Refactor Asteroidpy: Reduce Duplication & Verbosity

Hey guys! Today, we're diving deep into some exciting refactoring suggestions for the asteroidpy project. Our main goal? To reduce duplication, trim down verbosity, and make the codebase cleaner and more efficient. Let's break down these powerful improvements step by step.

1. Consolidating httpx_get and httpx_post into a Single Helper Function

In the realm of coding, duplication is often the enemy. When we find ourselves writing similar blocks of code multiple times, it’s a prime opportunity to refactor. In asteroidpy, the httpx_get and httpx_post functions share a lot of common logic. These functions are essential for making HTTP requests, which are fundamental for fetching data from various APIs and services. By streamlining these functions, we not only reduce the lines of code but also make the codebase easier to maintain and understand. Think of it as decluttering your workspace – a cleaner space leads to a clearer mind, and in coding, a cleaner codebase leads to fewer bugs and easier updates. The suggestion here is to collapse these two functions into a single, more versatile helper function named _httpx. This function will handle both GET and POST requests, reducing redundancy and improving code maintainability. The original approach used separate functions for GET and POST requests, leading to duplication of code. By consolidating these into a single function, we eliminate this duplication, making the codebase more concise and easier to manage. This not only saves time in the long run but also reduces the risk of introducing inconsistencies or bugs when updating the request logic. The proposed _httpx function takes a method parameter (either "get" or "post"), along with the URL, payload, and return type. It then uses this information to make the appropriate HTTP request. This eliminates the need for two separate functions, reducing code duplication and improving maintainability. It simplifies the process of making HTTP requests by abstracting away the specific details of GET and POST methods. This abstraction makes the code more readable and reduces the chances of errors. Moreover, it prepares the codebase for future enhancements, such as adding support for other HTTP methods, with minimal changes.

from typing import Any, Tuple, Literal

async def _httpx(
    method: Literal["get","post"],
    url: str,
    payload: dict[str,Any],
    return_type: Literal["json","text"],
) -> Tuple[Any,int]:
    async with httpx.AsyncClient() as client:
        send = getattr(client, method)
        r = await send(url, params=payload if method=="get" else None,
                            data=payload if method=="post" else None)
    body = r.json() if return_type=="json" else r.text
    return body, r.status_code

async def httpx_get(url: str, payload: dict[str,Any], return_type: Literal["json","text"]) -> Tuple[Any,int]:
    return await _httpx("get", url, payload, return_type)

async def httpx_post(url: str, payload: dict[str,Any], return_type: Literal["json","text"]) -> Tuple[Any,int]:
    return await _httpx("post", url, payload, return_type)

2. Optimizing neocp_confirmation for Efficiency

Let's talk about efficiency. In the original neocp_confirmation function, a one-row table is created and then a row is removed if there’s no data. This is like preparing a dish and then throwing it away if you're not hungry – it's wasteful! The refactoring suggestion here is to early-return an empty table if no data is available. This small change can significantly improve the function's performance, especially when dealing with large datasets or frequent calls. Imagine you're building a house. Would you start laying the foundation if you weren't sure you had the blueprints or the materials? Of course not! Similarly, in coding, we should avoid unnecessary computations and operations. By returning an empty table early, we prevent the function from doing extra work when it’s not needed. The revised function checks if the data is a list and, if not, immediately returns an empty table. This eliminates the overhead of creating and then removing a dummy row, which is particularly beneficial when the function is called frequently or when data is often unavailable. This optimization not only saves computational resources but also makes the code more readable and easier to follow. It reflects a best practice in software development: fail fast and avoid unnecessary work. The focus shifts from creating a table and then potentially removing a row to only creating the table when there is actual data to populate it. This approach aligns with the principle of writing lean and efficient code. Furthermore, the refactored code simplifies the logic for adding rows to the table. Instead of adding a dummy row and then removing it, the code now directly adds rows based on the data items. This streamlines the process and makes the code easier to understand and maintain.

def _empty_neocp_table() -> QTable:
    return QTable(
        names=('Temp_Desig','Score','R.A.','Decl','Alt','V','NObs','Arc','Not_seen'),
        dtype=(str,int,str,str,float,float,int,float,float),
        meta={'name': 'NEOcp confirmation'},
    )

def neocp_confirmation(...) -> QTable:
    configuration.load_config(config)
    data, _ = asyncio.run(httpx_get(...,"json"))
    table = _empty_neocp_table()
    if not isinstance(data, list):
        return table

    # now loop and add rows, no need to remove initial dummy
    for item in data:
        try:
            score = int(item['Score']); mag = float(item['V'])
        except (KeyError,ValueError):
            continue
        if score>min_score and mag<max_magnitude and is_visible(...):
            table.add_row([...])
    return table

3. Simplifying object_ephemeris with a Dictionary Lookup

Control flow is a crucial aspect of programming. The way we structure our if/else statements can significantly impact the readability and efficiency of our code. In the original object_ephemeris function, a stepped if/elif block is used to determine the time step for ephemeris calculations. This can become cumbersome and hard to read as the number of steps increases. The suggestion is to replace this stepped structure with a simple dictionary lookup. A dictionary provides a clean and efficient way to map stepping strings to their corresponding values. This not only makes the code more readable but also makes it easier to add or modify steps in the future. Imagine you have a map that tells you exactly where to go based on a simple instruction. That’s what a dictionary lookup does for your code – it provides a direct and efficient way to find the right value based on a key. By using a dictionary, we transform a complex decision-making process into a straightforward lookup operation. This not only simplifies the code but also improves its performance. The _STEP dictionary maps stepping strings (like 'm' for minutes, 'h' for hours) to their corresponding time units. This allows the object_ephemeris function to quickly determine the appropriate step value based on the input. The get method is used to retrieve the value from the dictionary, with a default value of '1h' if the stepping string is not found. This ensures that the function always has a valid step value, even if the input is unexpected. This approach not only simplifies the code but also makes it more robust and easier to maintain. Adding or modifying steps is as simple as adding or modifying entries in the dictionary, without the need to change the control flow logic.

_STEP: dict[str,Any] = {
    'm': 1*u.minute,
    'h': '1h',
    'd': '1d',
    'w': '7d',
}

def object_ephemeris(..., stepping: str) -> QTable:
    configuration.load_config(config)
    # ...
    step = _STEP.get(stepping, '1h')  # default 1h
    eph = MPC.get_ephemeris(..., step=step, number=30)
    return eph['Date','RA','Dec','Elongation','V','Altitude','Proper motion','Direction']

Conclusion: Streamlining for Success

These refactoring suggestions aim to eliminate duplicated control flow, remove unnecessary cast[…], and maintain the exact same external behavior. By implementing these changes, we can make the asteroidpy codebase more maintainable, readable, and efficient. Remember, clean code is happy code, and happy code leads to fewer headaches down the road! These changes demonstrate the power of refactoring in improving the quality and maintainability of code. By addressing issues such as code duplication, verbosity, and complex control flow, we can create a codebase that is easier to understand, modify, and extend. This is crucial for the long-term success of any software project. In summary, refactoring is not just about making code shorter; it’s about making it smarter, more efficient, and more robust. It's an ongoing process that helps us keep our code in top shape and ready for whatever challenges come our way. So, let’s embrace these suggestions and continue to make asteroidpy the best it can be!