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question:**Problem Statement:** The Python `time` module offers various functions that assist in manipulating and converting times. Your task is to write a utility that takes a list of strings representing date-times in different formats and returns a list of date-times converted into a standardized format. Additionally, your utility should include a function to measure the time taken to process this list. # Requirements: 1. **`convert_date_times(date_time_strings: List[str], date_format: str) -> List[str]`:** - **Input:** - `date_time_strings`: A list of strings where each string represents a date-time in a different format. - `date_format`: A string that specifies the desired output format for the date-times (e.g., `"%Y-%m-%d %H:%M:%S"`). - **Output:** - A list of strings where each string represents a date-time converted to the `date_format`. - **Constraints:** - The input date-times could be in various formats, and the function should be flexible enough to parse them correctly. - If a string cannot be parsed, it should be skipped (not included in the output list). 2. **`measure_conversion_time(date_time_strings: List[str], date_format: str) -> float`:** - **Input:** - `date_time_strings`: A list of strings where each string represents a date-time in a different format. - `date_format`: A string that specifies the desired output format for the date-times. - **Output:** - A floating-point number representing the time taken (in seconds) to convert all date-times using the `convert_date_times` function. - **Constraints:** - This function should utilize high-resolution timers provided by the `time` module to measure the time taken for the conversion. # Example Usage: ```python from typing import List import time def convert_date_times(date_time_strings: List[str], date_format: str) -> List[str]: converted_dates = [] for dt_str in date_time_strings: for fmt in ["%Y-%m-%d %H:%M:%S", "%d-%m-%Y %H:%M:%S", "%m/%d/%Y %I:%M %p"]: try: dt = time.strptime(dt_str, fmt) converted_dates.append(time.strftime(date_format, dt)) break except ValueError: continue return converted_dates def measure_conversion_time(date_time_strings: List[str], date_format: str) -> float: start_time = time.perf_counter() convert_date_times(date_time_strings, date_format) end_time = time.perf_counter() return end_time - start_time # Example: date_time_strings = ["2023-10-01 13:45:30", "01-10-2023 14:50:15", "10/01/2023 01:55 PM"] date_format = "%Y-%m-%d %H:%M:%S" converted_dates = convert_date_times(date_time_strings, date_format) print(converted_dates) conversion_time = measure_conversion_time(date_time_strings, date_format) print(f"Time taken for conversion: {conversion_time} seconds") ``` # Note: - Include comprehensive error handling in your implementation. - Your solution should be efficient and make use of appropriate functions from the `time` module as described in the documentation provided.
answer:from typing import List import time def convert_date_times(date_time_strings: List[str], date_format: str) -> List[str]: Converts a list of date-time strings into a standardized format. :param date_time_strings: List of strings representing date-times in various formats. :param date_format: The desired output format for the date-times. :return: A list of strings where each string represents a date-time converted to the `date_format`. converted_dates = [] for dt_str in date_time_strings: for fmt in ["%Y-%m-%d %H:%M:%S", "%d-%m-%Y %H:%M:%S", "%m/%d/%Y %I:%M %p"]: try: dt = time.strptime(dt_str, fmt) converted_dates.append(time.strftime(date_format, dt)) break except ValueError: continue return converted_dates def measure_conversion_time(date_time_strings: List[str], date_format: str) -> float: Measures the time taken to convert a list of date-time strings into a standardized format. :param date_time_strings: List of strings representing date-times in various formats. :param date_format: The desired output format for the date-times. :return: Time taken (in seconds) to convert all date-times using the `convert_date_times` function. start_time = time.perf_counter() convert_date_times(date_time_strings, date_format) end_time = time.perf_counter() return end_time - start_time
question:**Question: Optimize a Batch Processing System with String Templating, Binary Data Handling, and Multi-threading** You are tasked with designing and implementing a batch processing system that will handle large datasets. This system must: 1. Use templating to generate consistent file names for processed output files. 2. Efficiently manage binary data while reading and writing files. 3. Utilize multi-threading to process multiple datasets in parallel for improved performance. # Requirements: 1. **Data Handling**: Utilize the `struct` module to read and write binary data. Each dataset is represented as a binary file consisting of records with the format: - `record_id` (4-byte unsigned int) - `value` (4-byte float) 2. **Templating**: Implement a feature to generate output file names using template strings based on a format provided by the user. The template should support placeholders for: - Record ID (`record_id`) - Date (`date`) - Original file name (`orig_name`) 3. **Multi-threading**: Employ the `threading` module to process multiple datasets concurrently. Ensure that the main program continues to function while the datasets are being processed in the background. # Input and Output: - **Input**: - List of binary dataset file paths. - Output file naming template provided by user. - Directory to save processed files. - **Output**: - Processed binary files saved in the specified directory with names generated based on the provided template. # Constraints: - Each record's `value` is doubled before saving to the output file. - Ensure thread-safe operations when handling file reads/writes. - Use appropriate synchronization mechanisms to handle shared resources. # Performance: - The system should efficiently process large datasets with minimal delay, leveraging multi-threading for performance. - Ensure the system scales well with the increasing number of datasets and handles large binary files properly. # Example: 1. **Input**: - File paths: `["dataset1.bin", "dataset2.bin"]` - Naming template: `"processed_%date%_%record_id%_%orig_name%"` - Output directory: `"output/"` 2. **Processing**: - Read each record from the input binary files. - Double the `value` for each record. - Write the modified record to a new binary file with the name generated from the template string (`date`, `record_id`, and `orig_name` included). 3. **Output**: Assuming today's date is "25Oct23", for a sample record from `dataset1.bin` with `record_id` 101 and `value` 25.5, the output file name could be `"processed_25Oct23_101_dataset1.bin"`. # Implementation: Implement the following functions: 1. **generate_filename**(template: str, record_id: int, date: str, orig_name: str) -> str: - Generates a file name based on the provided template and placeholders. 2. **process_dataset**(input_file: str, output_dir: str, template: str): - Reads records from the binary input file, processes each record, and writes to an output file with the name generated by `generate_filename`. 3. **batch_process**(files: List[str], output_dir: str, template: str): - Manages the multi-threading to process multiple datasets concurrently. # Notes: - Ensure that you handle exceptions properly, especially for file I/O operations. - Make use of synchronization primitives from `threading` module to ensure thread safety.
answer:import os from datetime import datetime import threading import struct def generate_filename(template: str, record_id: int, date: str, orig_name: str) -> str: Generates a filename based on a given template. template: str - The template string containing placeholders for record_id, date, and orig_name. record_id: int - The ID of the record. date: str - The date string to be included in the filename. orig_name: str - The original file name. Returns: str - The generated file name. filename = template.replace("%record_id%", str(record_id)) filename = filename.replace("%date%", date) filename = filename.replace("%orig_name%", orig_name) return filename def process_dataset(input_file: str, output_dir: str, template: str): Processes a single dataset file. input_file: str - The path to the input binary dataset file. output_dir: str - The directory where the output files should be saved. template: str - The template string for naming the output files. date_str = datetime.now().strftime("%d%b%y") orig_name = os.path.basename(input_file) with open(input_file, "rb") as f: while chunk := f.read(8): # Each record is 8 bytes long record_id, value = struct.unpack('If', chunk) value *= 2 # Double the value output_filename = generate_filename(template, record_id, date_str, orig_name) output_path = os.path.join(output_dir, output_filename) with open(output_path, "ab") as outf: outf.write(struct.pack('If', record_id, value)) def batch_process(files, output_dir, template): Processes multiple dataset files concurrently using multi-threading. files: List[str] - The list of dataset file paths. output_dir: str - The directory where the output files should be saved. template: str - The template string for naming the output files. threads = [] for input_file in files: thread = threading.Thread(target=process_dataset, args=(input_file, output_dir, template)) threads.append(thread) thread.start() for thread in threads: thread.join()
question:**Objective**: Utilize the `atexit` module to manage resource cleanup and demonstrate understanding of function registration and argument passing. Problem Statement You are tasked with creating a logging system for a Python application that logs messages to a file. The log file should be closed properly upon the normal termination of the program. Additionally, provide functionality to dynamically register multiple cleanup functions with different priorities and arguments. Requirements 1. **Log Manager Class**: - Create a `LogManager` class with the following methods: - `__init__(self, filename)`: - Initializes the log manager with the given log file name. - Opens the file for writing. - `log(self, message)`: - Logs the provided message to the file. - Adds a timestamp to each log entry. - `close(self)`: - Closes the log file properly. - Handles any exceptions that might occur. 2. **Dynamic Cleanup Registration**: - Implement a function `register_cleanup(func, *args, **kwargs)` that registers cleanup functions using `atexit.register()` with optional arguments. - The function should allow registering the same function multiple times with different arguments. - Ensure all registered functions are called in the reverse order of registration at program termination. 3. **Usage**: - Create an instance of `LogManager` and register its `close` method as a cleanup function. - Register at least two additional cleanup functions with different priorities and arguments and demonstrate their execution order at interpreter termination. Input and Output - **Input**: No direct input from the user is needed. - **Output**: The log file should contain the logged messages with timestamps. The terminal should print messages indicating the cleanup functions' execution order. Constraints - The log file operations should handle file-related exceptions gracefully. - Functions registered without arguments should be demonstrated using `atexit.register` as a decorator. Example Execution ```python # Sample usage demonstrating the expected functionality. if __name__ == "__main__": log_manager = LogManager("app.log") log_manager.log("Application started") register_cleanup(log_manager.close) # Register log file close method def goodbye(name): print(f"Goodbye, {name}!") register_cleanup(goodbye, "Alice") register_cleanup(goodbye, "Bob") @atexit.register def farewell(): print("Farewell, the program is ending.") log_manager.log("Application running") log_manager.log("Application terminating") # Expected log file content: # [Timestamp] Application started # [Timestamp] Application running # [Timestamp] Application terminating # Expected terminal output at program termination: # Farewell, the program is ending. # Goodbye, Bob! # Goodbye, Alice! ``` Implement the `LogManager` class and `register_cleanup` function to meet the requirements.
answer:import atexit import datetime class LogManager: def __init__(self, filename): self.filename = filename self.file = None try: self.file = open(self.filename, 'w') except Exception as e: print(f"Failed to open log file {self.filename}: {e}") def log(self, message): if self.file: timestamp = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') try: self.file.write(f"[{timestamp}] {message}n") except Exception as e: print(f"Failed to write to log file: {e}") def close(self): if self.file: try: self.file.close() self.file = None except Exception as e: print(f"Failed to close log file: {e}") def register_cleanup(func, *args, **kwargs): atexit.register(func, *args, **kwargs) if __name__ == "__main__": log_manager = LogManager("app.log") log_manager.log("Application started") register_cleanup(log_manager.close) # Register log file close method def goodbye(name): print(f"Goodbye, {name}!") register_cleanup(goodbye, "Alice") register_cleanup(goodbye, "Bob") @atexit.register def farewell(): print("Farewell, the program is ending.") log_manager.log("Application running") log_manager.log("Application terminating")
question:# Assessing Unix Shadow Password Database Interaction using `spwd` Module Objective: You are required to demonstrate your understanding of the `spwd` module in Python to interact with the Unix shadow password database. Your task involves writing functions to retrieve specific information and handle potential exceptions or privilege issues. Problem Statement: 1. **Function: `get_user_shadow_info(username: str) -> dict`**: Implement a function that: - Takes a username as input. - Returns a dictionary with the user's shadow password information. The keys should correspond to the attribute names (`sp_namp`, `sp_pwdp`, `sp_lstchg`, etc.), and the values should be the respective data for those attributes. - Raises a `PermissionError` if the user does not have the necessary privileges to access the shadow password database. - Raises a `KeyError` if the username does not exist in the database. 2. **Function: `list_expired_accounts(expiration_date: int) -> List[str]`**: Implement a function that: - Takes an integer `expiration_date` representing the number of days since 1970-01-01 (Unix epoch). - Returns a list of usernames whose accounts have expired as of the given expiration date or earlier. - Handles any potential permission issues by raising a `PermissionError`. Input Constraints: - `username`: A string representing a valid Unix username. - `expiration_date`: An integer that denotes days since the Unix epoch. Output Format: - `get_user_shadow_info`: Returns a dictionary with keys `sp_namp`, `sp_pwdp`, `sp_lstchg`, `sp_min`, `sp_max`, `sp_warn`, `sp_inact`, `sp_expire`, `sp_flag`. - `list_expired_accounts`: Returns a list of usernames as strings. Example: ```python # Example usage: try: user_info = get_user_shadow_info("john") print(user_info) except PermissionError: print("Permission Denied. Please run as root.") except KeyError: print("Username not found.") try: expired_users = list_expired_accounts(19000) # Jan 1, 2022 print(expired_users) except PermissionError: print("Permission Denied. Please run as root.") ``` Notes: 1. Ensure your code handles permission and existence-related exceptions gracefully. 2. Consider edge cases where no accounts might be expired. 3. Properly document your code for clarity. ```python import spwd def get_user_shadow_info(username: str) -> dict: # Your implementation here pass def list_expired_accounts(expiration_date: int) -> List[str]: # Your implementation here pass ```
answer:import spwd from typing import List def get_user_shadow_info(username: str) -> dict: try: user_info = spwd.getspnam(username) return { 'sp_namp': user_info.sp_namp, 'sp_pwdp': user_info.sp_pwdp, 'sp_lstchg': user_info.sp_lstchg, 'sp_min': user_info.sp_min, 'sp_max': user_info.sp_max, 'sp_warn': user_info.sp_warn, 'sp_inact': user_info.sp_inact, 'sp_expire': user_info.sp_expire, 'sp_flag': user_info.sp_flag } except PermissionError: raise PermissionError("Permission Denied. Please run as root.") except KeyError: raise KeyError(f"Username {username} not found.") def list_expired_accounts(expiration_date: int) -> List[str]: try: expired_users = [] all_users = spwd.getspall() for user in all_users: if user.sp_expire != -1 and user.sp_expire <= expiration_date: expired_users.append(user.sp_namp) return expired_users except PermissionError: raise PermissionError("Permission Denied. Please run as root.")