⚡️ Speed up function _get_colors_from_colormap by 443%

[codeflash-ai]

📄 443% (4.43x) speedup for _get_colors_from_colormap in pandas/plotting/_matplotlib/style.py

⏱️ Runtime : 55.4 milliseconds → 10.2 milliseconds (best of 61 runs)

📝 Explanation and details

The optimization achieves a 5.4x speedup by replacing scalar colormap evaluations with vectorized operations.

Key optimization: Instead of calling cmap(num) individually for each color value in a list comprehension ([cmap(num) for num in np.linspace(0, 1, num=num_colors)]), the optimized version:

1. Pre-computes the linspace values: vals = np.linspace(0, 1, num=num_colors)
2. Vectorizes colormap evaluation: res = cmap(vals) - matplotlib colormaps can process entire arrays at once
3. Preserves output format: [tuple(color) for color in res] maintains the original list-of-tuples return type

Why this is faster: The original code made num_colors separate function calls to the colormap, each processing a single scalar. Matplotlib colormaps internally use NumPy operations that are much more efficient when operating on arrays rather than individual values. The vectorized approach eliminates the Python loop overhead and leverages NumPy's optimized C implementations.

Performance impact: The optimization shows dramatic improvements for larger color counts - test cases with 1000 colors see 10-11x speedups (8.4ms → 0.68ms), while smaller cases see 20-50% improvements. Since _get_colors_from_colormap is called by _derive_colors in pandas plotting workflows, this optimization will benefit any plotting operation that generates color palettes from colormaps, especially when many colors are needed for complex visualizations.

Test case effectiveness: The optimization excels with larger num_colors values where the vectorization advantage is most pronounced, while maintaining correctness for edge cases like zero colors or error conditions.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 57 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	87.5%

🌀 Generated Regression Tests and Runtime

from __future__ import annotations

import matplotlib as mpl

# imports
import pytest  # used for our unit tests
from pandas.plotting._matplotlib.style import _get_colors_from_colormap

# unit tests

# ------ BASIC TEST CASES ------


def test_basic_with_builtin_colormap_name():
    # Test with a common colormap name and 5 colors
    codeflash_output = _get_colors_from_colormap("viridis", 5)
    colors = codeflash_output  # 182μs -> 149μs (21.8% faster)
    # Each color should be a tuple of length 4 (RGBA)
    for color in colors:
        # Each channel should be in [0, 1]
        for channel in color:
            pass


def test_basic_with_colormap_instance():
    # Test with a Colormap instance directly
    cmap = mpl.colormaps["plasma"]
    codeflash_output = _get_colors_from_colormap(cmap, 3)
    colors = codeflash_output  # 164μs -> 143μs (15.1% faster)
    for color in colors:
        for channel in color:
            pass


def test_basic_with_different_num_colors():
    # Test with different numbers of colors
    for n in [1, 2, 10]:
        codeflash_output = _get_colors_from_colormap("inferno", n)
        colors = codeflash_output  # 435μs -> 356μs (22.1% faster)
        for color in colors:
            pass


# ------ EDGE TEST CASES ------


def test_edge_zero_colors():
    # Test with num_colors=0 should return an empty list
    codeflash_output = _get_colors_from_colormap("magma", 0)
    colors = codeflash_output  # 28.3μs -> 138μs (79.6% slower)


def test_edge_one_color():
    # Test with num_colors=1 should return a list with one color
    codeflash_output = _get_colors_from_colormap("cividis", 1)
    colors = codeflash_output  # 157μs -> 146μs (7.04% faster)


def test_edge_float_num_colors():
    # Test with float num_colors should raise a TypeError from np.linspace
    with pytest.raises(TypeError):
        _get_colors_from_colormap("viridis", 3.5)  # 8.46μs -> 8.93μs (5.35% slower)


def test_edge_none_colormap():
    # Test with None as colormap should raise TypeError
    with pytest.raises(TypeError):
        _get_colors_from_colormap(None, 5)  # 36.9μs -> 38.1μs (3.18% slower)


def test_edge_extreme_color_channels():
    # Test that color channels are always in [0,1] even for edge colormaps
    codeflash_output = _get_colors_from_colormap("Greys", 10)
    colors = codeflash_output  # 379μs -> 307μs (23.5% faster)
    for color in colors:
        for channel in color:
            pass


# ------ LARGE SCALE TEST CASES ------


def test_large_scale_many_colors():
    # Test with a large number of colors (e.g. 1000)
    num_colors = 1000
    codeflash_output = _get_colors_from_colormap("viridis", num_colors)
    colors = codeflash_output  # 8.34ms -> 686μs (1115% faster)


def test_large_scale_all_builtin_colormaps():
    # Test with all built-in colormap names for a small num_colors
    for cmap_name in list(mpl.colormaps)[:20]:  # limit to first 20 for speed
        codeflash_output = _get_colors_from_colormap(cmap_name, 5)
        colors = codeflash_output  # 3.37ms -> 2.64ms (27.5% faster)
        for color in colors:
            pass


def test_large_scale_performance():
    # Test that generating 500 colors does not take excessive time
    import time

    start = time.time()
    codeflash_output = _get_colors_from_colormap("plasma", 500)
    colors = codeflash_output  # 4.26ms -> 428μs (895% faster)
    end = time.time()


def test_large_scale_consistency():
    # Test that repeated calls with the same arguments yield the same result
    codeflash_output = _get_colors_from_colormap("magma", 100)
    colors1 = codeflash_output  # 990μs -> 207μs (377% faster)
    codeflash_output = _get_colors_from_colormap("magma", 100)
    colors2 = codeflash_output  # 918μs -> 156μs (488% faster)


# ------ MISCELLANEOUS/ADDITIONAL CASES ------


def test_colormap_instance_equivalence():
    # Test that passing a colormap name and its instance yields the same result
    cmap_name = "viridis"
    cmap_instance = mpl.colormaps[cmap_name]
    codeflash_output = _get_colors_from_colormap(cmap_name, 10)
    colors_name = codeflash_output  # 224μs -> 146μs (52.9% faster)
    codeflash_output = _get_colors_from_colormap(cmap_instance, 10)
    colors_instance = codeflash_output  # 174μs -> 102μs (70.2% faster)

from __future__ import annotations

import matplotlib as mpl  # for colormap access

# imports
import pytest  # used for our unit tests
from pandas.plotting._matplotlib.style import _get_colors_from_colormap

# unit tests

# ----------- Basic Test Cases -----------


def test_basic_with_string_colormap_name():
    # Test with a known colormap name and a small number of colors
    codeflash_output = _get_colors_from_colormap("viridis", 3)
    colors = codeflash_output  # 194μs -> 175μs (10.7% faster)
    for color in colors:
        for channel in color:
            pass


def test_basic_with_colormap_instance():
    # Test with a Colormap instance directly
    cmap = mpl.colormaps["plasma"]
    codeflash_output = _get_colors_from_colormap(cmap, 5)
    colors = codeflash_output  # 187μs -> 152μs (23.2% faster)
    for color in colors:
        for channel in color:
            pass


def test_basic_num_colors_one():
    # Test with num_colors=1
    codeflash_output = _get_colors_from_colormap("inferno", 1)
    colors = codeflash_output  # 150μs -> 149μs (0.490% faster)
    color = colors[0]
    for channel in color:
        pass


def test_basic_num_colors_two():
    # Test with num_colors=2, should get start and end of colormap
    codeflash_output = _get_colors_from_colormap("magma", 2)
    colors = codeflash_output  # 166μs -> 151μs (10.4% faster)
    # First color should be colormap(0.0), last should be colormap(1.0)
    cmap = mpl.colormaps["magma"]


# ----------- Edge Test Cases -----------


def test_edge_num_colors_zero():
    # Test with num_colors=0, should return empty list
    codeflash_output = _get_colors_from_colormap("viridis", 0)
    colors = codeflash_output  # 38.8μs -> 159μs (75.6% slower)


def test_edge_num_colors_negative():
    # Test with negative num_colors, should raise ValueError from np.linspace
    with pytest.raises(ValueError):
        _get_colors_from_colormap("viridis", -5)  # 7.40μs -> 8.19μs (9.63% slower)


def test_edge_num_colors_float():
    # Test with float num_colors, should raise TypeError from np.linspace
    with pytest.raises(TypeError):
        _get_colors_from_colormap("viridis", 2.5)  # 6.26μs -> 6.56μs (4.51% slower)


def test_edge_colormap_is_none():
    # Passing None as colormap should raise TypeError
    with pytest.raises(TypeError):
        _get_colors_from_colormap(None, 3)  # 36.5μs -> 39.1μs (6.69% slower)


def test_edge_colormap_is_integer():
    # Passing an integer as colormap should raise TypeError
    with pytest.raises(TypeError):
        _get_colors_from_colormap(123, 3)  # 28.0μs -> 27.4μs (1.93% faster)


def test_edge_colormap_is_object():
    # Passing an unrelated object as colormap should raise TypeError
    class Dummy:
        pass

    with pytest.raises(TypeError):
        _get_colors_from_colormap(Dummy(), 3)  # 26.6μs -> 27.9μs (4.66% slower)


def test_large_scale_num_colors_1000():
    # Test with num_colors=1000, check length and sample values
    codeflash_output = _get_colors_from_colormap("plasma", 1000)
    colors = codeflash_output  # 8.47ms -> 725μs (1067% faster)
    # Check first, middle, last color for correct mapping
    cmap = mpl.colormaps["plasma"]
    mid_index = len(colors) // 2
    mid_val = mid_index / (len(colors) - 1)


def test_large_scale_all_unique_colors():
    # For large num_colors, all colors should be unique
    codeflash_output = _get_colors_from_colormap("viridis", 1000)
    colors = codeflash_output  # 8.43ms -> 687μs (1127% faster)
    # Convert to string for uniqueness check
    color_strs = [str(c) for c in colors]


def test_large_scale_performance():
    # Test that function completes in reasonable time for large input
    import time

    start = time.time()
    codeflash_output = _get_colors_from_colormap("inferno", 999)
    colors = codeflash_output  # 8.40ms -> 682μs (1130% faster)
    duration = time.time() - start


def test_large_scale_with_colormap_instance():
    # Large scale test with Colormap instance
    cmap = mpl.colormaps["magma"]
    codeflash_output = _get_colors_from_colormap(cmap, 999)
    colors = codeflash_output  # 8.34ms -> 673μs (1138% faster)


# ----------- Miscellaneous Functional Test Cases -----------


def test_functional_colormap_object_identity():
    # Passing the same colormap instance twice should yield same results
    cmap = mpl.colormaps["viridis"]
    codeflash_output = _get_colors_from_colormap(cmap, 10)
    colors1 = codeflash_output  # 231μs -> 155μs (48.3% faster)
    codeflash_output = _get_colors_from_colormap(cmap, 10)
    colors2 = codeflash_output  # 96.0μs -> 30.5μs (215% faster)


def test_functional_colormap_string_vs_instance_equivalence():
    # Passing string and instance should yield same results
    cmap = mpl.colormaps["plasma"]
    codeflash_output = _get_colors_from_colormap("plasma", 7)
    colors1 = codeflash_output  # 201μs -> 147μs (36.9% faster)
    codeflash_output = _get_colors_from_colormap(cmap, 7)
    colors2 = codeflash_output  # 152μs -> 101μs (50.1% faster)


def test_functional_rgba_values_are_in_bounds():
    # All RGBA channels should be within [0,1]
    codeflash_output = _get_colors_from_colormap("viridis", 10)
    colors = codeflash_output  # 222μs -> 147μs (50.4% faster)
    for color in colors:
        pass


def test_functional_colormap_output_type():
    # Output should always be a list of 4-tuples
    codeflash_output = _get_colors_from_colormap("plasma", 4)
    colors = codeflash_output  # 175μs -> 144μs (21.1% faster)
    for color in colors:
        pass


def test_functional_colormap_with_boundary_values():
    # Test that boundary values are mapped correctly
    cmap = mpl.colormaps["inferno"]
    codeflash_output = _get_colors_from_colormap("inferno", 2)
    colors = codeflash_output  # 158μs -> 140μs (12.4% faster)


# 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-_get_colors_from_colormap-mir1dgit and push.