Spanish spelling encodes both the syllable boundaries and the stress, so the library needs no dictionary: diphthongs, hiatuses and triphthongs, the silent u of qu and gu, the vocalic y, the h inside a diphthong and the consonant clusters give the syllables; the written accent, or the ending of the word when there is none, gives the stressed syllable. Adverbs in -mente and hyphenated compounds carry two stresses.

>>> from ests.syllables import stress_type, syllabify, word_stress, word_stresses

>>> syllabify("murciélago")
['mur', 'cié', 'la', 'go']

>>> syllabify("averiguáis")
['a', 've', 'ri', 'guáis']

>>> word_stress("construir"), stress_type("construir")
(1, 'aguda')

>>> word_stresses("fácilmente")
[0, 2]

More in the documentation.

The library allows extracting the following statistics from a text:

• the number of sentences
• the number of words
• the number of unique words
• the number of long words
• the number of complex words
• the number of simple words
• the number of monosyllable words
• the number of polysyllable words
• the number of characters
• the number of letters
• the number of spaces
• the number of syllables
• the number of punctuation marks and their distribution by type
• the distribution of words by the number of letters
• the distribution of words by the number of syllables

A complex word has three or more syllables and a long word seven or more letters, as the Spanish readability formulas count them. Any statistic can be printed in a readable form:

>>> from ests import BasicStats

>>> text = "Hay tres clases de mentiras: mentiras, malditas mentiras y estadísticas"
>>> BasicStats(text).print_stats()
     Statistic      |  Value
------------------------------
Sentences           |    1
Words               |    10
Unique words        |    8
Long words          |    5
Complex words       |    5
Simple words        |    5
Monosyllabic words  |    4
Polysyllabic words  |    6
Characters          |    71
Letters             |    60
Spaces              |    9
Syllables           |    23
Punctuation marks   |    2

More in the documentation.

The library allows counting the following readability metrics:

• Flesch reading ease with the coefficients of Szigriszt-Pazos or of Fernández Huerta
• Gutiérrez de Polini comprehensibility formula
• Crawford grade
• Legibilidad µ
• SOL grade, the SMOG index converted to Spanish
• LIX readability measure
• RIX readability measure

An interpretation layer works on top of the formulas: the band of a scale for the reading ease and for Legibilidad µ, a consensus grade as the median of the grade formulas, the school stage and the reader age of Spain, and reading time by the norms of Spanish-speaking readers.

The coefficients of the Flesch reading ease are selected by the preset argument: by default the fórmula de perspicuidad of Szigriszt-Pazos with the INFLESZ scale validated on texts for patients (general); the coefficients of Fernández Huerta with the bands of their author are available as classic.

>>> from pprint import pprint
>>> from ests import ReadabilityStats

>>> text = "Hay tres clases de mentiras: mentiras, malditas mentiras y estadísticas"
>>> rs = ReadabilityStats(text)

>>> pprint(rs.get_stats(), sort_dicts=False)
{'flesch_reading_easy': 53.545000000000016,
 'gutierrez_polini_index': 33.5,
 'crawford_grade': 5.812999999999999,
 'mu_index': 50.943396226415096,
 'sol_grade': 9.258359866374562,
 'lix': 60.0,
 'rix': 5.0,
 'consensus_grade': 9.0,
 'reading_time': 0.03597122302158273}

>>> rs.print_stats()
                   Metric                    |  Value
-------------------------------------------------------
Flesch reading ease (Szigriszt-Pazos)        |  53.55
Gutiérrez de Polini comprehensibility        |  33.50
Crawford grade                               |   5.81
Legibilidad µ                                |  50.94
SOL grade (SMOG for Spanish)                 |   9.26
LIX readability index                        |  60.00
RIX readability index                        |   5.00
Consensus grade                              |   9.00
Reading time (min)                           |   0.04

>>> rs.describe_level()
'algo difícil'

>>> rs.describe_grade()
'ESO (12-16 years)'

More in the documentation.

The library allows counting 32 lexical diversity metrics, among them:

• Type-Token Ratio and its variations: RTTR, CTTR, Herdan, Summer, Maas, Dugast
• Moving Average Type-Token Ratio and Mean Segmental Type-Token Ratio
• Measure of Textual Lexical Diversity and its moving-window variants MA-MTLD and MTLD-W
• Hypergeometric Distribution D
• Simpson's index, its reciprocal and the Gini-Simpson index
• the hapax index (Honoré's R), the measures of Yule, Herdan, Sichel, Michéa, Brunet, Dugast and Baayen
• Shannon entropy, evenness and perplexity
• the slope of Zipf's law, the Zipf-Mandelbrot fit and the exponent of Heaps' law

Any metric can be computed over windows of equal length, which is the standard way to compare texts of different lengths: the mean over the windows comes with a confidence interval.

>>> from ests import DiversityStats

>>> text = "Hay tres clases de mentiras: mentiras, malditas mentiras y estadísticas"
>>> ds = DiversityStats(text)

>>> ds.ttr, ds.mtld, ds.yule_k
(0.8, 14.000000000000004, 600.0)

>>> ds.frequency_spectrum
{1: 7, 3: 1}

>>> DiversityStats("La legibilidad de un texto depende de la longitud de sus oraciones y de sus "
...                "palabras. Las fórmulas clásicas miden esas dos magnitudes y las combinan en "
...                "un solo número. Ninguna de ellas mide la comprensión: miden la superficie "
...                "del texto.").windowed("ttr", window_len=10)
WindowStats(mean=0.875, std=0.1258305739211792, lower=0.6747754774664074, upper=1.0752245225335926, n_windows=4)

More in the documentation.

• docs - project documentation
• ests:
  • basic_stats.py - basic text statistics
  • constants.py - constants of the Spanish language and of the metrics
  • diversity_stats.py - lexical diversity metrics
  • exceptions.py - library exceptions
  • extractors.py - tools for object extraction from a text
  • readability_stats.py - readability metrics
  • syllables.py - syllabification and stress
  • utils.py - helper tools
• tests - tests mirroring the package structure