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import pandas as pd
from src.utils import read_data_file
SEP = "*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*"
def split_execution_traces(log_text: str):
parts = log_text.strip().split(SEP)
return [p.strip() for p in parts if p.strip()]
def _extract_code(block: str):
lines = block.splitlines()
start_idx = None
for i, ln in enumerate(lines):
if ln.strip().lower().startswith("-- code:"):
start_idx = i + 1
break
if start_idx is None:
return None, None # (code_text, code_end_idx)
end_idx = len(lines)
for j in range(start_idx, len(lines)):
s = lines[j].strip()
if s.startswith("-- ") and s.endswith(":"):
end_idx = j
break
code_block = "\n".join(lines[start_idx:end_idx]).strip("\n")
return (code_block if code_block.strip() else None), end_idx
def _extract_test_output(block: str):
lines = block.splitlines()
_, after_code_idx = _extract_code(block)
if after_code_idx is None:
return None, None
section_idx = None
section_tag = None
for k in range(after_code_idx, len(lines)):
tag = lines[k].strip().lower()
if tag.startswith("-- output:") or tag.startswith("-- error:"):
section_idx = k
section_tag = tag
break
if section_idx is None:
return None, None
end_idx = len(lines)
for t in range(section_idx + 1, len(lines)):
s = lines[t].strip()
if s.startswith("-- ") and s.endswith(":"):
end_idx = t
break
content = "\n".join(lines[section_idx + 1 : end_idx]).strip("\n")
if not content.strip():
content = None
out_type = "stdout" if section_tag.startswith("-- output:") else "stderr"
return out_type, content
def _read_scalar_after_section(lines, idx):
"""Return the first non-empty line after lines[idx], or None."""
for j in range(idx + 1, len(lines)):
txt = lines[j].strip()
if not txt:
# allow empty line(s) before the scalar
continue
return txt
return None
def _extract_submission(block: str):
"""
Extract from a SUBMISSION/SUBMITION block:
- execution_time (float, seconds)
- grade (float, percent 0..100)
- testcases (list of dicts with index, input, correct_output, user_output) OR None
- output_type: "stdout" (for test cases) or "stderr" (for error trace)
- output_content: raw text of test cases+grade or the error message
"""
lines = block.splitlines()
_, after_code_idx = _extract_code(block)
scan_start = after_code_idx if after_code_idx is not None else 0
execution_time = None
grade = None
testcases = []
output_lines = []
output_type, output_content = None, None
i = scan_start
while i < len(lines):
raw = lines[i].strip()
low = raw.lower()
# -- EXECUTION TIME:
if low.startswith("-- execution time:"):
val = _read_scalar_after_section(lines, i)
if val is not None:
try:
execution_time = float(val)
except:
pass
i += 1
continue
# -- ERROR:
if low.startswith("-- error:"):
output_type = "stderr"
# collect everything until next "-- " section or end
j = i + 1
err_lines = []
while j < len(lines):
if lines[j].strip().startswith("-- "):
break
err_lines.append(lines[j])
j += 1
output_content = "\n".join(err_lines).strip()
i = j
continue
# -- TEST CASE N:
if low.startswith("-- test case"):
idx = None
try:
head = raw.split(":", 1)[0]
idx = int(head.lower().split("test case", 1)[1].strip())
except:
pass
j = i + 1
current = {"index": idx, "input": "", "correct_output": "", "user_output": ""}
active = None
output_lines.append(lines[i]) # keep the "-- TEST CASE ..." line
while j < len(lines):
sraw = lines[j].rstrip("\n")
slow = sraw.strip().lower()
if slow.startswith("-- test case") or slow.startswith("-- grade:"):
break
output_lines.append(sraw)
if slow.startswith("---- input:"):
active = "input"; current["input"] = ""
elif slow.startswith("---- correct output:"):
active = "correct_output"; current["correct_output"] = ""
elif slow.startswith("---- user output:"):
active = "user_output"; current["user_output"] = ""
else:
if active:
current[active] += (sraw + "\n")
j += 1
for k in ("input", "correct_output", "user_output"):
current[k] = current[k].rstrip("\n")
testcases.append(current)
i = j
output_type = "stdout"
continue
# -- GRADE:
if low.startswith("-- grade:"):
val = _read_scalar_after_section(lines, i)
if val is not None:
v = val.strip().rstrip("%").strip()
try:
grade = float(v)
except:
pass
output_lines.append(lines[i])
if val: output_lines.append(val)
i += 1
continue
i += 1
if output_type == "stdout":
output_content = "\n".join(output_lines).strip() or None
return execution_time, grade, testcases or None, output_type, output_content
def parse_execution_data(full_path):
"""
Returns one row per trace with:
user, class_number, type, timestamp, code,
output_type, output_content (for TEST),
execution_time, grade, testcases (for SUBMISSION/SUBMITION),
trace
"""
print(f"Parsing execution data from {full_path}...")
user = full_path.split("/")[-3]
class_number = full_path.split("/")[-5]
assessment_id, exercise_id = full_path.split("/")[-1].replace(".log", "").split("_")
assessment_id = int(assessment_id)
exercise_id = int(exercise_id)
log_text = read_data_file(full_path) # you provide this
rows = []
for block in split_execution_traces(log_text):
# header '== TYPE (timestamp)'
first = next((ln.strip() for ln in block.splitlines() if ln.strip()), "")
header = first.lstrip("=").strip()
typ, ts = None, None
if "(" in header and ")" in header:
left, rest = header.split("(", 1)
typ = left.strip()
ts = rest.split(")", 1)[0].strip()
else:
typ = header
code, _ = _extract_code(block)
# Defaults
output_type, output_content = None, None
execution_time, grade, testcases = None, None, None
tnorm = (typ or "").strip().lower()
if tnorm == "test":
output_type, output_content = _extract_test_output(block)
elif tnorm in ("submission", "submition", "submit"):
execution_time, grade, testcases, output_type, output_content = _extract_submission(block)
rows.append({
"user": user,
"class_number": class_number,
"assessment_id": assessment_id,
"exercise_id": exercise_id,
"type": typ,
"timestamp": ts,
"code": code,
"output_type": output_type,
"output_content": output_content,
"execution_time": execution_time,
"grade": grade, # percent 0..100
"testcases": testcases, # list[dict] or None
"trace": block
})
return pd.DataFrame(rows)
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