import os
os.chdir(r'C:\Users\Aki\source\repos\acoustic_model\acoustic_model')

import pandas as pd

import convert_xsampa2ipa
import defaultfiles as default


def load_transcriptions():
	stimmen_transcription = pd.ExcelFile(default.stimmen_transcription_xlsx)
	df = pd.read_excel(stimmen_transcription, 'original')

	# mapping from ipa to xsampa
	mapping = convert_xsampa2ipa.load_converter('xsampa', 'ipa', default.ipa_xsampa_converter_dir)
	#for xsampa, ipa in zip(df['X-SAMPA'], df['IPA']):
	#    ipa_converted = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
	#    if not ipa_converted == ipa:
	#        print('{0}: {1} - {2}'.format(xsampa, ipa_converted, ipa))

	ipas     = []
	for xsampa in df['Self Xsampa']:
		if not isinstance(xsampa, float): # 'NaN'
			# typo?
			xsampa = xsampa.replace('r2:z@rA:\\t', 'r2:z@rA:t').replace(';', ':')

			ipa = convert_xsampa2ipa.xsampa2ipa(mapping, xsampa)
			ipa = ipa.replace('ː', ':').replace(' ', '')
			ipas.append(ipa)
		else:
			ipas.append('')

	df_ = pd.DataFrame({'filename': df['Filename'], 
						'word': df['Word'], 
						'xsampa': df['Self Xsampa'],
						'ipa': pd.Series(ipas)})
	df_ = df_[~df_['ipa'].str.contains('/')]
	return df_.dropna()