Home/ Questions/Q 16746
Next
In Process

askthedev.com Latest Questions

Asked: 2024-09-27T11:46:08+05:30 In:

How to Efficiently Filter 2D Points within a Distance Using NumPy in Python?

I’ve been diving into coding challenges lately, and I stumbled upon this fascinating idea of “code golfing” using Python libraries like NumPy and SciPy. For those unfamiliar, code golfing is all about solving programming problems with the least number of characters in your source code. So, I’m looking to get a bit creative here and thought it would be fun to pose a challenge to the community!

Here’s the scenario: let’s say we’re working with a massive dataset of points in a 2D space – think of it like a scatter plot with two coordinates, x and y. Your task is to implement a function that calculates the distance from the origin (0, 0) to each of these points and return only the points that fall within a specific distance threshold, say, within a radius of 5 units away from the origin.

But let’s add a twist, shall we? You need to do this in the most character-efficient way possible while leveraging NumPy. Now, keep in mind that we can rely on NumPy’s built-in functionalities to simplify our calculations. Your final output should be a list of the filtered points, and you obviously want to keep your character count as low as possible.

To give an example, consider the points: [(1, 2), (3, 4), (5, 1), (0, 0), (4, 4), (-3, -4)]. You would calculate the distance for each point and only return those within the 5-unit threshold.

A few things to bear in mind: you have to think about how to input your data efficiently, perhaps using NumPy arrays, and also consider how you handle the distance calculation neatly. It sounds straightforward, but packing it all in as few characters as possible while maintaining readability can be tricky!

I’m eager to see how creative everyone can get with this! What clever solutions can you come up with? Any unique techniques or shortcuts in NumPy that you think might work well? Let’s see those golfing skills in action!

Share

    Leave an answer

    You must login to add an answer.

    Need An Account,

    2 Answers

    1. import numpy as np

f = lambda p: p[np.linalg.norm(p,axis=1)<=5]
points = np.array([(1,2),(3,4),(5,1),(0,0),(4,4),(-3,-4)])
result = f(points)
print(result)

    2. Here’s a compact implementation of the requested functionality using NumPy. This one-liner function calculates the distance of each point from the origin and filters out those that fall within the specified threshold. By utilizing broadcasting and boolean indexing in NumPy, we can achieve this in an efficient manner:

      import numpy as np

def filter_points(points): return points[np.linalg.norm(points, axis=1) <= 5]

      To exemplify the function, we can create a NumPy array from the given points and invoke the `filter_points` function:

      points = np.array([(1, 2), (3, 4), (5, 1), (0, 0), (4, 4), (-3, -4)])
filtered = filter_points(points)
print(filtered)

    Related Questions