scireadability 2.0.2

Calculate statistical features from text, mainly scientific literature

scireadability

scireadability is a user-friendly Python library designed to calculate text statistics for English texts. It's helpful for assessing readability, complexity, and grade level of texts. While specifically enhanced for scientific documents, it works well with any type of text. Punctuation is removed by default, with the exception of apostrophes in contractions.

You can try it out on the scireadability demo site here.

This library is built upon the foundation of the textstat Python library, but behaves differently.

Why scireadability?

While building upon the excellent textstat library, scireadability is enhanced to provide more accurate coverage of scientific and technical texts.

• CMUdict-driven syllables (with multiple pronunciations handled conservatively).
• Token-based difficult-word math for formulas that require it (e.g., Dale–Chall, SPACHE).
• Consistent tokenization and letter counting that plays nicely with Coleman–Liau.
• A custom dictionary for words that are inaccurately counted by other methods (often jargon, species names, and other specialized words)

scireadability currently supports English. For non-English texts, textstat offers broad coverage.

Key features

• Accurate syllable counts using a multi-tiered approach:
  1. CMUdict (takes the minimum syllable count across pronunciations),
  2. a custom dictionary you can edit/extend,
  3. a refined regex fallback with scientific-name adjustments.
• Token-based difficult word rates where the original formulas expect them.
• Configurable apostrophe handling and rounding.

Quick start

Install

pip install scireadability

Usage

>>> import scireadability

>>> test_data = (
...     "Within the heterogeneous canopy of the Amazonian rainforest, a fascinating interspecies interaction manifests "
...     "between Cephalotes atratus, a species of arboreal ant, and Epiphytes dendrobii, a genus of epiphytic orchids.  "
...     "Observations reveal that C. atratus colonies cultivate E. dendrobii within their carton nests, providing a "
...     "nitrogen-rich substrate derived from ant detritus.  In return, the orchids, exhibiting a CAM photosynthetic "
...     "pathway adapted to the shaded understory, contribute to nest structural integrity through their root systems and "
...     "potentially volatile organic compounds.  This interaction exemplifies a form of facultative mutualism, where both "
...     "species derive benefits, yet neither exhibits obligate dependence for survival in situ. Further investigation into "
...     "the biochemical signaling involved in this symbiosis promises to elucidate novel ecological strategies."
... )

>>> scireadability.flesch_reading_ease(test_data)
>>> scireadability.flesch_kincaid_grade(test_data)
>>> scireadability.smog_index(test_data)
>>> scireadability.coleman_liau_index(test_data)
>>> scireadability.automated_readability_index(test_data)
>>> scireadability.dale_chall_readability_score(test_data)
>>> scireadability.linsear_write_formula(test_data)
>>> scireadability.gunning_fog(test_data)

# Using the custom dictionary:
>>> scireadability.add_word_to_dictionary("pterodactyl", 4)
>>> scireadability.syllable_count("pterodactyl")

For all functions, the input argument (text) is the text you want to analyze.

Language support

This library is English-only by design. Syllables are computed via:

• CMUdict: Carnegie Mellon Pronouncing Dictionary; when multiple pronunciations exist, the minimum syllable count is used.
• Custom dictionary: User-editable overrides for domain terms.
• Regex fallback: An improved counter that handles common scientific suffixes (e.g., species names), which typical counters undercount.

Custom syllable dictionary

Tune syllables for edge cases or specialized vocabulary.

• load_custom_syllable_dict()
• overwrite_dictionary(file_path)
• add_word_to_dictionary(word, syllable_count)
• add_words_from_file_to_dictionary(file_path)
• revert_dictionary_to_default()
• print_dictionary()

Dictionary file format

{
  "CUSTOM_SYLLABLE_DICT": {
    "word1": 3,
    "word2": 2,
    "anotherword": 4
  }
}

Controlling apostrophe handling

scireadability.set_rm_apostrophe(rm_apostrophe)

This is a global setting that changes the library's behavior for all subsequent calls.

By default, this is set to false (apostrophes in common contractions like don't or it's are preserved). If you set it to true, all apostrophes will be stripped along with other punctuation. Because this is a global change, it's recommended to set it once at the beginning of your script.

Controlling output rounding

This library offers two ways to control rounding: a global setting and a flexible per-call override.

scireadability.set_rounding(rounding, points=None)

Call this function once to change the default rounding behavior for all subsequent formula calls.

• By default, rounding is False.

• If you enable rounding without specifying points, each metric uses a sensible default precision (e.g., one decimal for grade levels, two for scores).

• Pass an explicit points value to force a specific number of decimals for all calls.

Per-call override

For more explicit and predictable control, you can pass rounding arguments directly to any formula function. These arguments will always take precedence over the global setting for that specific call.

# The global setting is off, but this specific call will be rounded
scireadability.flesch_kincaid_grade(text, rounding=True, points=1)

# Override the global setting to get an unrounded score for just this call
scireadability.set_rounding(True, points=2)
scireadability.flesch_reading_ease(text, rounding=False)

List of functions

Formulas

Flesch Reading Ease

scireadability.flesch_reading_ease(text)

Higher = easier (approx. up to ~121; negatives possible for very hard text).

Flesch–Kincaid Grade Level

scireadability.flesch_kincaid_grade(text)

Estimated U.S. grade level based on ASL and ASW.

Gunning Fog Index

scireadability.gunning_fog(text)

Uses average sentence length and the percentage of polysyllabic tokens (≥3 syllables).

SMOG Index

scireadability.smog_index(text)

Most reliable with ~30 sentences; returns 0.0 if fewer than 3 sentences.

Automated Readability Index (ARI)

scireadability.automated_readability_index(text)

Grade level from characters/word and words/sentence.

Coleman–Liau Index

scireadability.coleman_liau_index(text)

Grade level from letters/word and sentences/word (no syllables).

Linsear Write Formula

scireadability.linsear_write_formula(text)

Uses the first 100 words; counts “easy” (1–2 syllables) and “difficult” (≥3).

Dale–Chall Readability Score

scireadability.dale_chall_readability_score(text)

Computes the standard DC score from token-based difficult words and maps to grade bands in text_standard.

Score	Understood by
4.9 or lower	Average 4th-grade student or below
5.0–5.9	Average 5th or 6th-grade student
6.0–6.9	Average 7th or 8th-grade student
7.0–7.9	Average 9th or 10th-grade student
8.0–8.9	Average 11th or 12th-grade student
9.0–9.9	College (13th–15th grade)

Readability Consensus (Text Standard)

scireadability.text_standard(text, as_string=True)

Consensus grade from multiple indices. Dale–Chall is first converted from score → grade band before voting.

FORCAST

scireadability.forcast(text)

Grade estimate from single-syllable counts in a 150-word sample (warns if shorter).

SPACHE

scireadability.spache_readability(text)

For young readers; uses sentence length and percentage of “hard words” (token-based).

McAlpine EFLAW

scireadability.mcalpine_eflaw(text)

Useful for EFL materials; combines word count, mini-word count (≤3 letters), and sentence count.

LIX

A Swedish readability formula that measures the text's difficulty based on average sentence length and the percentage of long words (more than 6 characters). The score is not mapped to a specific grade level.

scireadability.lix(text)

Score	Readability
< 30	Very easy
30–40	Easy
40–50	Standard
50–60	Difficult
> 60	Very difficult

RIX

A simple formula that calculates a grade-level score based on the ratio of long words (more than 6 characters) to the number of sentences. It is closely related to LIX but presents the output as a grade level.

Reading time

scireadability.reading_time(text, wpm=200.0)

Returns seconds, using a words-per-minute model (default 200 WPM).

Aggregates and averages

Syllable count

scireadability.syllable_count(text)

Total syllables; CMUdict → custom dict → regex fallback.

Word count (lexicon)

scireadability.lexicon_count(text, removepunct=True)

Counts tokens; hyphens/punctuation removed by default. Apostrophes depend on set_rm_apostrophe().

Sentence count

scireadability.sentence_count(text)

Regex-based; very short “sentences” (≤2 words) are ignored.

Character count

scireadability.char_count(text, ignore_spaces=True)

Counts all characters (optionally ignoring spaces).

Letter count

scireadability.letter_count(text, ignore_spaces=True)

Counts alphabetic code points (letters only). Spaces aren’t letters, so the flag typically has no effect.

Polysyllable / Monosyllable counts

scireadability.polysyllabcount(text)   # ≥3 syllables
scireadability.monosyllabcount(text)   # exactly 1 syllable

Limitations

• SMOG is best with ~30 sentences; <3 returns 0.0.
• Short snippets make most readability scores unstable.
• Extremely novel jargon may still require custom dictionary entries.
• Counting syllables with heuristics is inherently approximate; the regex fallback agrees with CMUdict ~91% of the time.
• English only.

Contributing

If you hit a bug or want to propose a tweak, please open an issue or leave feedback on the Try it page.

If you’re able to fix a bug or add a feature, we welcome a pull request.

1. Fork the repo and branch off master (or create a dedicated branch).
2. Add tests that demonstrate the fix/feature.
3. Open a PR.