WUT_Computer_Science/Programming/NLP/format_alignments.py
Krzysztof kuhy Rudnicki bd24dd57ea Hold the coursework to the same lint bar as the tooling
kuhy overruled the "do not fix coursework to be better than it was submitted"
rule: git history is the archive, so the working tree can be clean. This lands
the config and the mechanical half of the cleanup -- 9,281 ruff findings under
the fleet config down to 3,686, with no per-file-ignores anywhere.

What is in here:

- pyproject.toml, the first one in this repo. select = ["ALL"], mypy strict,
  and the vendored list mirrored into ruff exclude and coverage omit. Five
  rules are off and each says why; CPY001 is the only judgement call, because
  stamping a copyright header onto 137 university assignments would assert a
  licence that was never claimed. No hook is wired up yet -- a gate lands only
  once its subject is clean.

- ruff format + the safe fixes, then a second pass over the docstring rules
  ruff marks unsafe (they only edit docstring text). 108 files.

- TRAK/sightpy is vendored, not coursework: it is rafael-fuente/Python-Raytracer
  (BSD 3-Clause). Verified against a fresh upstream clone with line endings
  normalised -- 30 of 32 files byte-identical, ray.py and scene.py carrying a
  12- and 13-line local patch that counts rays for the report. 35 files that
  should never have been linted, now excluded and restored.

- WDWR, SPD, MOM and ECRYPT are fully clean, by hand and verified by running
  them: WDWR's three plot scripts print the same statistics as before, MOM's
  PuLP model solves to the same cost. WDWR's graph.py had a 342-character CSV
  header embedded as a string literal, which cannot be wrapped -- the data
  moved to data/pareto_front.csv. Two 0-byte .py stubs are deleted; an empty
  file is not a deliverable and git still has it.

Three harness bugs found by actually running things:

- `run` now forces MPLBACKEND=Agg. TRAK finished its ray trace in 37 seconds
  and then sat for 45 minutes: plt.show() had opened an image viewer and was
  waiting for someone to close the window.
- TRAK's main.py only pops that viewer with --show now, and its runner writes
  to a gitignored build/ via the new --entry-args. The default output path is
  outputs/output.png, which is TRACKED -- a plain run overwrote a submitted
  deliverable.
- course_lib.sh was 357 lines, over this repo's own cap. Split into
  course_probe.sh and course_run.sh.

Verified: ECOTE 26 tests pass, ECRYPT_PROJECT 99 pass, TRAK renders
cornell_box in 37s and exits 0, every module compiles, and every import ruff
removed was checked to be genuinely unused.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
2026-08-28 19:53:04 +02:00

165 lines
5.6 KiB
Python

import copy
import re
import pandas as pd
import processing
# input file name
file_path = "alignments_unformatted_headlines.txt"
# output file path
output_file_path = "headlines_fixed_format.wa"
# paths to headlines database
chunked_path1 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
chunked_path2 = "test_goldStandard/headlines/STSint.testinput.headlines.sent1.chunk.txt"
alignment_path = "test_goldStandard/headlines/STSint.testinput.headlines.wa"
# paths to students andsewrs database
# chunked_path1 = "test_goldStandard/student/STSint.testinput.answers-students.sent1.chunk.txt"
# chunked_path2 = "test_goldStandard/student/STSint.testinput.answers-students.sent2.chunk.txt"
# alignment_path = "test_goldStandard/student/STSint.testinput.answers-students.wa"
# load data
goldstandard_chunked = processing.load_chunked(chunked_path1, chunked_path2)
goldstandard_alignment = processing.load_alignment(alignment_path)
# get a nice table
data = pd.merge(
goldstandard_chunked, goldstandard_alignment, left_index=True, right_index=True
)
# open generated alignments
with open(file_path) as file:
responses = [eval(line.strip()) for line in file]
for i, r in enumerate(responses):
print("\nresponse number " + str(i))
print(r)
unformatted = copy.deepcopy(responses)
for i, response in enumerate(responses):
temp = response.lstrip("\n")
temp = temp.rstrip("\n")
temp = re.sub(r"(?<==)>", "> ", temp) # add space after >
temp = re.sub(r"(?<!\n) +(?!\n)", " ", temp) # remove double space
temp = temp.replace("]", "")
temp = temp.replace("[", "")
temp = temp.replace("'", "")
temp = temp.replace(" ==> ", " <==> ")
temp = temp.replace(" => ", " <==> ")
temp = temp.replace(" <=> ", " <==> ")
temp = temp.replace(" <== ", " <==> ")
temp = temp.replace("<==> //", " <==> 0 //")
temp = temp.replace("NOALI <==>", "0 <==>")
temp = temp.replace("<==> NOALI", "<==> 0")
temp = temp.replace("\n// NOALI", "\n0 <==> 0 // NOALI")
temp = temp.replace("\n // ", "\n0 <==> ")
temp = temp.replace("\n// ", "\n0 <==> ")
temp = re.sub(r"(^|[^<])(==>+)", r" <==>", temp)
temp = temp.replace("// - //", "// 0 //")
temp = temp.replace("// score", "// ")
temp = temp.replace("// alignment type", "// NOALI")
temp = temp.replace("> -", "> 0")
temp = temp.replace("- <", "0 <")
temp = temp.replace("// //", "// NOALI //")
temp = temp.replace("equi", "EQUI")
temp = re.sub(r"\d\. ", "", temp) # remove 1., 2. ...
temp = temp.upper()
temp = re.sub(r"^(<==>)", r"0 \1", temp)
temp = re.sub(r"(?<!\n) +(?!\n)", " ", temp) # remove double space
temp = temp.split("\n")
for k, t in enumerate(temp):
if "<==>" not in t:
temp[k] = "0 <==> " + temp[k]
temp[k] = temp[k].split("<==>")
temp[k] = [temp[k][0], *temp[k][1].split("//")]
chunk1arr = data.iloc[i]["chunked_sentance1"]
q = 1
numberList = []
for chunk in chunk1arr:
chunk = re.sub(r"(?<!\n) +(?!\n)", " ", chunk)
n_of_words = len(chunk.strip().split(" "))
index_str = ""
for qq in range(q, q + n_of_words):
index_str = index_str + str(qq) + " "
numberList.append(index_str)
q = q + n_of_words
for j, chunk in enumerate(data.iloc[i]["chunked_sentance1"]):
pattern = re.compile(chunk, re.IGNORECASE)
temp[k][0] = pattern.sub(numberList[j], temp[k][0])
chunk1arr = data.iloc[i]["chunked_sentance2"]
q = 1
numberList = []
for chunk in chunk1arr:
chunk = re.sub(r"(?<!\n) +(?!\n)", " ", chunk)
n_of_words = len(chunk.strip().split(" "))
index_str = ""
for qq in range(q, q + n_of_words):
index_str = index_str + str(qq) + " "
numberList.append(index_str)
q = q + n_of_words
for j, chunk in enumerate(data.iloc[i]["chunked_sentance2"]):
pattern = re.compile(chunk, re.IGNORECASE)
temp[k][1] = pattern.sub(numberList[j], temp[k][1])
if any(char.isalpha() and ord(char) < 128 for char in temp[k][0]) or any(
char.isalpha() and ord(char) < 128 for char in temp[k][1]
):
temp[k] = ""
if len(temp[k]) >= 4:
temp[k][3] = temp[k][3].replace("NOALI", "0")
if temp[k][3] == "":
temp[k][3] = " 0 "
if temp[k][3] == " ":
temp[k][3] = " 0 "
temp[k] = (
temp[k][0]
+ " <==> "
+ temp[k][1]
+ " // "
+ temp[k][2]
+ " // "
+ temp[k][3]
)
elif len(temp[k]) == 3:
temp[k] = temp[k][0] + " <==> " + temp[k][1] + " // " + temp[k][2] + " // 0"
temp[k] = re.sub(r"\s{2,}", " ", temp[k]) # remove double space
temp = [x for x in temp if x != ""]
responses[i] = "\n".join(temp).strip()
indexes = []
responses_final = []
rejected_indexes = []
for n, r in enumerate(responses):
if r == "":
rejected_indexes.append(n + 1)
continue
if r == "\n":
rejected_indexes.append(n + 1)
continue
indexes.append(n + 1)
responses_final.append(r)
print("rejected indexes:")
print(rejected_indexes)
with open(output_file_path, "w") as file:
for i, r in zip(indexes, responses_final):
file.write('<sentence id="' + str(i + 1) + '" status="">\n')
file.write("<alignment>\n")
file.write(r)
file.write("\n</alignment>\n")
file.write("</sentence>\n\n")