SAWADA Takahiro / Gugen Koubou LLC fanannan

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Help the repository owner identify potential issues, inconsistencies, and areas for improvement
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The LLM should walk through all directories recursively from the specified starting path or $ARGUMENTS

	/*!
	* schema-model.js
	* (c) Gugen Koubou 2025 / MIT License
	*
	* Self-contained immutable dictionary with embedded schema (Strict & Clojure-inspired)
	* Plain JavaScript / Zero dependencies / Single file / UMD / Browser & Node.js
	*
	* ──────────────────────────────────────────────────────────────────────────────
	* Purpose
	* - Embeds "hidden schema" in a dictionary for strict validation on all updates

	"""
	# Django モデルインスタンス複製ツール - ドキュメント

	## 使い方

	```python
	# インポート方法
	from your_app.utils import duplicate_instance

	# 基本的な使用方法（すべての関連オブジェクトを複製する）

	import queue
	from typing import Callable, Any, Optional, List
	import threading
	import time
	import atexit
	import logging
	import weakref


	class FunctionExecutor:

	; Clojupyter Snippet
	(require '[clojupyter.misc.helper :as helper])
	(helper/add-dependencies '[cnuernber/libpython-clj "1.36"])
	(require '[libpython-clj.require :refer [require-python]]
	'[libpython-clj.python :refer [py. py.. py.- $. $.. $a $c
	as-python as-jvm
	->python ->jvm
	get-attr call-attr call-attr-kw
	get-item att-type-map
	call call-kw initialize!

	; not fast, just straitfoward
	def make_heatmap_data(xs, ys, zs, func, cutoff, num_grids, min_samples):
	xmin = np.nanpercentile(xs, cutoff*100)
	xmax = np.nanpercentile(xs, (1-cutoff)*100)
	ymin = np.nanpercentile(ys, cutoff*100)
	ymax = np.nanpercentile(ys, (1-cutoff)*100)
	xm = (xmax-xmin)/num_grids
	ym = (ymax-ymin)/num_grids
	r = [[list() for _ in range(num_grids)] for _ in range(num_grids)]
	for x, y, z in zip(xs, ys, zs):

	# coding: utf-8

	import os
	import shutil
	import pandas as pd
	import numpy as np
	import math
	import quandl
	from fastai.structured import add_datepart, proc_df
	from fastai.column_data import ColumnarModelData

	#!/usr/bin/env python
	# -- coding: utf-8 --

	from zipline.data.bundles import register
	from zipline.data.bundles.yahoo_csv import yahoo_csv_equities # yahoo_csv.py need to be placed in zipline.data.bundles

	tickers = {
	"SPY", "VXX", "VXZ"
	}
	register(

	# supplimental fix to pandas-datareader-'0.4.1' (not yet pulled proposal)
	#
	#
	#$ git clone https://github.com/rgkimball/pandas-datareader
	#$ cd pandas-datareader
	#$ git checkout fix-yahoo
	#$ pip install -e .

	def fetch_yahoo(ticker):
	NUM_RETRY = 5