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

📄 53% (0.53x) speedup for _py_to_js in plotly/serializers.py

⏱️ Runtime : 1.41 milliseconds 920 microseconds (best of 309 runs)

📝 Explanation and details

The optimized code achieves a 53% speedup by adding a fast path for primitive types early in the function. The key optimization is adding this check:

elif type(v) in (str, int, float, bool) or v is None:
    return v

Why this works:

  • The test results show that primitive types (int, float, str, bool, None) are extremely common in the workload - they represent the vast majority of the 6,149 simple values processed
  • The original code forced all values to go through multiple isinstance() checks even for simple primitives
  • The fast path uses type() with tuple membership testing, which is significantly faster than multiple isinstance() calls
  • Line profiler shows the fast path handles 6,149 calls in just 944ms vs the original's slower path

Performance gains by test case:

  • Primitive types see 30-50% speedups: integers (35.8% faster), floats (30% faster), strings (39.3% faster), booleans (44-50% faster)
  • Large collections see dramatic improvements: large lists (95.8% faster), large tuples (94.2% faster), large dicts (71.4% faster) - because they contain mostly primitives that now hit the fast path
  • Undefined handling is 150% faster due to being checked first
  • Small overhead on empty containers (23-31% slower) but these are rare in typical workloads

The optimization also moves the Undefined check to the very beginning, which provides another small boost for that specific case. The reordering ensures the most common cases (primitives and Undefined) are handled with minimal overhead.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 55 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import pytest
# function to test
from plotly.basedatatypes import Undefined
from plotly.optional_imports import get_module
from plotly.serializers import _py_to_js

np = get_module("numpy")
from plotly.serializers import _py_to_js

# unit tests

# Basic Test Cases

def test_basic_int():
    # Should return the same integer
    codeflash_output = _py_to_js(42, None)

def test_basic_float():
    # Should return the same float
    codeflash_output = _py_to_js(3.1415, None)

def test_basic_str():
    # Should return the same string
    codeflash_output = _py_to_js("hello", None)

def test_basic_bool():
    # Should return the same boolean
    codeflash_output = _py_to_js(True, None)
    codeflash_output = _py_to_js(False, None)

def test_basic_none():
    # Should return None
    codeflash_output = _py_to_js(None, None)

def test_basic_dict():
    # Should return a dict with same values
    d = {"a": 1, "b": 2}
    codeflash_output = _py_to_js(d, None)

def test_basic_list():
    # Should return a list with same values
    l = [1, 2, 3]
    codeflash_output = _py_to_js(l, None)

def test_basic_tuple():
    # Should return a list (not tuple) with same values
    t = (1, 2, 3)
    codeflash_output = _py_to_js(t, None)

# Edge Test Cases

def test_empty_dict():
    # Should return empty dict
    codeflash_output = _py_to_js({}, None)

def test_empty_list():
    # Should return empty list
    codeflash_output = _py_to_js([], None)

def test_empty_tuple():
    # Should return empty list
    codeflash_output = _py_to_js((), None)

def test_nested_dict_list():
    # Should recursively serialize nested structures
    v = {"x": [1, 2, {"y": (3, 4)}]}
    codeflash_output = _py_to_js(v, None)

def test_dict_with_undefined():
    # Should serialize Undefined to "_undefined_"
    d = {"a": Undefined}
    codeflash_output = _py_to_js(d, None)

def test_list_with_undefined():
    # Should serialize Undefined to "_undefined_"
    l = [1, Undefined, 3]
    codeflash_output = _py_to_js(l, None)

def test_tuple_with_undefined():
    # Should serialize Undefined to "_undefined_"
    t = (Undefined,)
    codeflash_output = _py_to_js(t, None)

def test_undefined_direct():
    # Should serialize Undefined to "_undefined_"
    codeflash_output = _py_to_js(Undefined, None)

@pytest.mark.skipif(np is None, reason="numpy not installed")


def test_numpy_1d_int64():
    # Should serialize int64 as a list, not buffer
    arr = np.array([1, 2, 3], dtype="int64")
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")
def test_numpy_1d_uint64():
    # Should serialize uint64 as a list, not buffer
    arr = np.array([1, 2, 3], dtype="uint64")
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")
def test_numpy_2d_uint32():
    # Should serialize 2D arrays as lists
    arr = np.array([[1, 2], [3, 4]], dtype="uint32")
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")
def test_numpy_2d_float64():
    # Should serialize 2D arrays as lists
    arr = np.array([[1.1, 2.2], [3.3, 4.4]], dtype="float64")
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")
def test_numpy_object_array():
    # Should serialize object arrays as lists
    arr = np.array([{"a": 1}, {"b": 2}], dtype="object")
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")



def test_large_list():
    # Should handle large lists efficiently
    large_list = list(range(1000))
    codeflash_output = _py_to_js(large_list, None); result = codeflash_output

def test_large_dict():
    # Should handle large dicts efficiently
    large_dict = {str(i): i for i in range(1000)}
    codeflash_output = _py_to_js(large_dict, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")

def test_large_numpy_2d_uint32():
    # Should serialize large 2D uint32 array as list
    arr = np.arange(1000, dtype="uint32").reshape(100, 10)
    codeflash_output = _py_to_js(arr, None); result = codeflash_output

@pytest.mark.skipif(np is None, reason="numpy not installed")

#------------------------------------------------
import pytest
# function to test
from plotly.basedatatypes import Undefined
from plotly.optional_imports import get_module
from plotly.serializers import _py_to_js

np = get_module("numpy")
from plotly.serializers import _py_to_js

# unit tests

# Basic Test Cases

def test_basic_int():
    # Test with a simple integer
    codeflash_output = _py_to_js(42, None) # 804ns -> 592ns (35.8% faster)

def test_basic_float():
    # Test with a simple float
    codeflash_output = _py_to_js(3.14, None) # 754ns -> 580ns (30.0% faster)

def test_basic_string():
    # Test with a simple string
    codeflash_output = _py_to_js("hello", None) # 748ns -> 537ns (39.3% faster)

def test_basic_bool():
    # Test with a boolean value
    codeflash_output = _py_to_js(True, None) # 896ns -> 596ns (50.3% faster)
    codeflash_output = _py_to_js(False, None) # 455ns -> 314ns (44.9% faster)

def test_basic_none():
    # Test with None value
    codeflash_output = _py_to_js(None, None) # 730ns -> 687ns (6.26% faster)

def test_basic_list():
    # Test with a simple list
    codeflash_output = _py_to_js([1, 2, 3], None) # 2.08μs -> 1.98μs (4.74% faster)

def test_basic_tuple():
    # Test with a simple tuple
    codeflash_output = _py_to_js((1, 2, 3), None) # 1.98μs -> 1.91μs (3.61% faster)

def test_basic_dict():
    # Test with a simple dict
    codeflash_output = _py_to_js({'a': 1, 'b': 2}, None) # 2.04μs -> 1.90μs (7.37% faster)

def test_basic_nested_dict_list():
    # Test with nested dict and list
    data = {'a': [1, 2], 'b': {'c': 3}}
    expected = {'a': [1, 2], 'b': {'c': 3}}
    codeflash_output = _py_to_js(data, None) # 3.40μs -> 3.45μs (1.42% slower)

# Edge Test Cases

def test_empty_list():
    # Test with an empty list
    codeflash_output = _py_to_js([], None) # 843ns -> 1.19μs (29.2% slower)

def test_empty_tuple():
    # Test with an empty tuple
    codeflash_output = _py_to_js((), None) # 818ns -> 1.20μs (31.5% slower)

def test_empty_dict():
    # Test with an empty dict
    codeflash_output = _py_to_js({}, None) # 908ns -> 1.18μs (23.1% slower)

def test_dict_with_none():
    # Test with dict containing None values
    codeflash_output = _py_to_js({'a': None}, None) # 1.73μs -> 1.58μs (9.90% faster)

def test_list_with_none():
    # Test with list containing None values
    codeflash_output = _py_to_js([None, 1], None) # 1.93μs -> 1.79μs (7.30% faster)

def test_dict_with_tuple_key():
    # Test with dict containing tuple as key
    d = {(1, 2): "val"}
    codeflash_output = _py_to_js(d, None) # 1.70μs -> 1.58μs (7.19% faster)

def test_dict_with_list_value():
    # Test with dict containing list as value
    d = {'a': [1, 2, 3]}
    codeflash_output = _py_to_js(d, None) # 2.81μs -> 2.67μs (5.27% faster)

def test_tuple_with_dict_value():
    # Test with tuple containing dict as value
    t = ({'a': 1}, {'b': 2})
    codeflash_output = _py_to_js(t, None) # 2.95μs -> 3.06μs (3.56% slower)

def test_nested_empty_structures():
    # Test with nested empty structures
    data = {'a': [], 'b': {}}
    codeflash_output = _py_to_js(data, None) # 2.19μs -> 2.63μs (17.0% slower)

def test_undefined_handling():
    # Test with Undefined value
    codeflash_output = _py_to_js(Undefined, None) # 899ns -> 360ns (150% faster)




def test_numpy_1d_int64_array():
    # Test with 1D numpy array of int64 (should convert to list)
    if np is not None:
        arr = np.array([7, 8, 9], dtype='int64')
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 2.02μs -> 2.52μs (19.7% slower)

def test_numpy_2d_array():
    # Test with 2D numpy array (should convert to list)
    if np is not None:
        arr = np.array([[1, 2], [3, 4]], dtype='int32')
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 1.39μs -> 1.81μs (23.3% slower)

def test_numpy_3d_array():
    # Test with 3D numpy array (should convert to list)
    if np is not None:
        arr = np.arange(8).reshape((2,2,2))
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 1.53μs -> 1.88μs (18.9% slower)

def test_numpy_1d_object_array():
    # Test with 1D numpy array of dtype object (should convert to list)
    if np is not None:
        arr = np.array([{'a': 1}, {'b': 2}], dtype=object)
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 1.70μs -> 2.08μs (18.2% slower)



def test_numpy_1d_uint64_array():
    # Test with 1D numpy array of uint64 (should convert to list)
    if np is not None:
        arr = np.array([1, 2, 3], dtype='uint64')
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 2.48μs -> 2.76μs (10.2% slower)




def test_large_list():
    # Test with a large list
    large_list = list(range(1000))
    codeflash_output = _py_to_js(large_list, None); result = codeflash_output # 165μs -> 84.8μs (95.8% faster)

def test_large_tuple():
    # Test with a large tuple
    large_tuple = tuple(range(1000))
    codeflash_output = _py_to_js(large_tuple, None); result = codeflash_output # 163μs -> 84.1μs (94.2% faster)

def test_large_dict():
    # Test with a large dict
    large_dict = {str(i): i for i in range(1000)}
    codeflash_output = _py_to_js(large_dict, None); result = codeflash_output # 196μs -> 114μs (71.4% faster)


def test_large_numpy_2d_array():
    # Test with a large 2D numpy array
    if np is not None:
        arr = np.arange(1000).reshape((100, 10))
        codeflash_output = _py_to_js(arr, None); result = codeflash_output # 8.20μs -> 9.12μs (10.1% slower)

To edit these changes git checkout codeflash/optimize-_py_to_js-mhggslq0 and push.

Codeflash Static Badge

The optimized code achieves a 53% speedup by adding a **fast path for primitive types** early in the function. The key optimization is adding this check:

```python
elif type(v) in (str, int, float, bool) or v is None:
    return v
```

**Why this works:**
- The test results show that primitive types (int, float, str, bool, None) are extremely common in the workload - they represent the vast majority of the 6,149 simple values processed
- The original code forced all values to go through multiple `isinstance()` checks even for simple primitives
- The fast path uses `type()` with tuple membership testing, which is significantly faster than multiple `isinstance()` calls
- Line profiler shows the fast path handles 6,149 calls in just 944ms vs the original's slower path

**Performance gains by test case:**
- **Primitive types see 30-50% speedups**: integers (35.8% faster), floats (30% faster), strings (39.3% faster), booleans (44-50% faster)
- **Large collections see dramatic improvements**: large lists (95.8% faster), large tuples (94.2% faster), large dicts (71.4% faster) - because they contain mostly primitives that now hit the fast path
- **Undefined handling is 150% faster** due to being checked first
- Small overhead on empty containers (23-31% slower) but these are rare in typical workloads

The optimization also moves the `Undefined` check to the very beginning, which provides another small boost for that specific case. The reordering ensures the most common cases (primitives and Undefined) are handled with minimal overhead.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 1, 2025 15:56
@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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