yeiichi

#!/bin/zsh
# display-running-apps.sh
# Lists currently running GUI apps on macOS (like Command–L App Switcher)

osascript <<'EOF' | tr ',' '\n' | sed 's/^ *//; s/ *$//'
tell application "System Events"
	set app_list to (name of every process whose background only is false)
end tell
return app_list
EOF

yeiichi

#!/usr/bin/env bash
# ==============================================================================
#  make_django_docker_template_kit.sh
# ------------------------------------------------------------------------------
#  PURPOSE:
#    Automates the creation of a reusable **Django + Docker Compose** template kit.
#    Generates all directories and placeholder files needed to start a new
#    containerized Django project, including Docker, Compose, Makefile, and docs.
#
#  USAGE:

yeiichi

# language: Makefile
# ==============================================================================
#  Makefile.django-docker-helper
# ------------------------------------------------------------------------------
#  Helper Makefile for Django + Docker Compose development.
#
#  PURPOSE:
#    - Simplify common Django and Docker workflows.
#    - Provide short aliases for build, run, migrate, backup, test, and more.
#    - Encourage consistent developer operations across environments.

yeiichi

from typing import List, Tuple


def _reconstruct_subset(items: List[int], reachable_history: List[int], target_sum: int) -> List[int]:
    """
    Reconstruct one subset of `items` that achieves `target_sum` using the
    saved DP snapshots in `reachable_history`.
    """
    subset: List[int] = []
    cur_sum = target_sum

yeiichi

#!/usr/bin/env python3
"""
This module provides a class and utility functions to analyze and infer SQL
types for columns in CSV files via statistical and datatype examination.

It handles various datatypes like integers, floats, booleans, dates, and
timestamps while accommodating dialect-specific SQL type mappings. Additional
functionality includes updating statistics across data chunks, robustness
against missing or invalid data, and generation of NULL/NOT NULL constraints.
"""

yeiichi

#!/usr/bin/env python3
"""
A tool for classifying and moving CSV files based on column headers.

This script processes CSV files by matching their headers against predefined
layouts. Depending on the matched layout, the files are moved to their
corresponding destination directories. The script supports both file-based
and directory-based classification and offers options for dry-run execution,
recursion, and verbose output.

yeiichi

#!/usr/bin/env python3
"""
A program to concatenate Excel files in a directory.

This script combines all `.xlsx` files present in a specified directory, ensuring
they each have identical and consistent columns (order-sensitive). The combined
data is written into a single output Excel file. Additionally, duplicate rows are
dropped from the final result, and the index is reset. An output file name can
either be provided via the command-line arguments or auto-generated based on
the current timestamp.

yeiichi

#!/usr/bin/env python3
from __future__ import annotations

import argparse
from pathlib import Path

from openpyxl import load_workbook
from openpyxl.utils import get_column_letter
from openpyxl.workbook.workbook import Workbook
from openpyxl.worksheet.table import Table, TableStyleInfo

yeiichi

#!/usr/bin/env bash
# shellcheck shell=bash
# Bash wrapper to run Python scripts using a virtual environment.

set -Eeuo pipefail

# Constants
readonly HOME_DIR="${HOME}"
readonly VENV_DIR="${HOME_DIR}/path/to/venv"
readonly VENV_BIN="${VENV_DIR}/bin"

yeiichi

#!/usr/bin/env python3
import pandas as pd
import numpy as np

def normalize_missing(
    df: pd.DataFrame,
    targets=None,
    columns=None,
    output_dtype="object"
) -> pd.DataFrame: