<jupyter_start><jupyter_text>Uruk specifics* Plain display of signs, quads and clusters is customized* Signs and quads have graphics<jupyter_code>A = use("uruk:clone", checkout="clone", hoist=globals())
A.reuse()<jupyter_output>0.44s All features loaded/computed - for details use loadLog()<jupyter_text>Unravel and plainCustom<jupyter_code>c = 167736
tree = A.unravel(c, explain=True)
tree = A.unravel(c, explain="details")
tree
A.pretty(c)
c = 2001
A.plain(c)<jupyter_output><empty_output><jupyter_text>Tablets in plain<jupyter_code>t = F.otype.s("tablet")[0]
A.plain(t)
A.plainTuple((t,), 1)
A.show(((t,),))<jupyter_output><empty_output><jupyter_text>Graphics<jupyter_code>query = """
case
  sign type=numeral
"""

results = A.search(query)
A.plain(results[1][1])
A.plainTuple(results[0], 1)
A.table(results, end=5)<jupyter_output><empty_output><jupyter_text>Examples<jupyter_code>s1 = 106597
s2 = 106589
q1 = 143013
q2 = 143015
c1 = 194488
c2 = 219151
examples = (s1, s2, q1, q2, c1, c2)
colors = """
    cyan
    yellow
    lightsalmon
    lightgreen
    goldenrod
    cornflowerblue
    forestgreen
    burlywood
    orange
    indianred
""".strip().split()
highlights = dict(zip(examples, colors))
lines = set()
A.displayReset()
A.displaySetup(showGraphics=False, withNodes=True)
A.dm("## Example nodes\n")
for e in examples:
    slots = L.d(e, otype="sign")
    if len(slots) > 1:
        lines.add(L.u(slots[0], otype="line")[0])
        lines.add(L.u(slots[-1], otype="line")[0])
    lines.add(L.u(e, otype="line")[0])
    A.plain(e)
    A.pretty(e)
A.dm("\n---\n## Example lines\n")
for ln in sorted(lines):
    print(A.getSource(ln))
    A.plain(ln, highlights=highlights)
    A.pretty(ln, highlights=highlights)
A.displayReset()
example = T.nodeFromSection(("P005381",))
A.getSource(example)
A.pretty(example, withNodes=False)
A.pretty(example, withNodes=False, showGraphics=False, lineNumbers=True)
A.photo(example)
typeShow(A)
A.showContext()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16406643180674382, 'fraction_numerical': 0.04026170105686965, 'mean_word_length': 5.454545454545454, 'pattern_counts': {'":': 0, '<': 20, '<?xml version=': 0, '>': 21, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '328175', 'license': 'MIT', 'n_tokens_mistral': 850, 'n_tokens_neox': 787, 'n_words': 142, 'path': 'zz_test/099-uruk.ipynb', 'repo_name': 'sethbam9/tutorials', 'size': '1987'}