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- Implemented bandwidth sensitivity and power sensitivity plots. - Created a contour map for bit rate multiplying factors. - Added input parameters for C/N and bandwidth with validation. - Displayed computed results and sensitivity analysis metrics. - Integrated interactive graphs for user exploration. - Included background information section for user guidance.
108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
"""
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Database module for managing user contributions.
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Uses parameterized queries (no SQL injection) and context managers.
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"""
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import sqlite3
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import os
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from datetime import datetime
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DB_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data")
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def _get_db_path(db_name: str) -> str:
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os.makedirs(DB_DIR, exist_ok=True)
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return os.path.join(DB_DIR, db_name)
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def _init_db(db_path: str):
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with sqlite3.connect(db_path) as conn:
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conn.execute(
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"""CREATE TABLE IF NOT EXISTS contributions (
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num INTEGER PRIMARY KEY,
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name TEXT NOT NULL,
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title TEXT NOT NULL,
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keywords TEXT,
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text TEXT NOT NULL,
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date TEXT NOT NULL,
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password TEXT DEFAULT ''
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)"""
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)
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def write_contribution(
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db_name: str, name: str, title: str, keywords: str, text: str, password: str = ""
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) -> int:
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"""Write a new contribution. Returns the new contribution ID."""
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db_path = _get_db_path(db_name)
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_init_db(db_path)
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with sqlite3.connect(db_path) as conn:
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cursor = conn.cursor()
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cursor.execute("SELECT COALESCE(MAX(num), 0) FROM contributions")
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next_id = cursor.fetchone()[0] + 1
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cursor.execute(
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"INSERT INTO contributions (num, name, title, keywords, text, date, password) "
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"VALUES (?, ?, ?, ?, ?, ?, ?)",
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(next_id, name, title, keywords, text, datetime.now().strftime("%Y-%m-%d"), password),
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)
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return next_id
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def search_contributions(
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db_name: str,
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name_filter: str = "",
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title_filter: str = "",
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keywords_filter: str = "",
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content_filter: str = "",
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limit: int = 50,
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) -> list[dict]:
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"""Search contributions with optional filters. Returns list of dicts."""
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db_path = _get_db_path(db_name)
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if not os.path.isfile(db_path):
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return []
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_init_db(db_path)
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with sqlite3.connect(db_path) as conn:
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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cursor.execute(
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"""SELECT num, name, title, keywords, text, date, password
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FROM contributions
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WHERE name LIKE ? AND title LIKE ? AND keywords LIKE ? AND text LIKE ?
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ORDER BY num DESC
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LIMIT ?""",
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(
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f"%{name_filter}%",
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f"%{title_filter}%",
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f"%{keywords_filter}%",
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f"%{content_filter}%",
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limit,
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),
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)
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return [dict(row) for row in cursor.fetchall()]
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def delete_contribution(db_name: str, num: int, password: str) -> bool:
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"""Delete a contribution if the password matches. Returns True on success."""
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db_path = _get_db_path(db_name)
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if not os.path.isfile(db_path):
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return False
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with sqlite3.connect(db_path) as conn:
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cursor = conn.cursor()
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cursor.execute(
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"SELECT password FROM contributions WHERE num = ?", (num,)
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)
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row = cursor.fetchone()
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if row is None:
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return False
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if row[0] != password:
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return False
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cursor.execute("DELETE FROM contributions WHERE num = ?", (num,))
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return True
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