from numpy import NaN
from pandas.core.frame import DataFrame
import pympi.Praat as pr
import pandas as pd
from pathlib import Path
import re

input_dir = "outputs/mfa_align_output"
output_path = "outputs/grid2csv_output"

#creates dataframe with previously dropped entries
original_df = pd.read_csv("inputs/vandam-data/annotations/cha/converted/BN32_010007_0_0.csv")
original_df = original_df.dropna(subset = ['transcription'])
#original_df = original_df[original_df['transcription'].str.match(r"[^a-zA-Z]")]
original_df = (original_df[(pd.isnull(original_df['transcription'])) | (original_df['transcription'].str.match(r"[^a-zA-Z]"))])

speaker_type_dic = {
        "CHI - words" : "CHI",
        "CHI - phones" : "CHI",
        "MOT - words" : "FEM",
        "MOT - phones" : "FEM",
        "FAT - words" : "MAL",
        "FAT - phones" : "MAL",
        "SIS - words" : "OCH",
        "SIS - phones" : "OCH"
        }

#selects TextGrid files only
files = Path(input_dir).glob('*.TextGrid')

#initialize header
df_header = pd.DataFrame(columns= ["speaker_id","segment_offset","mwu_type","transcription","speaker_type",
                    "segment_onset","vcm_type","addresseee","raw_filename","ling_type","lex_type"])

corpus_onset= 0
corpus_offset = 0

#initialize empty list
interval_list = []

#Loop through each textgrid file of the folder
for file in files:

    #open textgrid file
    print(file)
    grid = pr.TextGrid(file)

    #gets original onset and offset of the sliced recording
    audio_onset = int(re.split("\D+", file.stem)[1])
    audio_offset = int(re.split("\D+", file.stem)[2])

    if(audio_onset < corpus_onset): corpus_onset = audio_onset
    if(audio_offset > corpus_offset): corpus_offset = audio_offset

    #loop through all tiers
    for tier in grid.get_tiers():
        for interval in tier.get_all_intervals():
            
            #populates dataframe
            temp_dict = {'speaker_id': tier.name, 'segment_onset': (interval[0]*1000 + audio_onset),
                'segment_offset': (interval[1]*1000 + audio_onset), 'transcription': interval[2]}

            #populates list
            interval_list.append(temp_dict)

#makes dataframe from header and data
df = DataFrame(interval_list, columns= ["speaker_id","segment_offset","mwu_type","transcription","speaker_type",
                    "segment_onset","vcm_type","addresseee","raw_filename","ling_type","lex_type"])

#populates speaker_type column
df['speaker_type'] = df['speaker_id'].map(speaker_type_dic)

#enriches the csv with previously dropped entries corresponsing to the right timestamps
orig_df_subset = original_df[(original_df['segment_onset'] >= corpus_onset) &
                     (original_df['segment_offset'] <= corpus_offset)]


df = pd.concat([df, orig_df_subset])

#sort values by segment_onset before exporting
df.sort_values(by='segment_onset', inplace= True)

#exports to csv
df.to_csv("{0}/BN.csv".format(output_path), mode = "x", na_rep= "NA", index= False)
print("----------------SAVED!-----------------")