ganapati

Metre, verse boundaries, citations, and lemmas. import ganapati registers the Sanskrit reader profiles, so a GRETIL or TEI source is chunked by verse and its tree is built from the citations.

from litesearch import Index
from ganapati import Meter

ix = Index('vault.db')
ix.add('mahabharata.htm')
ix.search('dharma')[:3]
ix.db.by_meter(meter=Meter.ANUSTUBH)[:3]

/Users/71293/code/personal/orgs/ganapati/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

[{'content': 'nārāyaṇaṃ namaskṛtya naraṃ caiva narottamam\ndevīṃ sarasvatīṃ caiva tato jayam udīrayet',
  'metadata': '{"meter": "anuṣṭubh", "variant": "pathyā", "gana": "ta_ra_ga_ga", "pada": "8"}',
  'node_id': '73a63025c7fc3d14#0',
  'doc_id': '73a63025c7fc3d14',
  'page': 0},
 {'content': 'samāsīnān abhyagacchad brahmarṣīn saṃśitavratān\nvinayāvanato bhūtvā kadā cit sūtanandanaḥ',
  'metadata': '{"meter": "anuṣṭubh", "variant": "pathyā ra-vipulā", "gana": "ya_ta_ga_ga", "pada": "8"}',
  'node_id': '73a63025c7fc3d14#0',
  'doc_id': '73a63025c7fc3d14',
  'page': 2},
 {'content': 'tam āśramam anuprāptaṃ naimiṣāraṇyavāsinaḥ\ncitrāḥ śrotuṃ kathās tatra parivavrus tapasvinaḥ',
  'metadata': '{"meter": "anuṣṭubh", "variant": "pathyā", "gana": "ja_sa_ga_ga", "pada": "8"}',
  'node_id': '73a63025c7fc3d14#0',
  'doc_id': '73a63025c7fc3d14',
  'page': 3}]

Every chunk carries its metre as searchable metadata.

verse_meta('dharmakṣetre kurukṣetre samavetā yuyutsavaḥ '
           'māmakāḥ pāṇḍavāścaiva kimakurvata sañjaya')

{'meter': 'anuṣṭubh', 'variant': 'pathyā', 'gana': 'ma_ra_ga_ga', 'pada': '8'}

The scan behind it, and the metre that pattern names:

scan('dharmakṣetre kurukṣetre')     # g for heavy, l for light

'gggglggg'

detect_meter('kaścit kāntāvirahaguruṇā svādhikārātpramattaḥ '
             'śāpenāstaṃgamitamahimā varṣabhogyeṇa bhartuḥ '
             'yakṣaścakre janakatanayāsnānapuṇyodakeṣu '
             'snigdhacchāyātaruṣu vasatiṃ rāmagiryāśrameṣu')['name']

<Meter.MANDAKRANTA: 'mandākrāntā'>

A line at a time

Forced alignment and ASR hand you lines, not verses. match_pada names a metre from one pāda and group_verses finds how many lines make a verse.

padas = ['kaścit kāntāvirahaguruṇā svādhikārātpramattaḥ',
         'śāpenāstaṃgamitamahimā varṣabhogyeṇa bhartuḥ',
         'yakṣaścakre janakatanayāsnānapuṇyodakeṣu',
         'snigdhacchāyātaruṣu vasatiṃ rāmagiryāśrameṣu']
match_pada(padas[0]), group_verses(padas).size

The three modules

module	what is in it
ganapati.text	verse boundaries, pādas and verse numbers, the two chunkers, readers for GRETIL, TEI, VR XML and DCS
ganapati.metre	IAST transliteration, syllable weights, the gaṇas, 80 metres, mātrā metres, a metre by name
ganapati.lemma	vidyut lemmas, Monier-Williams glosses, the source’s own etymology entries and their repair, the reader profiles

Install

pip install ganapati

Lemmas and glosses need vidyut and an 81 MB data download, both reached on first use. Without them a store still gets metre, audio timings, and whatever analysis the source itself carries.