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Coding workflows · Width-threshold image selection
03
A concrete starting point
Select among one to 64 URL-width pairs using a display width of 1–10000 and density ratio from 1/4 to 8. Test exact thresholds and largest-candidate fallback, rejecting duplicate widths or invalid numbers without fetching the selected URL.
Define the change
Describe the input, expected output and boundaries that must stay intact.
Inspect the work
Review the proposed edits and any commands before relying on their result.
Check the outcome
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Width-threshold image selection
A concrete starting point
import math
def image_candidate(candidates, width, ratio):
if type(width) is not int or not 1 <= width <= 10000 or type(ratio) not in (int, float) or not 0.25 <= ratio <= 8 or not math.isfinite(ratio):
raise ValueError("image.viewport")
if type(candidates) is not list or not 1 <= len(candidates) <= 64:
raise ValueError("image.candidates")
sizes, seen = [], set()
for pair in candidates:
if type(pair) not in (list, tuple) or len(pair) != 2:
raise ValueError("image.pair")
url, pixels = pair
if type(url) is not str or not 1 <= len(url) <= 2048 or any(ord(c) < 32 or ord(c) == 127 for c in url):
raise ValueError("image.url")
if type(pixels) is not int or not 1 <= pixels <= 100000 or pixels in seen:
raise ValueError("image.size")
seen.add(pixels)
sizes.append((pixels, url))
sizes.sort()
target = width*ratio
return next((url for pixels, url in sizes if pixels >= target), sizes[-1][1])
The example is a starting point for a real project, not a claim that generated code is already tested. Adjust paths, dependencies and expected behavior to match your application.