<jupyter_start><jupyter_text>Queries on <NAME>'s dissertation claims How to write nicely with `Markup` Check this out!https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet  Getting TF ready<jupyter_code>%load_ext autoreload
%autoreload 2
# First, I have to laod different modules that I use for analyzing the data and for plotting:
import sys, os, collections
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt; plt.rcdefaults()
from matplotlib.pyplot import figure
from collections import Counter

# Second, I have to load the Text Fabric app
from tf.fabric import Fabric
from tf.app import use
A = use('bhsa', hoist=globals())<jupyter_output>connecting to online GitHub repo annotation/app-bhsa ... connected
	code/__init__.py...downloaded
	code/app.py...downloaded
	code/config.py...downloaded
	code/static...directory
		code/static/display.css...downloaded
		code/static/logo.png...downloaded
	OK
Using TF-app in /Users/Randy/text-fabric-data/annotation/app-bhsa/code:
	rv1.1=#35e4fee27a1cd6f0a5caea9118129349ddb0604e (latest release)
	connecting to online GitHub repo etcbc/bhsa ... connected
Using data in /Users/Randy/text-fabric-data/etcbc/bhsa/tf/c:
	rv1.6=#bac4a9f5a2bbdede96ba6caea45e762fe88f88c5 (latest release)
	connecting to online GitHub repo etcbc/phono ... connected
Using data in /Users/Randy/text-fabric-data/etcbc/phono/tf/c:
	r1.2=#1ac68e976ee4a7f23eb6bb4c6f401a033d0ec169 (latest release)
	connecting to online GitHub repo etcbc/parallels ... connected
Using data in /Users/Randy/text-fabric-data/etcbc/parallels/tf/c:
	r1.2=#395dfe2cb69c261862fab9f0289e594a52121d5c (latest release)
   |     0.00s No structure info in [...]<jupyter_text>Dissertation Queries...  Querie 1On page 75 Vogel states that there are only 2 passages where the phrases generally translated as "which were with him" and "man and his house" occur. The below querie tests this claim.<jupyter_code>OccurenceQ='''
clause
    phrase
        word lex=<CR
        word lex=<M
    phrase
        word lex=>JC/
        word lex=BJT/
'''
OccurenceQ  = A.search(OccurenceQ)
A.table(OccurenceQ, start=1, end=5, condensed=True)<jupyter_output>1.96s 0 results
{'fraction_non_alphanumeric': 0.0965391621129326, 'fraction_numerical': 0.050546448087431695, 'mean_word_length': 4.451612903225806, 'pattern_counts': {'":': 0, '<': 10, '<?xml version=': 0, '>': 9, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 5, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '581722', 'license': 'MIT', 'n_tokens_mistral': 846, 'n_tokens_neox': 768, 'n_words': 208, 'path': 'studentNBs/0000_ETCBC-TF_0003-BHS_c.vogel_dissertation-queries_o.glanz_r.folkenberg.ipynb', 'repo_name': 'oliverglanz/Text-Fabric', 'size': '2201'}