'''
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Featurize folders of text files if default_text_features = ['textacy_features']

Featurizes .TXT files with textacy - mostly intelligibility measures, as shown below.
More info on textacy can be found @ https://github.com/chartbeat-labs/textacy

-----

ts = textacy.TextStats(doc)

ts.n_unique_words
57
>>> ts.basic_counts
{'n_sents': 3,
 'n_words': 73,
 'n_chars': 414,
 'n_syllables': 134,
 'n_unique_words': 57,
 'n_long_words': 30,
 'n_monosyllable_words': 38,
 'n_polysyllable_words': 19}
>>> ts.flesch_kincaid_grade_level
15.56027397260274
>>> ts.readability_stats
{'flesch_kincaid_grade_level': 15.56027397260274,
 'flesch_reading_ease': 26.84351598173518,
 'smog_index': 17.5058628484301,
 'gunning_fog_index': 20.144292237442922,
 'coleman_liau_index': 16.32928468493151,
 'automated_readability_index': 17.448173515981736,
 'lix': 65.42922374429223,
 'gulpease_index': 44.61643835616438,
 'wiener_sachtextformel': 11.857779908675797}
'''
import textacy, os
import numpy as np 

def stats(matrix):
    mean=np.mean(matrix)
    std=np.std(matrix)
    maxv=np.amax(matrix)
    minv=np.amin(matrix)
    median=np.median(matrix)
    output=np.array([mean,std,maxv,minv,median])
    return output

def textacy_featurize(transcript, nlp):
    features=list()
    labels=list()

    # use Spacy doc
    doc = nlp(transcript)
    
    
    ts = textacy.TextStats(doc)
    uniquewords=ts.n_unique_words
    features.append(uniquewords)
    labels.append('uniquewords')

    mfeatures=ts.basic_counts
    features=features+list(mfeatures.values())
    labels=labels+list(mfeatures)

    kincaid=ts.flesch_kincaid_grade_level
    features.append(kincaid)
    labels.append('flesch_kincaid_grade_level')

    readability=ts.readability_stats
    features=features+list(readability.values())
    labels=labels+list(readability)
    
    return features, labels
