fhardison

from collections import defaultdict
import sys
import csv


filename = 'macula-greek-SBLGNT.tsv'


lemmas_domain = defaultdict(set)
lemmas_senses = defaultdict(set)

fhardison

import xml.etree.ElementTree as ET
from difflib import get_close_matches
from pathlib import Path
import sys


DIR = Path('macula-greek/SBLGNT/lowfat/')
FILES = {
        'MAT': '01-matthew.xml',
        'MRK': '02-mark.xml',

fhardison

from pathlib import Path
from greek_normalisation.utils import nfc
from greek_accentuation.characters import add_breathing, Breathing,Accent, add_diacritic 
import re
import sys


FIND = [x for x in "α ε η υ ο ω i".split(' ') if x]
DIPTH = [x for x in "ει αι οι ου αυ ευ ηυ".split(' ') if x]

fhardison

from gnt_data import ChunkType, TokenType, get_tokens, get_tokens_by_chunk
from collections import Counter


def get_stats(token_type):

    ALL_GNT = get_tokens(token_type)

    BOOKS = get_tokens_by_chunk(token_type, ChunkType.book)

fhardison

from gnt_data import get_tokens, TokenType
from collections import defaultdict, namedtuple
from tabulate import tabulate
from statistics import mean, median


gnt_lemmas = get_tokens(TokenType.lemma)

data = defaultdict(list)

fhardison

from greek_normalisation.utils import nfc,grave_to_acute

def layout_paradigm(forms, row_identifiers, column_idenfiers):
#""" forms: [(form, parse)]
#... idenfiers = [lambda x: -> Boolean]
#"""
    output = []
    for rfn in row_identifiers:
        row = []
        for cfn in column_idenfiers:

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This is a literate doctest. Run python -m doctest -v examples.rst to test.

>>> from vocab_utils import group_tokens_by >>> from gnt_data import ChunkType, TokenType

Get sentences grouped by pericopes and show data for first 5.

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# filename-section OR filename-chapter.section (for longer works, i.e. shepherd)
013-shepherd.txt-22.0
013-shepherd.txt-19.0
013-shepherd.txt-20.0
013-shepherd.txt-21.0
013-shepherd.txt-24.0
013-shepherd.txt-12.0
013-shepherd.txt-4.0
013-shepherd.txt-9.0
013-shepherd.txt-3.0

fhardison

from greek_normalisation.utils import nfc, strip_last_accent_if_two, grave_to_acute
import re
from ordering import next_best
import sys
import glob
import json
from pysblgnt import morphgnt_rows
from collections import defaultdict

fhardison

import sys
import re
import mistletoe


def read_to_dict(fpath, encoding='UTF-8'):
    lines = {}
    with open(fpath, 'r', encoding=encoding) as file:
        for line in file:
            num, content = line.split(' ', maxsplit=1)