⚡️ Speed up method Colormap._repr_png_ by 5%

[codeflash-ai]

📄 5% (0.05x) speedup for Colormap._repr_png_ in lib/matplotlib/colors.py

⏱️ Runtime : 17.1 milliseconds → 16.2 milliseconds (best of 9 runs)

📝 Explanation and details

The optimized code achieves a 5% speedup through three key optimizations that reduce computational overhead in the _repr_png_ method:

1. Efficient Array Creation with Broadcasting
The original code uses np.tile(np.linspace(...), ...) which creates a temporary array and then copies it multiple times. The optimized version replaces this with:

• np.linspace to create a 1D array once
• np.empty to preallocate the target shape
• Broadcasting assignment X[:] = x_lin to fill the array

This eliminates the memory allocation and copying overhead of np.tile, reducing both memory usage and CPU time for the array creation step.

2. Float32 Precision Optimization
Using dtype=np.float32 instead of the default float64 provides several benefits:

• 50% less memory usage for the arrays
• Faster numpy operations due to smaller data size
• Better cache locality
• Since the final output is converted to uint8 pixels anyway, the reduced precision has no impact on visual quality

3. Caching Expensive Property Access
The line profiler shows that accessing mpl.__version__ is surprisingly expensive (86% of runtime in the optimized version). The optimization caches this value in a local variable, avoiding repeated property lookups during string formatting.

4. Context Manager for BytesIO
Using with io.BytesIO() provides automatic resource cleanup and is a minor improvement in memory management.

The test results show consistent 2-9% improvements across various colormap sizes, with larger improvements on smaller colormaps (8-9%) and smaller but still meaningful gains on larger ones (2-3%). This suggests the optimizations are particularly effective for the array creation overhead, which becomes proportionally more significant for smaller datasets.

These optimizations maintain identical behavior and output while reducing computational overhead in what appears to be a utility function for generating PNG representations of colormaps.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import io

import numpy as np

# imports
import pytest
from matplotlib.colors import Colormap
from PIL import Image

# function to test (see above for full class definition, including _repr_png_)

# ---- UNIT TESTS FOR Colormap._repr_png_ ----


# Helper: Minimal subclass to provide a _lut and _init for testing
class DummyColormap(Colormap):
    def _init(self):
        # Simple grayscale LUT for testing: N+3 rows (for under, over, bad)
        self._lut = np.zeros((self.N + 3, 4), dtype=float)
        for i in range(self.N):
            gray = i / (self.N - 1) if self.N > 1 else 0.0
            self._lut[i] = (gray, gray, gray, 1.0)
        # Under/over colors
        self._lut[self.N] = (1.0, 0.0, 0.0, 1.0)  # under: red
        self._lut[self.N + 1] = (0.0, 1.0, 0.0, 1.0)  # over: green
        self._lut[self.N + 2] = (0.0, 0.0, 0.0, 0.0)  # bad: transparent
        self._isinit = True


@pytest.fixture
def colormap_256():
    # Standard 256-level grayscale colormap
    return DummyColormap("dummy_gray", N=256)


@pytest.fixture
def colormap_small():
    # Small colormap for edge tests
    return DummyColormap("tiny", N=2)


@pytest.fixture
def colormap_large():
    # Large colormap for large scale tests
    return DummyColormap("large", N=999)


# --- BASIC TEST CASES ---


def test_png_basic_validity(colormap_256):
    """Test that _repr_png_ returns valid PNG bytes that can be opened as an image."""
    codeflash_output = colormap_256._repr_png_()
    png_bytes = codeflash_output  # 1.05ms -> 1.01ms (4.12% faster)
    # Try to open as PNG image
    img = Image.open(io.BytesIO(png_bytes))


def test_png_metadata(colormap_256):
    """Test that PNG metadata includes expected fields and values."""
    codeflash_output = colormap_256._repr_png_()
    png_bytes = codeflash_output  # 1.01ms -> 951μs (6.30% faster)
    img = Image.open(io.BytesIO(png_bytes))
    meta = img.info
    # Author and Software should contain matplotlib version and url
    author = meta.get("Author")


def test_png_different_names():
    """Test that the PNG metadata Title/Description reflect the colormap name."""
    cmap = DummyColormap("foobar", N=10)
    codeflash_output = cmap._repr_png_()
    png_bytes = codeflash_output  # 906μs -> 881μs (2.78% faster)
    img = Image.open(io.BytesIO(png_bytes))
    meta = img.info


def test_png_basic_image_content(colormap_256):
    """Test that the PNG image visually encodes the colormap: left pixel is black, right is white."""
    codeflash_output = colormap_256._repr_png_()
    png_bytes = codeflash_output  # 961μs -> 945μs (1.78% faster)
    img = Image.open(io.BytesIO(png_bytes))
    # Convert to numpy array for pixel checks
    arr = np.array(img)


# --- EDGE TEST CASES ---


def test_png_tiny_colormap(colormap_small):
    """Test with a colormap of only 2 colors (N=2)."""
    codeflash_output = colormap_small._repr_png_()
    png_bytes = codeflash_output  # 888μs -> 821μs (8.09% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)
    # Only two unique RGB colors should appear (black and white)
    unique_colors = set(tuple(rgba[:3]) for row in arr for rgba in row)


def test_png_empty_colormap():
    """Test edge case: N=1 (degenerate colormap)."""
    cmap = DummyColormap("empty", N=1)
    codeflash_output = cmap._repr_png_()
    png_bytes = codeflash_output  # 881μs -> 811μs (8.61% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)
    # All pixels should be the same color (since only 1 color in LUT)
    first_pixel = tuple(arr[0, 0])


def test_png_non_ascii_name():
    """Test that non-ASCII colormap names are handled in metadata."""
    name = "cmap_μλΩ"
    cmap = DummyColormap(name, N=10)
    codeflash_output = cmap._repr_png_()
    png_bytes = codeflash_output  # 897μs -> 835μs (7.35% faster)
    img = Image.open(io.BytesIO(png_bytes))
    meta = img.info


def test_png_alpha_channel(colormap_256):
    """Test that alpha channel is always 255 for all pixels (fully opaque)."""
    codeflash_output = colormap_256._repr_png_()
    png_bytes = codeflash_output  # 959μs -> 926μs (3.58% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)


def test_png_handles_large_N():
    """Test that a large N colormap (close to 1000) works and is performant."""
    cmap = DummyColormap("largeN", N=999)
    codeflash_output = cmap._repr_png_()
    png_bytes = codeflash_output  # 1.26ms -> 1.23ms (2.28% faster)
    img = Image.open(io.BytesIO(png_bytes))


def test_png_handles_zero_N():
    """Test edge case: N=0 should produce an image with all 'under' color."""
    cmap = DummyColormap("zero", N=0)
    codeflash_output = cmap._repr_png_()
    png_bytes = codeflash_output  # 929μs -> 893μs (4.04% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)


def test_png_performance_large_colormap(colormap_large):
    """Test that _repr_png_ completes in reasonable time for large LUTs."""
    import time

    start = time.time()
    codeflash_output = colormap_large._repr_png_()
    png_bytes = codeflash_output  # 1.29ms -> 1.20ms (8.18% faster)
    elapsed = time.time() - start
    # Check image is valid
    img = Image.open(io.BytesIO(png_bytes))


def test_png_large_image_content(colormap_large):
    """Test that the leftmost and rightmost columns of a large LUT colormap are correct."""
    codeflash_output = colormap_large._repr_png_()
    png_bytes = codeflash_output  # 1.26ms -> 1.21ms (3.40% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)


def test_png_unique_colors_in_row(colormap_large):
    """Test that a row in the PNG image contains many unique colors for large N."""
    codeflash_output = colormap_large._repr_png_()
    png_bytes = codeflash_output  # 1.25ms -> 1.21ms (3.07% faster)
    img = Image.open(io.BytesIO(png_bytes))
    arr = np.array(img)
    # Count unique colors in the first row (should be >500 for 999-level grayscale)
    unique_colors = {tuple(rgb) for rgb in arr[0, :, :3]}


# --- DETERMINISM/REPEATABILITY ---


def test_png_deterministic_output(colormap_256):
    """Test that repeated calls to _repr_png_ produce identical PNG bytes."""
    codeflash_output = colormap_256._repr_png_()
    png1 = codeflash_output  # 963μs -> 918μs (4.88% faster)
    codeflash_output = colormap_256._repr_png_()
    png2 = codeflash_output  # 785μs -> 727μs (7.98% faster)


def test_png_deterministic_image_content(colormap_256):
    """Test that image content is the same between runs."""
    img1 = Image.open(
        io.BytesIO(colormap_256._repr_png_())
    )  # 987μs -> 943μs (4.65% faster)
    img2 = Image.open(
        io.BytesIO(colormap_256._repr_png_())
    )  # 780μs -> 713μs (9.42% faster)
    arr1 = np.array(img1)
    arr2 = np.array(img2)


# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-Colormap._repr_png_-mja0up1t and push.