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@codeflash-ai codeflash-ai bot commented Nov 1, 2025

📄 34% (0.34x) speedup for _Distplot.make_normal in plotly/figure_factory/_distplot.py

⏱️ Runtime : 28.1 milliseconds 21.0 milliseconds (best of 220 runs)

📝 Explanation and details

The optimized code achieves a 33% speedup by reducing attribute access overhead and optimizing mathematical computations.

Key optimizations:

  1. Local variable caching: The optimized version pulls frequently accessed instance attributes (self.histnorm, self.bin_size, etc.) into local variables at the start of the method. This eliminates repeated attribute lookups during loop execution, which is particularly beneficial since Python's attribute access has overhead.

  2. Function reference caching: scipy_stats.norm.fit and scipy_stats.norm.pdf are cached as local variables (norm_fit, norm_pdf) to avoid repeated module attribute lookups in the tight loop.

  3. Optimized x-coordinate generation: Instead of the original list comprehension that repeatedly accessed self.start[index] and self.end[index], the optimized version pre-computes step = (e0 - s0) / 500 and uses local variables, reducing arithmetic operations per iteration.

  4. Vectorized operations: The optimized code leverages NumPy's vectorized multiplication when histnorm == ALTERNATIVE_HISTNORM, operating on the entire array y *= bin_size[index] instead of element-wise operations.

Performance impact by test case:

  • Large-scale scenarios see the biggest gains (36-37% faster) when processing many traces, as the attribute access overhead compounds
  • Basic cases with single/few traces still benefit (19-30% faster) from reduced overhead
  • Edge cases with identical values or single values see 23-25% improvements

The optimizations are particularly effective for the common use case of processing multiple statistical distributions, where the nested loops amplify the benefits of reduced attribute access overhead.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 63 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 2 Passed
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import math

# imports
import pytest  # used for our unit tests
from plotly.figure_factory._distplot import _Distplot


# function to test
class DummyScipyStatsNorm:
    """Dummy scipy.stats.norm for testing purposes."""
    @staticmethod
    def fit(data):
        # Fit normal: mean and stddev
        n = len(data)
        if n == 0:
            raise ValueError("No data points to fit.")
        mean = sum(data) / n
        # sample standard deviation
        if n == 1:
            sd = 0.0
        else:
            sd = math.sqrt(sum((x - mean) ** 2 for x in data) / n)
        return mean, sd

    @staticmethod
    def pdf(xs, loc, scale):
        # Return the PDF values for each x in xs for N(loc, scale)
        if scale == 0:
            # Dirac delta at loc; all xs except loc get 0.0, loc gets inf
            return [float('inf') if x == loc else 0.0 for x in xs]
        sqrt_2pi = math.sqrt(2 * math.pi)
        return [
            (1.0 / (scale * sqrt_2pi)) * math.exp(-((x - loc) ** 2) / (2 * scale ** 2))
            for x in xs
        ]

class DummyOptionalImports:
    """Dummy optional_imports for testing purposes."""
    @staticmethod
    def get_module(name):
        if name == "scipy.stats":
            return DummyScipyStatsNorm
        raise ImportError("Unknown module requested.")

scipy_stats = DummyOptionalImports.get_module("scipy.stats")
ALTERNATIVE_HISTNORM = "probability"
from plotly.figure_factory._distplot import _Distplot

# =========================
# Unit tests for make_normal
# =========================

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

def test_single_trace_basic_normal():
    """Test with a single trace of basic data."""
    data = [[1, 2, 3, 4, 5]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["A"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); result = codeflash_output # 188μs -> 157μs (20.2% faster)
    curve = result[0]

def test_multiple_traces_basic():
    """Test with multiple traces and group labels."""
    data = [[1, 2, 3], [10, 11, 12]]
    labels = ["group1", "group2"]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=labels,
        bin_size=[1, 1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); result = codeflash_output # 281μs -> 217μs (29.5% faster)

def test_showlegend_behavior():
    """Test showlegend toggling based on show_hist."""
    dp1 = _Distplot(
        hist_data=[[1, 2, 3]],
        histnorm=None,
        group_labels=["g"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    dp2 = _Distplot(
        hist_data=[[1, 2, 3]],
        histnorm=None,
        group_labels=["g"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=True,
    )
    curve1 = dp1.make_normal()[0] # 152μs -> 125μs (21.2% faster)
    curve2 = dp2.make_normal()[0] # 116μs -> 89.8μs (29.6% faster)

def test_colors_cycle():
    """Test that colors cycle if more traces than colors."""
    colors = ["rgb(1,2,3)", "rgb(4,5,6)"]
    data = [[1,2,3],[4,5,6],[7,8,9]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["a","b","c"],
        bin_size=[1,1,1],
        curve_type="normal",
        colors=colors,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 374μs -> 288μs (29.6% faster)

def test_custom_bin_size_and_histnorm():
    """Test ALTERNATIVE_HISTNORM scaling."""
    data = [[1,2,3,4,5]]
    bin_size = [2.0]
    dp = _Distplot(
        hist_data=data,
        histnorm=ALTERNATIVE_HISTNORM,
        group_labels=["A"],
        bin_size=bin_size,
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 149μs -> 123μs (21.6% faster)
    # y values should be scaled by bin_size
    # Compare with histnorm=None
    dp2 = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["A"],
        bin_size=bin_size,
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    y_scaled = curves[0]["y"]
    y_unscaled = dp2.make_normal()[0]["y"] # 113μs -> 87.3μs (29.5% faster)
    # Each y_scaled[i] == y_unscaled[i] * bin_size[0]
    for ys, yu in zip(y_scaled, y_unscaled):
        pass

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

def test_single_value_trace():
    """Test with a trace containing only one value."""
    data = [[42]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["single"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 128μs -> 102μs (25.1% faster)
    # All y values except at x==42 should be 0.0, at x==42 inf
    for x, y in zip(curves[0]["x"], curves[0]["y"]):
        if math.isclose(x, 42.0):
            pass
        else:
            pass

def test_identical_values_trace():
    """Test with a trace of identical values."""
    data = [[7,7,7,7,7]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["identical"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 126μs -> 98.6μs (28.5% faster)
    # All y values except at x==7 should be 0.0, at x==7 inf
    for x, y in zip(curves[0]["x"], curves[0]["y"]):
        if math.isclose(x, 7.0):
            pass
        else:
            pass

def test_negative_and_zero_values():
    """Test with negative, zero, and positive values."""
    data = [[-5, 0, 5]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["mixed"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 159μs -> 133μs (19.4% faster)


def test_large_bin_size_scaling():
    """Test that large bin_size scales y values proportionally."""
    data = [[1,2,3,4,5]]
    dp = _Distplot(
        hist_data=data,
        histnorm=ALTERNATIVE_HISTNORM,
        group_labels=["A"],
        bin_size=[100.0],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 200μs -> 175μs (14.4% faster)
    # All y values should be much larger than with bin_size=1.0
    dp2 = _Distplot(
        hist_data=data,
        histnorm=ALTERNATIVE_HISTNORM,
        group_labels=["A"],
        bin_size=[1.0],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    y_large = curves[0]["y"]
    y_small = dp2.make_normal()[0]["y"] # 124μs -> 98.9μs (26.1% faster)
    for yl, ys in zip(y_large, y_small):
        pass

def test_color_none_defaults():
    """Test that colors=None uses default color list."""
    data = [[1,2,3]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["A"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 155μs -> 130μs (19.5% faster)

def test_rug_text_none_defaults():
    """Test that rug_text=None sets rug_text to [None]*trace_number."""
    data = [[1,2,3],[4,5,6]]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["A","B"],
        bin_size=[1,1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )

# ---- Large Scale Test Cases ----

def test_many_traces():
    """Test with 100 traces of small data."""
    data = [[i, i+1, i+2] for i in range(100)]
    labels = [f"g{i}" for i in range(100)]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=labels,
        bin_size=[1]*100,
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 10.7ms -> 7.78ms (37.0% faster)
    for i, curve in enumerate(curves):
        pass

def test_large_trace():
    """Test with a single trace of 1000 values."""
    data = [list(range(1000))]
    dp = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["big"],
        bin_size=[1],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 194μs -> 172μs (13.0% faster)

def test_large_trace_with_alternative_histnorm():
    """Test with a large trace and ALTERNATIVE_HISTNORM."""
    data = [list(range(1000))]
    dp = _Distplot(
        hist_data=data,
        histnorm=ALTERNATIVE_HISTNORM,
        group_labels=["big"],
        bin_size=[2.0],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp.make_normal(); curves = codeflash_output # 182μs -> 158μs (14.9% faster)
    # All y values should be scaled by bin_size
    dp2 = _Distplot(
        hist_data=data,
        histnorm=None,
        group_labels=["big"],
        bin_size=[2.0],
        curve_type="normal",
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    y_scaled = curves[0]["y"]
    y_unscaled = dp2.make_normal()[0]["y"] # 144μs -> 118μs (21.9% faster)
    for ys, yu in zip(y_scaled, y_unscaled):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import math
# Patch the tested code's import
import sys
# function to test
import types

# imports
import pytest  # used for our unit tests
from plotly.figure_factory._distplot import _Distplot


# Minimal scipy.stats.norm replacement for testing
class FakeNorm:
    @staticmethod
    def fit(data):
        # mean and stddev
        n = len(data)
        mean = sum(data) / n
        var = sum((x - mean) ** 2 for x in data) / n
        stddev = math.sqrt(var)
        return mean, stddev

    @staticmethod
    def pdf(xs, loc, scale):
        # xs: list of values
        # loc: mean, scale: stddev
        def pdf_single(x):
            if scale == 0:
                return 1.0 if x == loc else 0.0
            return (
                1.0
                / (scale * math.sqrt(2 * math.pi))
                * math.exp(-((x - loc) ** 2) / (2 * scale ** 2))
            )

        return [pdf_single(x) for x in xs]


# Patch the imported scipy_stats.norm in the tested class
class FakeOptionalImports:
    @staticmethod
    def get_module(name):
        class FakeScipyStats:
            norm = FakeNorm
        return FakeScipyStats()

# Copy-paste the tested class
ALTERNATIVE_HISTNORM = "probability"
from plotly.figure_factory._distplot import _Distplot

# unit tests

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

def test_single_group_basic_normal_curve():
    # Test with one group, simple data
    hist_data = [[1, 2, 3, 4, 5]]
    histnorm = "density"
    group_labels = ["A"]
    bin_size = [1]
    curve_type = "normal"
    colors = ["rgb(31, 119, 180)"]
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 152μs -> 127μs (19.1% faster)
    c = curve[0]

def test_multiple_groups_basic():
    # Test with two groups, different data
    hist_data = [[1, 2, 3], [10, 11, 12]]
    histnorm = "density"
    group_labels = ["A", "B"]
    bin_size = [1, 2]
    curve_type = "normal"
    colors = ["red", "blue"]
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 268μs -> 214μs (25.3% faster)

def test_histnorm_probability_scales_y():
    # Test that ALTERNATIVE_HISTNORM scales y by bin_size
    hist_data = [[0, 0, 0, 0, 0]]
    histnorm = ALTERNATIVE_HISTNORM
    group_labels = ["zeros"]
    bin_size = [2]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 131μs -> 106μs (23.1% faster)
    # All values are zero, so mean=0, stddev=0, so pdf at x=0 is 1, else 0
    yvals = curve[0]["y"]

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

def test_single_value_group():
    # Only one value in group
    hist_data = [[42]]
    histnorm = "density"
    group_labels = ["single"]
    bin_size = [1]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 127μs -> 102μs (24.5% faster)
    # Only one x value matches mean, so only one y is 1, rest are 0
    yvals = curve[0]["y"]

def test_identical_values_group():
    # All values are identical, stddev=0
    hist_data = [[7, 7, 7, 7]]
    histnorm = "density"
    group_labels = ["identical"]
    bin_size = [1]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 127μs -> 101μs (25.3% faster)
    yvals = curve[0]["y"]

def test_negative_and_positive_values():
    # Data with negative and positive values
    hist_data = [[-2, 0, 2]]
    histnorm = "density"
    group_labels = ["negpos"]
    bin_size = [1]
    curve_type = "normal"
    colors = ["green"]
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 161μs -> 134μs (20.0% faster)

def test_bin_size_affects_probability_histnorm():
    # Test that y is scaled by bin_size in ALTERNATIVE_HISTNORM
    hist_data = [[1, 1, 1]]
    histnorm = ALTERNATIVE_HISTNORM
    group_labels = ["A"]
    bin_size = [3]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 130μs -> 105μs (23.9% faster)
    yvals = curve[0]["y"]

def test_color_defaults_cycle():
    # More groups than default colors, colors should cycle
    hist_data = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11], [12, 13], [14, 15], [16, 17], [18, 19], [20, 21]]
    histnorm = "density"
    group_labels = [str(i) for i in range(11)]
    bin_size = [1] * 11
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 1.25ms -> 940μs (33.4% faster)

def test_showlegend_behavior():
    # showlegend should be False if show_hist is True, else True
    hist_data = [[1, 2, 3]]
    group_labels = ["A"]
    bin_size = [1]
    colors = None
    rug_text = None
    curve_type = "normal"

    # show_hist True
    distplot1 = _Distplot(hist_data, "density", group_labels, bin_size, curve_type, colors, rug_text, True, True)
    codeflash_output = distplot1.make_normal(); curve1 = codeflash_output # 150μs -> 126μs (19.5% faster)

    # show_hist False
    distplot2 = _Distplot(hist_data, "density", group_labels, bin_size, curve_type, colors, rug_text, False, True)
    codeflash_output = distplot2.make_normal(); curve2 = codeflash_output # 110μs -> 87.1μs (27.2% faster)

# ----------------------
# Large Scale Test Cases
# ----------------------

def test_large_number_of_groups():
    # 100 groups, each with 5 values
    hist_data = [[i + j for j in range(5)] for i in range(100)]
    histnorm = "density"
    group_labels = [f"g{i}" for i in range(100)]
    bin_size = [1] * 100
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 10.5ms -> 7.73ms (36.1% faster)
    for i in range(100):
        pass

def test_large_group_size():
    # One group, 1000 values
    hist_data = [list(range(1000))]
    histnorm = "density"
    group_labels = ["big"]
    bin_size = [1]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 204μs -> 174μs (17.2% faster)

def test_large_bin_size():
    # Test with large bin_size in ALTERNATIVE_HISTNORM
    hist_data = [[5, 5, 5, 5]]
    histnorm = ALTERNATIVE_HISTNORM
    group_labels = ["largebin"]
    bin_size = [999]
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = True
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 138μs -> 111μs (23.9% faster)
    yvals = curve[0]["y"]

def test_large_data_multiple_groups():
    # 10 groups, each with 100 values
    hist_data = [list(range(i, i + 100)) for i in range(10)]
    histnorm = "density"
    group_labels = [f"g{i}" for i in range(10)]
    bin_size = [1] * 10
    curve_type = "normal"
    colors = None
    rug_text = None
    show_hist = False
    show_curve = True

    distplot = _Distplot(
        hist_data,
        histnorm,
        group_labels,
        bin_size,
        curve_type,
        colors,
        rug_text,
        show_hist,
        show_curve,
    )
    codeflash_output = distplot.make_normal(); curve = codeflash_output # 1.15ms -> 882μs (30.7% faster)
    for i in range(10):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
from plotly.figure_factory._distplot import _Distplot

def test__Distplot_make_normal():
    _Distplot.make_normal(_Distplot('', 0, 0, 0, 0, 0, 0, 0, 0))
🔎 Concolic Coverage Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_grpsys06/tmpkm9rcoch/test_concolic_coverage.py::test__Distplot_make_normal 1.54μs 2.48μs -38.0%⚠️

To edit these changes git checkout codeflash/optimize-_Distplot.make_normal-mhg72arx and push.

Codeflash Static Badge

The optimized code achieves a **33% speedup** by reducing attribute access overhead and optimizing mathematical computations. 

**Key optimizations:**

1. **Local variable caching**: The optimized version pulls frequently accessed instance attributes (`self.histnorm`, `self.bin_size`, etc.) into local variables at the start of the method. This eliminates repeated attribute lookups during loop execution, which is particularly beneficial since Python's attribute access has overhead.

2. **Function reference caching**: `scipy_stats.norm.fit` and `scipy_stats.norm.pdf` are cached as local variables (`norm_fit`, `norm_pdf`) to avoid repeated module attribute lookups in the tight loop.

3. **Optimized x-coordinate generation**: Instead of the original list comprehension that repeatedly accessed `self.start[index]` and `self.end[index]`, the optimized version pre-computes `step = (e0 - s0) / 500` and uses local variables, reducing arithmetic operations per iteration.

4. **Vectorized operations**: The optimized code leverages NumPy's vectorized multiplication when `histnorm == ALTERNATIVE_HISTNORM`, operating on the entire array `y *= bin_size[index]` instead of element-wise operations.

**Performance impact by test case:**
- **Large-scale scenarios** see the biggest gains (36-37% faster) when processing many traces, as the attribute access overhead compounds
- **Basic cases** with single/few traces still benefit (19-30% faster) from reduced overhead
- **Edge cases** with identical values or single values see 23-25% improvements

The optimizations are particularly effective for the common use case of processing multiple statistical distributions, where the nested loops amplify the benefits of reduced attribute access overhead.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 1, 2025 11:24
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Nov 1, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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