Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
日本語の概要は準備中です。原文の説明を表示しています。
Redact text from PDF documents for blind review anonymization
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Redact identifying information from academic papers for blind review.
# ❌ WRONG - This removes ALL text from the page:
for block in page.get_text("blocks"):
page.add_redact_annot(fitz.Rect(block[:4]))
# ❌ WRONG - Drawing rectangles over text:
page.draw_rect(fitz.Rect(0, 0, 600, 100), fill=(0,0,0))
# ✅ CORRECT - Only redact specific search matches:
for rect in page.search_for("John Smith"):
page.add_redact_annot(rect)
IMPORTANT: Use FULL names/phrases, not partial matches!
*@*.edu, *@*.comarXiv:XXXX.XXXXX10.XXXX/...import fitz
import os
def redact_with_pymupdf(input_path: str, output_path: str, patterns: list[str]):
"""Redact specific patterns from PDF using PyMuPDF."""
doc = fitz.open(input_path)
original_len = sum(len(p.get_text()) for p in doc)
# Find References page - stop redacting there
references_page = None
for i, page in enumerate(doc):
if "references" in page.get_text().lower():
references_page = i
break
for page_num, page in enumerate(doc):
if references_page is not None and page_num >= references_page:
continue # Skip References section
for pattern in patterns:
# ONLY redact exact search matches
for rect in page.search_for(pattern):
page.add_redact_annot(rect, fill=(0, 0, 0))
page.apply_redactions()
os.makedirs(os.path.dirname(output_path), exist_ok=True)
doc.save(output_path)
doc.close()
# MUST verify after saving
verify_redaction(input_path, output_path)
Always run this after ANY redaction to catch errors early:
import fitz
def verify_redaction(original_path, output_path):
"""Verify redaction didn't corrupt the PDF."""
orig = fitz.open(original_path)
redc = fitz.open(output_path)
orig_len = sum(len(p.get_text()) for p in orig)
redc_len = sum(len(p.get_text()) for p in redc)
print(f"Original: {len(orig)} pages, {orig_len} chars")
print(f"Redacted: {len(redc)} pages, {redc_len} chars")
print(f"Retained: {redc_len/orig_len:.1%}")
# DEFENSIVE CHECKS - fail fast if something went wrong
if len(redc) != len(orig):
raise ValueError(f"Page count changed: {len(orig)} -> {len(redc)}")
if redc_len < 1000:
raise ValueError(f"PDF corrupted: only {redc_len} chars remain!")
if redc_len < orig_len * 0.7:
raise ValueError(f"Too much removed: kept only {redc_len/orig_len:.0%}")
orig.close()
redc.close()
print("✓ Verification passed")
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
日本語の概要は準備中です。原文の説明を表示しています。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
日本語の概要は準備中です。原文の説明を表示しています。
Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze failed GitHub Action jobs for a pull request.
日本語の概要は準備中です。原文の説明を表示しています。
Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
日本語の概要は準備中です。原文の説明を表示しています。
Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.
日本語の概要は準備中です。原文の説明を表示しています。