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

📄 320% (3.20x) speedup for Figure.select_polars in plotly/graph_objs/_figure.py

⏱️ Runtime : 183 microseconds 43.5 microseconds (best of 5 runs)

📝 Explanation and details

The optimized code improves performance by pre-filtering layout keys before expensive operations. The key optimization is in the _select_layout_subplots_by_prefix method:

What was optimized:

  • Added prefix_filtered_keys = [k for k in self.layout if k.startswith(prefix) and self.layout[k] is not None] to pre-filter keys once
  • Modified the reduce call to use layout_keys_filters[1:] (skipping the first filter since it's already applied)
  • Applied _natural_sort_strings to the smaller pre-filtered set instead of the full layout

Why this is faster:

  1. Reduced sorting overhead: _natural_sort_strings now operates on a much smaller dataset (only keys matching the prefix) rather than all layout keys
  2. Eliminated redundant filtering: The prefix and None-check filter is applied once upfront via list comprehension instead of being repeatedly evaluated in the reduce chain
  3. Better cache locality: Working with a smaller, pre-filtered list improves memory access patterns

Performance characteristics:

  • The optimization is most effective for figures with many layout keys where only a small subset match the prefix (common in complex plotly figures)
  • From the profiler results, the critical _natural_sort_strings call dropped from 466,725ns to 162,970ns (65% reduction)
  • The overall method time improved from 559,864ns to 254,843ns (54% faster)

This optimization excels when selecting specific subplot types (like "polar") from figures with many different layout elements, as demonstrated by the 320% speedup in the test cases.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 70 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest
from plotly.graph_objs._figure import Figure

# Function to test: select_polars
# We'll define a minimal Figure class with select_polars method,
# simulating the relevant logic from the provided code.

class DummyPolar:
    """A dummy polar subplot object for testing."""
    def __init__(self, **kwargs):
        for k, v in kwargs.items():
            setattr(self, k, v)
        self._props = kwargs

    def __getitem__(self, item):
        return self._props[item]

    def __contains__(self, item):
        return item in self._props

    def __eq__(self, other):
        return isinstance(other, DummyPolar) and self._props == other._props

    def __repr__(self):
        return f"DummyPolar({self._props})"
from plotly.graph_objs._figure import Figure

# -------------------------------
# Unit tests for select_polars
# -------------------------------

# 1. Basic Test Cases

def test_select_polars_select_all():
    """Test selecting all polars with no selector."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
        "polar3": DummyPolar(a=3),
    }
    fig = Figure(polars)
    result = list(fig.select_polars())

def test_select_polars_selector_dict():
    """Test selecting polars with a dict selector."""
    polars = {
        "polar": DummyPolar(a=1, b=2),
        "polar2": DummyPolar(a=1, b=3),
        "polar3": DummyPolar(a=2, b=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector={"a":1}))
    result = list(fig.select_polars(selector={"a":1, "b":2}))
    result = list(fig.select_polars(selector={"a":2, "b":2}))
    result = list(fig.select_polars(selector={"a":999}))

def test_select_polars_selector_function():
    """Test selecting polars with a function selector."""
    polars = {
        "polar": DummyPolar(a=1, b=2),
        "polar2": DummyPolar(a=1, b=3),
        "polar3": DummyPolar(a=2, b=2),
    }
    fig = Figure(polars)
    # Select all with b == 2
    result = list(fig.select_polars(selector=lambda p: p.b == 2))
    # Select all with a > 1
    result = list(fig.select_polars(selector=lambda p: p.a > 1))
    # Select none
    result = list(fig.select_polars(selector=lambda p: False))


def test_select_polars_none_values():
    """Test selecting polars when some layout values are None."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": None,
        "polar3": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars())

# 2. Edge Test Cases

def test_select_polars_no_matching_selector():
    """Selector matches no polars."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector={"a":999}))

def test_select_polars_selector_partial_key():
    """Selector dict with key not present in any polar."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector={"b":1}))

def test_select_polars_selector_empty_dict():
    """Selector is an empty dict: should select all polars."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector={}))

def test_select_polars_selector_none():
    """Selector is None: should select all polars."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector=None))

def test_select_polars_with_row_col():
    """Test selecting polars by row and col."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
        "polar3": DummyPolar(a=3),
        "polar4": DummyPolar(a=4),
    }
    # grid_ref: 2 rows x 2 cols, each cell lists keys
    grid_ref = [
        [["polar"], ["polar2"]],
        [["polar3"], ["polar4"]],
    ]
    fig = Figure(polars, grid_ref=grid_ref)
    # Select row 1, col 1
    result = list(fig.select_polars(row=1, col=1))
    # Select row 2, col 2
    result = list(fig.select_polars(row=2, col=2))
    # Select row 2, col 1
    result = list(fig.select_polars(row=2, col=1))
    # Select row 1, col 2
    result = list(fig.select_polars(row=1, col=2))
    # Select row out of bounds
    result = list(fig.select_polars(row=3, col=1))

def test_select_polars_row_col_without_grid_ref():
    """Test selecting polars by row/col without grid_ref raises Exception."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    with pytest.raises(Exception) as e:
        list(fig.select_polars(row=1, col=1))

def test_select_polars_non_polar_keys():
    """Test that non-polar keys are ignored."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
        "notpolar": DummyPolar(a=3),
        "polar_extra": DummyPolar(a=4),
    }
    fig = Figure(polars)
    result = list(fig.select_polars())

def test_select_polars_selector_type_mismatch():
    """Selector is a type not supported (e.g., int). Should select none."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(a=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector=123))

def test_select_polars_selector_property_missing():
    """Selector dict with property missing on some polars."""
    polars = {
        "polar": DummyPolar(a=1),
        "polar2": DummyPolar(b=2),
    }
    fig = Figure(polars)
    result = list(fig.select_polars(selector={"a":1}))
    result = list(fig.select_polars(selector={"b":2}))

# 3. Large Scale Test Cases

def test_select_polars_large_scale_all():
    """Test selecting all polars in a large layout."""
    N = 1000
    polars = {f"polar{i}": DummyPolar(idx=i, val=i%10) for i in range(N)}
    fig = Figure(polars)
    result = list(fig.select_polars())

def test_select_polars_large_scale_selector():
    """Test selecting polars with a selector in a large layout."""
    N = 1000
    polars = {f"polar{i}": DummyPolar(idx=i, val=i%10) for i in range(N)}
    fig = Figure(polars)
    # Select all polars with val == 3
    result = list(fig.select_polars(selector={"val":3}))
    expected = [p for p in polars.values() if p.val == 3]
    # Select all polars with idx >= 990
    result = list(fig.select_polars(selector=lambda p: p.idx >= 990))
    expected = [p for p in polars.values() if p.idx >= 990]

def test_select_polars_large_scale_row_col():
    """Test selecting polars by row/col in a large grid."""
    N = 100
    # 10x10 grid
    grid_ref = []
    polars = {}
    count = 0
    for r in range(10):
        row = []
        for c in range(10):
            key = f"polar{count}"
            polars[key] = DummyPolar(idx=count, row=r+1, col=c+1)
            row.append([key])
            count += 1
        grid_ref.append(row)
    fig = Figure(polars, grid_ref=grid_ref)
    # Select row 5, col 7
    result = list(fig.select_polars(row=5, col=7))
    expected_key = f"polar{(5-1)*10 + (7-1)}"
    # Select all polars in row 1
    results_row_1 = []
    for c in range(1, 11):
        results_row_1 += list(fig.select_polars(row=1, col=c))
    expected_row_1 = [polars[f"polar{(0)*10 + (c-1)}"] for c in range(1, 11)]

def test_select_polars_large_scale_selector_and_row_col():
    """Test selecting polars with both selector and row/col in large grid."""
    N = 100
    grid_ref = []
    polars = {}
    count = 0
    for r in range(10):
        row = []
        for c in range(10):
            key = f"polar{count}"
            val = (r+c)%5
            polars[key] = DummyPolar(idx=count, row=r+1, col=c+1, val=val)
            row.append([key])
            count += 1
        grid_ref.append(row)
    fig = Figure(polars, grid_ref=grid_ref)
    # Select all polars in row 3, col 4 with val == 2
    result = list(fig.select_polars(selector={"val":2}, row=3, col=4))
    expected_key = f"polar{(3-1)*10 + (4-1)}"
    expected = polars[expected_key]
    if expected.val == 2:
        pass
    else:
        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
from plotly.graph_objs._figure import Figure


# Minimal stub implementation of Figure and select_polars for testing
class PolarStub:
    def __init__(self, **kwargs):
        self.__dict__.update(kwargs)
    def __getitem__(self, key):
        return getattr(self, key)
    def __contains__(self, key):
        return hasattr(self, key)
    def __repr__(self):
        return f"PolarStub({self.__dict__})"
from plotly.graph_objs._figure import Figure

# ----------------- UNIT TESTS -----------------

# 1. BASIC TEST CASES

def test_select_polars_returns_all_when_no_selector():
    # Should return all polar objects
    p1 = PolarStub(name="a")
    p2 = PolarStub(name="b")
    fig = Figure(layout={"polar": p1, "polar2": p2})
    result = list(fig.select_polars())

def test_select_polars_selector_dict_exact_match():
    # Should return only polars matching all dict keys/values
    p1 = PolarStub(name="foo", visible=True)
    p2 = PolarStub(name="bar", visible=False)
    fig = Figure(layout={"polar": p1, "polar2": p2})
    result = list(fig.select_polars(selector={"name": "foo", "visible": True}))
    result = list(fig.select_polars(selector={"visible": False}))

def test_select_polars_selector_function():
    # Should return polars for which selector(obj) returns True
    p1 = PolarStub(val=1)
    p2 = PolarStub(val=2)
    fig = Figure(layout={"polar": p1, "polar2": p2})
    result = list(fig.select_polars(selector=lambda p: p.val > 1))
    result = list(fig.select_polars(selector=lambda p: p.val < 3))

def test_select_polars_selector_int_index():
    # Should return the polar at the given index after filtering
    p1 = PolarStub(val=1)
    p2 = PolarStub(val=2)
    p3 = PolarStub(val=3)
    fig = Figure(layout={"polar": p1, "polar2": p2, "polar3": p3})
    # No selector, index 1
    result = list(fig.select_polars(selector=1))
    # With filtering, index 0 (should be p3)
    result = list(fig.select_polars(selector=0, row=None, col=None))

def test_select_polars_with_row_and_col():
    # Should filter by row and col attributes
    p1 = PolarStub(name="a", row=1, col=1)
    p2 = PolarStub(name="b", row=1, col=2)
    p3 = PolarStub(name="c", row=2, col=1)
    fig = Figure(layout={"polar": p1, "polar2": p2, "polar3": p3})
    result = list(fig.select_polars(row=1))
    result = list(fig.select_polars(col=1))
    result = list(fig.select_polars(row=2, col=1))

# 2. EDGE TEST CASES


def test_select_polars_no_matching_selector():
    # Should return empty if no polars match selector
    p1 = PolarStub(name="a")
    fig = Figure(layout={"polar": p1})
    result = list(fig.select_polars(selector={"name": "notfound"}))

def test_select_polars_selector_int_out_of_range():
    # Should raise IndexError if selector index is out of range
    p1 = PolarStub()
    fig = Figure(layout={"polar": p1})
    with pytest.raises(IndexError):
        list(fig.select_polars(selector=5))
    with pytest.raises(IndexError):
        list(fig.select_polars(selector=-1))

def test_select_polars_none_polars():
    # Should skip None values in layout
    p1 = PolarStub()
    fig = Figure(layout={"polar": p1, "polar2": None})
    result = list(fig.select_polars())

def test_select_polars_selector_dict_partial_key():
    # Should not match if only some keys match
    p1 = PolarStub(name="foo", visible=True)
    fig = Figure(layout={"polar": p1})
    result = list(fig.select_polars(selector={"name": "foo", "visible": False}))

def test_select_polars_selector_dict_extra_key():
    # Should not match if a key is missing in the object
    p1 = PolarStub(name="foo")
    fig = Figure(layout={"polar": p1})
    result = list(fig.select_polars(selector={"name": "foo", "missing": 123}))

def test_select_polars_with_secondary_y():
    # Should filter by secondary_y attribute
    p1 = PolarStub(name="a", secondary_y=True)
    p2 = PolarStub(name="b", secondary_y=False)
    fig = Figure(layout={"polar": p1, "polar2": p2})
    # Add secondary_y support to stub
    def _select_with_secondary_y(self, prefix, selector=None, row=None, col=None, secondary_y=None):
        polars = [v for k, v in self.layout.items() if k.startswith(prefix) and v is not None]
        if secondary_y is not None:
            polars = [p for p in polars if getattr(p, 'secondary_y', None) == secondary_y]
        if selector is not None:
            if isinstance(selector, int):
                if selector < 0 or selector >= len(polars):
                    raise IndexError("selector index out of range")
                return iter([polars[selector]])
            else:
                polars = [p for p in polars if self._selector_matches(p, selector)]
        return iter(polars)
    Figure._select_layout_subplots_by_prefix = _select_with_secondary_y
    result = list(fig.select_polars(secondary_y=True))
    result = list(fig.select_polars(secondary_y=False))

def test_select_polars_selector_callable_exception():
    # If selector function raises, should propagate
    p1 = PolarStub(val=1)
    fig = Figure(layout={"polar": p1})
    def bad_selector(obj):
        raise ValueError("bad selector")
    with pytest.raises(ValueError, match="bad selector"):
        list(fig.select_polars(selector=bad_selector))

# 3. LARGE SCALE TEST CASES

def test_select_polars_many_polars_basic():
    # Should handle large number of polars efficiently
    polars = [PolarStub(idx=i, group=i%3) for i in range(1000)]
    layout = {f"polar{i}": p for i, p in enumerate(polars)}
    fig = Figure(layout=layout)
    result = list(fig.select_polars())

def test_select_polars_many_polars_selector_dict():
    # Should filter correctly on large set
    polars = [PolarStub(idx=i, group=i%5) for i in range(1000)]
    layout = {f"polar{i}": p for i, p in enumerate(polars)}
    fig = Figure(layout=layout)
    # Select all with group==2
    result = list(fig.select_polars(selector={"group": 2}))
    expected = [p for p in polars if p.group == 2]

def test_select_polars_many_polars_selector_function():
    # Should filter with function on large set
    polars = [PolarStub(idx=i, group=i%10) for i in range(1000)]
    layout = {f"polar{i}": p for i, p in enumerate(polars)}
    fig = Figure(layout=layout)
    # Select all with idx divisible by 111
    result = list(fig.select_polars(selector=lambda p: p.idx % 111 == 0))
    expected = [p for p in polars if p.idx % 111 == 0]

def test_select_polars_many_polars_row_col():
    # Should filter by row and col in large set
    polars = []
    for i in range(1000):
        # Assign row and col in a grid of 10x10
        polars.append(PolarStub(idx=i, row=(i//100)+1, col=(i%100)//10+1))
    layout = {f"polar{i}": p for i, p in enumerate(polars)}
    fig = Figure(layout=layout)
    # Select all with row=5, col=3
    result = list(fig.select_polars(row=5, col=3))
    expected = [p for p in polars if p.row == 5 and p.col == 3]

def test_select_polars_performance_large():
    # Should not be quadratic in time for large N
    import time
    polars = [PolarStub(idx=i, group=i%2) for i in range(1000)]
    layout = {f"polar{i}": p for i, p in enumerate(polars)}
    fig = Figure(layout=layout)
    start = time.time()
    result = list(fig.select_polars(selector={"group": 1}))
    duration = time.time() - start
    expected = [p for p in polars if p.group == 1]
# 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-Figure.select_polars-mhg1oey5 and push.

Codeflash Static Badge

The optimized code improves performance by **pre-filtering layout keys** before expensive operations. The key optimization is in the `_select_layout_subplots_by_prefix` method:

**What was optimized:**
- Added `prefix_filtered_keys = [k for k in self.layout if k.startswith(prefix) and self.layout[k] is not None]` to pre-filter keys once
- Modified the `reduce` call to use `layout_keys_filters[1:]` (skipping the first filter since it's already applied)
- Applied `_natural_sort_strings` to the smaller pre-filtered set instead of the full layout

**Why this is faster:**
1. **Reduced sorting overhead**: `_natural_sort_strings` now operates on a much smaller dataset (only keys matching the prefix) rather than all layout keys
2. **Eliminated redundant filtering**: The prefix and None-check filter is applied once upfront via list comprehension instead of being repeatedly evaluated in the `reduce` chain
3. **Better cache locality**: Working with a smaller, pre-filtered list improves memory access patterns

**Performance characteristics:**
- The optimization is most effective for figures with many layout keys where only a small subset match the prefix (common in complex plotly figures)
- From the profiler results, the critical `_natural_sort_strings` call dropped from 466,725ns to 162,970ns (65% reduction)
- The overall method time improved from 559,864ns to 254,843ns (54% faster)

This optimization excels when selecting specific subplot types (like "polar") from figures with many different layout elements, as demonstrated by the 320% speedup in the test cases.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 1, 2025 08:53
@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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