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@codeflash-ai codeflash-ai bot commented Nov 13, 2025

📄 8% (0.08x) speedup for standalone_docs_json_and_render_items in src/bokeh/embed/util.py

⏱️ Runtime : 6.38 milliseconds 5.90 milliseconds (best of 59 runs)

📝 Explanation and details

The optimized code achieves an 8% speedup through several targeted micro-optimizations that reduce function call overhead and improve algorithmic efficiency:

Key Optimizations:

  1. Dict access optimization in standalone_docs_json_and_render_items: Replaced if doc not in docs: check followed by assignment with docs.setdefault(). This eliminates redundant hash table lookups, saving ~0.5% of total runtime.

  2. Short-circuit evaluation in submodel_has_python_callbacks: Replaced explicit loop with early-break logic using any() generator expression. This stops iteration immediately when the first model with callbacks is found, avoiding unnecessary model traversal.

  3. List comprehension for render items: Changed from appending items in a loop to a single list comprehension, reducing function call overhead and temporary variable assignments.

  4. Eliminated redundant imports in serialization functions: Moved the from ..core.types import ID import out of make_globally_unique_id(), removing repeated import overhead that was consuming ~18% of the function's time according to the profiler.

  5. Optimized UUID generation in make_globally_unique_css_safe_id: Direct string generation from uuid.uuid4() instead of calling make_globally_unique_id() and converting back, reducing function call overhead.

  6. Document model filtering improvement: Changed list comprehension to explicit loop for data_models collection, which shows better performance characteristics in the profiler (63.5% vs 65.9% of to_json time).

Impact on Hot Paths:
The function is called from notebook_content() in Jupyter notebook rendering, making these optimizations valuable for interactive data science workflows where plots are frequently generated. The 8% improvement translates to faster notebook cell execution and better user experience when creating multiple visualizations.

Test Case Performance:
The optimizations show consistent 5-13% improvements across error handling test cases, indicating the changes maintain correctness while improving performance across different code paths.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 44 Passed
🌀 Generated Regression Tests 7 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Existing Unit Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
unit/bokeh/embed/test_util__embed.py::Test_standalone_docs_json.test_output 1.12ms 947μs 18.2%✅
unit/bokeh/embed/test_util__embed.py::Test_standalone_docs_json_and_render_items.test_exception_for_missing_doc 24.8μs 24.7μs 0.287%✅
unit/bokeh/embed/test_util__embed.py::Test_standalone_docs_json_and_render_items.test_log_warning_if_python_event_callback 2.13ms 2.02ms 5.29%✅
unit/bokeh/embed/test_util__embed.py::Test_standalone_docs_json_and_render_items.test_log_warning_if_python_property_callback 2.12ms 2.02ms 4.67%✅
unit/bokeh/embed/test_util__embed.py::Test_standalone_docs_json_and_render_items.test_suppress_warnings 953μs 850μs 12.2%✅
🌀 Generated Regression Tests and Runtime
import logging
# Helper for unique IDs
import uuid
from typing import Sequence

# imports
import pytest
from bokeh.embed.util import standalone_docs_json_and_render_items

# --- Minimal stubs/mocks for Bokeh core classes and helpers ---

class RenderItem:
    # Minimal stub for RenderItem used in the function
    def __init__(self, docid, roots):
        self.docid = docid
        self.roots = roots

    def __eq__(self, other):
        return isinstance(other, RenderItem) and self.docid == other.docid and self.roots == other.roots

    def __repr__(self):
        return f"RenderItem(docid={self.docid!r}, roots={self.roots!r})"

class Model:
    # Minimal stub for Bokeh Model
    def __init__(self, name=None):
        self.name = name
        self.document = None
        self._callbacks = []
        self._event_callbacks = []

    def __repr__(self):
        return f"Model({self.name!r})"

_CALLBACKS_WARNING = "You are generating standalone HTML/JS output, but trying to use real Python callbacks"
from bokeh.embed.util import standalone_docs_json_and_render_items

# --- Unit tests ---

# 1. Basic Test Cases






def test_model_not_in_document_raises():
    # Test that passing a model not attached to a document raises ValueError
    m = Model("orphan")
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items(m) # 4.36μs -> 4.15μs (5.16% faster)


def test_invalid_input_type_raises():
    # Test that passing an invalid input type raises ValueError
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items(123) # 4.12μs -> 4.05μs (1.70% faster)
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items([Model("a"), 42]) # 3.26μs -> 2.98μs (9.21% faster)









import logging
import uuid
from typing import Any, Sequence

# imports
import pytest
from bokeh.embed.util import standalone_docs_json_and_render_items

# --- Minimal stubs for Bokeh types and utilities ---

# Simulate Bokeh's ID type
class ID(str):
    pass

# Simulate Bokeh's DocJson type
class DocJson(dict):
    pass

# Simulate Bokeh's Model base class
class Model:
    def __init__(self, name=None):
        self.name = name
        self.document = None
        self._callbacks = []
        self._event_callbacks = []

# Simulate Bokeh's RenderItem class
class RenderItem:
    def __init__(self, docid, roots):
        self.docid = docid
        self.roots = roots

    def __eq__(self, other):
        return (
            isinstance(other, RenderItem)
            and self.docid == other.docid
            and self.roots == other.roots
        )

# --- Function under test ---

_CALLBACKS_WARNING = "PYTHON CALLBACKS WARNING"
from bokeh.embed.util import standalone_docs_json_and_render_items

# --- Unit tests ---

# Basic Test Cases





def test_model_without_document_edge():
    # Model not attached to any document should raise ValueError
    m = Model(name="orphan")
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items(m) # 4.33μs -> 4.08μs (5.97% faster)

def test_invalid_input_type_edge():
    # Passing an invalid type should raise ValueError
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items(123) # 3.48μs -> 3.10μs (12.3% faster)
    with pytest.raises(ValueError):
        standalone_docs_json_and_render_items([Model(name="x"), 123]) # 3.12μs -> 2.76μs (13.1% faster)

def test_empty_sequence_edge():
    # Empty sequence should not raise, but produce empty output
    docs_json, render_items = standalone_docs_json_and_render_items([]) # 11.4μs -> 11.5μs (0.767% slower)







To edit these changes git checkout codeflash/optimize-standalone_docs_json_and_render_items-mhwtcl1l and push.

Codeflash Static Badge

The optimized code achieves an **8% speedup** through several targeted micro-optimizations that reduce function call overhead and improve algorithmic efficiency:

**Key Optimizations:**

1. **Dict access optimization in `standalone_docs_json_and_render_items`**: Replaced `if doc not in docs:` check followed by assignment with `docs.setdefault()`. This eliminates redundant hash table lookups, saving ~0.5% of total runtime.

2. **Short-circuit evaluation in `submodel_has_python_callbacks`**: Replaced explicit loop with early-break logic using `any()` generator expression. This stops iteration immediately when the first model with callbacks is found, avoiding unnecessary model traversal.

3. **List comprehension for render items**: Changed from appending items in a loop to a single list comprehension, reducing function call overhead and temporary variable assignments.

4. **Eliminated redundant imports in serialization functions**: Moved the `from ..core.types import ID` import out of `make_globally_unique_id()`, removing repeated import overhead that was consuming ~18% of the function's time according to the profiler.

5. **Optimized UUID generation in `make_globally_unique_css_safe_id`**: Direct string generation from `uuid.uuid4()` instead of calling `make_globally_unique_id()` and converting back, reducing function call overhead.

6. **Document model filtering improvement**: Changed list comprehension to explicit loop for `data_models` collection, which shows better performance characteristics in the profiler (63.5% vs 65.9% of `to_json` time).

**Impact on Hot Paths:**
The function is called from `notebook_content()` in Jupyter notebook rendering, making these optimizations valuable for interactive data science workflows where plots are frequently generated. The 8% improvement translates to faster notebook cell execution and better user experience when creating multiple visualizations.

**Test Case Performance:**
The optimizations show consistent 5-13% improvements across error handling test cases, indicating the changes maintain correctness while improving performance across different code paths.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 13, 2025 02:32
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 13, 2025
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash

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