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

📄 33% (0.33x) speedup for _Distplot.make_rug in plotly/figure_factory/_distplot.py

⏱️ Runtime : 263 microseconds 198 microseconds (best of 354 runs)

📝 Explanation and details

The optimized code achieves a 32% speedup by eliminating redundant attribute lookups and computations within the loop.

Key optimizations:

  1. Pre-fetching attributes to local variables: Moving self.hist_data, self.group_labels, self.colors, etc. to local variables outside the loop eliminates repeated attribute lookups. In Python, local variable access is significantly faster than attribute access through self..

  2. Pre-computing values:

    • n_colors = len(colors) calculates the color list length once instead of inside each loop iteration
    • showlegend_value is computed once outside the loop rather than evaluating the boolean expression repeatedly
    • len_xdata = len(xdata) caches the length to avoid calling len() twice per iteration
  3. Using dictionary literals instead of dict(): Replacing dict() constructor calls with dictionary literal syntax {} provides a small performance boost.

Performance impact by test case:

  • Large-scale scenarios see the biggest gains (38-45% faster) where many traces amplify the attribute lookup savings
  • Basic cases show consistent 10-18% improvements across different configurations
  • Edge cases like empty data show slight regression (~22% slower) due to initialization overhead, but this represents minimal absolute time difference

The optimization is particularly effective for scenarios with multiple traces or complex data structures, making it ideal for typical plotting workloads where users generate plots with multiple data series.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 66 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 2 Passed
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest  # used for our unit tests
from plotly.figure_factory._distplot import _Distplot

# unit tests

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

def test_single_trace_basic():
    # Basic: one trace, simple data, default colors
    dp = _Distplot(
        hist_data=[[1, 2, 3]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.16μs -> 1.92μs (12.5% faster)
    r = rug[0]

def test_multiple_traces_basic():
    # Basic: two traces, custom colors, custom rug_text
    dp = _Distplot(
        hist_data=[[1, 2], [3, 4]],
        histnorm=None,
        group_labels=["A", "B"],
        bin_size=None,
        curve_type=None,
        colors=["red", "blue"],
        rug_text=["foo", "bar"],
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.77μs -> 2.37μs (17.1% faster)

def test_show_hist_and_curve_flags():
    # Basic: show_hist True disables legend
    dp = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.05μs -> 1.86μs (10.1% faster)
    # show_curve True disables legend
    dp2 = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=True,
    )
    codeflash_output = dp2.make_rug(); rug2 = codeflash_output # 1.11μs -> 989ns (11.9% faster)
    # Both True disables legend
    dp3 = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=True,
    )
    codeflash_output = dp3.make_rug(); rug3 = codeflash_output # 842ns -> 752ns (12.0% faster)

def test_color_cycling():
    # Basic: colors shorter than number of traces, should cycle
    colors = ["red"]
    dp = _Distplot(
        hist_data=[[1], [2], [3]],
        histnorm=None,
        group_labels=["A", "B", "C"],
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 3.14μs -> 2.69μs (16.8% faster)

def test_default_colors_longer_than_traces():
    # Basic: default colors, more colors than traces
    dp = _Distplot(
        hist_data=[[1], [2]],
        histnorm=None,
        group_labels=["A", "B"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.78μs -> 2.36μs (17.9% faster)

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

def test_empty_hist_data():
    # Edge: No traces
    dp = _Distplot(
        hist_data=[],
        histnorm=None,
        group_labels=[],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 737ns -> 1.05μs (29.7% slower)


def test_none_rug_text_explicit():
    # Edge: rug_text explicitly None
    dp = _Distplot(
        hist_data=[[1]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 3.21μs -> 2.76μs (16.5% faster)

def test_rug_text_list_with_none():
    # Edge: rug_text list with None entry
    dp = _Distplot(
        hist_data=[[1], [2]],
        histnorm=None,
        group_labels=["A", "B"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=[None, "foo"],
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 3.37μs -> 2.85μs (18.1% faster)

def test_group_labels_with_duplicates():
    # Edge: duplicate group labels
    dp = _Distplot(
        hist_data=[[1], [2]],
        histnorm=None,
        group_labels=["A", "A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.92μs -> 2.60μs (12.3% faster)

def test_colors_shorter_than_traces():
    # Edge: colors shorter than traces, should cycle
    dp = _Distplot(
        hist_data=[[1], [2], [3]],
        histnorm=None,
        group_labels=["A", "B", "C"],
        bin_size=None,
        curve_type=None,
        colors=["red", "green"],
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 3.54μs -> 3.02μs (17.2% faster)

def test_group_labels_non_string():
    # Edge: group_labels are not strings
    dp = _Distplot(
        hist_data=[[1], [2]],
        histnorm=None,
        group_labels=[1, 2],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.88μs -> 2.50μs (15.6% faster)

def test_hist_data_with_negative_and_float_values():
    # Edge: hist_data with negative and float values
    dp = _Distplot(
        hist_data=[[-1.5, 0, 2.3]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.25μs -> 1.97μs (14.7% faster)

def test_hist_data_with_single_value():
    # Edge: trace with single value
    dp = _Distplot(
        hist_data=[[42]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.04μs -> 1.97μs (3.71% faster)

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

def test_large_number_of_traces():
    # Large: many traces, each with one value
    n = 100
    hist_data = [[i] for i in range(n)]
    group_labels = [str(i) for i in range(n)]
    colors = ["red", "green", "blue"]
    rug_text = [None] * n
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=rug_text,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 45.8μs -> 31.9μs (43.3% faster)
    for i in range(n):
        pass

def test_large_trace_length():
    # Large: one trace with many values
    n = 500
    hist_data = [list(range(n))]
    group_labels = ["A"]
    colors = ["red"]
    rug_text = ["foo"]
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=rug_text,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 2.39μs -> 2.22μs (7.33% faster)

def test_large_trace_with_varied_group_labels_and_colors():
    # Large: 50 traces, each with 20 values, custom colors and labels
    n_traces = 50
    n_vals = 20
    hist_data = [list(range(i, i + n_vals)) for i in range(n_traces)]
    group_labels = [f"Group_{i}" for i in range(n_traces)]
    colors = [f"rgb({i},{i},{i})" for i in range(n_traces)]
    rug_text = [f"text_{i}" for i in range(n_traces)]
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=rug_text,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 23.1μs -> 16.7μs (38.3% faster)
    for i in range(n_traces):
        pass

def test_large_color_cycle():
    # Large: 30 traces, only 3 colors, should cycle
    n_traces = 30
    hist_data = [[i] for i in range(n_traces)]
    group_labels = [str(i) for i in range(n_traces)]
    colors = ["red", "green", "blue"]
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 15.1μs -> 10.9μs (38.5% faster)
    for i in range(n_traces):
        pass

def test_large_group_labels_non_string():
    # Large: group_labels are integers, many traces
    n_traces = 100
    hist_data = [[i] for i in range(n_traces)]
    group_labels = list(range(n_traces))
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rug = codeflash_output # 44.5μs -> 31.8μs (39.7% faster)
    for i in range(n_traces):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import pytest  # used for our unit tests
from plotly.figure_factory._distplot import _Distplot

# unit tests

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

def test_single_trace_basic():
    # Basic: Single trace, simple data
    dp = _Distplot(
        hist_data=[[1, 2, 3]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.54μs -> 2.14μs (18.7% faster)
    rug = rugs[0]

def test_multiple_traces_basic():
    # Basic: Multiple traces with explicit colors and rug_text
    dp = _Distplot(
        hist_data=[[1, 2], [3, 4]],
        histnorm=None,
        group_labels=["A", "B"],
        bin_size=None,
        curve_type=None,
        colors=["red", "blue"],
        rug_text=["foo", "bar"],
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.90μs -> 2.52μs (15.3% faster)

def test_show_hist_and_curve_flags():
    # Basic: show_hist and show_curve flags affect showlegend
    dp = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=True,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.21μs -> 2.00μs (10.5% faster)
    dp = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=True,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 1.15μs -> 1.08μs (6.31% faster)

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

def test_empty_hist_data():
    # Edge: hist_data is empty
    dp = _Distplot(
        hist_data=[],
        histnorm=None,
        group_labels=[],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 770ns -> 985ns (21.8% slower)


def test_group_labels_length_mismatch():
    # Edge: group_labels length mismatch with hist_data
    with pytest.raises(IndexError):
        dp = _Distplot(
            hist_data=[[1, 2], [3, 4]],
            histnorm=None,
            group_labels=["A"],  # Only 1 label for 2 traces
            bin_size=None,
            curve_type=None,
            colors=None,
            rug_text=None,
            show_hist=False,
            show_curve=False,
        )
        dp.make_rug() # 3.79μs -> 3.27μs (16.1% faster)

def test_colors_shorter_than_traces():
    # Edge: colors list shorter than number of traces, should wrap around
    dp = _Distplot(
        hist_data=[[1], [2], [3]],
        histnorm=None,
        group_labels=["A", "B", "C"],
        bin_size=None,
        curve_type=None,
        colors=["red"],  # Only one color
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 3.96μs -> 3.26μs (21.5% faster)

def test_rug_text_none_and_partial():
    # Edge: rug_text is partially None
    dp = _Distplot(
        hist_data=[[1], [2]],
        histnorm=None,
        group_labels=["A", "B"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=[None, "foo"],
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.96μs -> 2.57μs (15.2% faster)

def test_group_labels_non_string():
    # Edge: group_labels are not strings
    dp = _Distplot(
        hist_data=[[1, 2]],
        histnorm=None,
        group_labels=[42],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.15μs -> 2.03μs (5.66% faster)

def test_hist_data_with_negative_and_float_values():
    # Edge: hist_data contains negative and float values
    dp = _Distplot(
        hist_data=[[-1.5, 0, 2.5]],
        histnorm=None,
        group_labels=["A"],
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.13μs -> 2.02μs (5.55% faster)

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

def test_large_number_of_traces():
    # Large: Many traces, each with a few elements
    n = 100
    hist_data = [[i, i+1] for i in range(n)]
    group_labels = [str(i) for i in range(n)]
    colors = ["red", "blue", "green"]
    rug_text = [None] * n
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=rug_text,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 46.0μs -> 31.7μs (45.4% faster)
    # Check color wrapping
    for i in range(n):
        pass

def test_large_trace_length():
    # Large: Single trace with many elements
    n = 1000
    hist_data = [list(range(n))]
    group_labels = ["A"]
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=None,
        rug_text=None,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 2.51μs -> 2.28μs (9.89% faster)

def test_large_trace_with_varied_labels_and_colors():
    # Large: Multiple traces, each with different labels and colors
    n = 50
    hist_data = [[i, i+1, i+2] for i in range(n)]
    group_labels = [f"label_{i}" for i in range(n)]
    colors = [f"rgb({i%256}, {i%256}, {i%256})" for i in range(n)]
    rug_text = [f"text_{i}" for i in range(n)]
    dp = _Distplot(
        hist_data=hist_data,
        histnorm=None,
        group_labels=group_labels,
        bin_size=None,
        curve_type=None,
        colors=colors,
        rug_text=rug_text,
        show_hist=False,
        show_curve=False,
    )
    codeflash_output = dp.make_rug(); rugs = codeflash_output # 22.5μs -> 16.1μs (39.1% faster)
    for i in range(n):
        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_rug():
    _Distplot.make_rug(_Distplot('', 0, 0, 0, 0, 0, 0, 0, 0))
🔎 Concolic Coverage Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
codeflash_concolic_grpsys06/tmpyc5q9bww/test_concolic_coverage.py::test__Distplot_make_rug 871ns 1.22μs -28.8%⚠️

To edit these changes git checkout codeflash/optimize-_Distplot.make_rug-mhg7f0wz and push.

Codeflash Static Badge

The optimized code achieves a **32% speedup** by eliminating redundant attribute lookups and computations within the loop. 

**Key optimizations:**

1. **Pre-fetching attributes to local variables**: Moving `self.hist_data`, `self.group_labels`, `self.colors`, etc. to local variables outside the loop eliminates repeated attribute lookups. In Python, local variable access is significantly faster than attribute access through `self.`.

2. **Pre-computing values**: 
   - `n_colors = len(colors)` calculates the color list length once instead of inside each loop iteration
   - `showlegend_value` is computed once outside the loop rather than evaluating the boolean expression repeatedly
   - `len_xdata = len(xdata)` caches the length to avoid calling `len()` twice per iteration

3. **Using dictionary literals instead of `dict()`**: Replacing `dict()` constructor calls with dictionary literal syntax `{}` provides a small performance boost.

**Performance impact by test case:**
- **Large-scale scenarios** see the biggest gains (38-45% faster) where many traces amplify the attribute lookup savings
- **Basic cases** show consistent 10-18% improvements across different configurations  
- **Edge cases** like empty data show slight regression (~22% slower) due to initialization overhead, but this represents minimal absolute time difference

The optimization is particularly effective for scenarios with multiple traces or complex data structures, making it ideal for typical plotting workloads where users generate plots with multiple data series.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 1, 2025 11:33
@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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