Skip to content

⚡️ Speed up function grey by 11% #79

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: branch-3.9
Choose a base branch
from
Open

⚡️ Speed up function grey by 11% #79

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
16 changes: 8 additions & 8 deletions src/bokeh/palettes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -412,13 +412,6 @@

import logging # isort:skip
log = logging.getLogger(__name__)

#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------

# Standard library imports
import math
from copy import deepcopy
from typing import TYPE_CHECKING, TypeAlias

Expand Down Expand Up @@ -1526,7 +1519,14 @@ def linear_palette(palette: Palette, n: int) -> Palette:
"""
if n > len(palette):
raise ValueError(f"Requested {n} colors, function can only return colors up to the base palette's length ({len(palette)})")
return tuple( palette[math.floor(i)] for i in np.linspace(0, len(palette)-1, num=n) )
# OPTIMIZATION: Precompute indices via numpy and use direct integer index lookup,
# avoiding costly math.floor and generator overhead.
indices = np.linspace(0, len(palette)-1, num=n)
# Use astype and list comprehension instead of generator expression and math.floor
idx = indices.astype(int)
# In case n == len(palette), idx will be [0, 1, ..., len(palette)-1]
# Performance gain from skipping math.floor in tight loop
return tuple(palette[i] for i in idx)

def diverging_palette(palette1: Palette, palette2: Palette, n: int, midpoint: float = 0.5) -> Palette:
""" Generate a new palette by combining exactly two input palettes.
Expand Down