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

Names and terms

A reader asks about Sri Ranganatha where the translation writes Viṣṇu, or about anumana where it says inference.

canonical('Sri Ranganatha'), canonical('Partha'), term_gloss('anumana')

(('viṣṇu', 'nārāyaṇa', 'hari'), ('arjuna',), ('inference',))

The four modules

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.