) list of mfccs for each voice frame
    """
    sound = parselmouth.Sound(path)
    mfcc_object = sound.to_mfcc(time_step=.001,number_of_coefficients=12)
    mfccs = mfcc_object.to_array()
    mfccs = np.delete(mfccs, (0), axis=0)
    return mfccs
    
def calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config):
    """
    Preparing mfcc matrix
    Args:
        audio_file: (.wav) parsed audio file
        out_loc: output location to save csv
        fl_name: (str) name of audio file
        r_config: config.config_raw_feature.pyConfigFeatureNmReader object
    """
    dict_ = {}
    mfccs = audio_mfcc(audio_file)
    
    for i in range(1,13):
        conf_str = r_config.base_raw['raw_feature']
        dict_[conf_str['aco_mfcc' + str(i)]] = mfccs[i-1, :]
        
    df = pd.DataFrame(dict_)
    df['Frames'] = df.index
    
    df[r_config.err_reason] = 'Pass'# may replace based on threshold in future release
    df['dbm_master_url'] = video_uri
    
    ut.save_output(df, out_loc, fl_name, mfcc_dir, csv_ext)
    
def run_mfcc(video_uri, out_dir, r_config):
    """
    Processing all patients to fetch mfccs
    
    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_mfcc(video_uri, out_loc, fl_name, r_config)
            return
            
        calc_mfcc(video_uri, audio_file, out_loc, fl_name, r_config)