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@codeflash-ai codeflash-ai bot commented Dec 9, 2025

📄 13% (0.13x) speedup for GraphicsContextPdf.clip_cmd in lib/matplotlib/backends/backend_pdf.py

⏱️ Runtime : 21.3 microseconds 18.8 microseconds (best of 16 runs)

📝 Explanation and details

The optimized code achieves a 13% speedup through several targeted micro-optimizations that reduce Python's attribute lookup overhead and object creation costs:

Key optimizations:

  1. Reduced attribute lookups in pathOperations: The original code performed repeated Op.moveto.value, Op.lineto.value, etc. lookups inside the list literal on every call. The optimized version extracts these values into local variables once, eliminating redundant attribute access. This is particularly effective since pathOperations is a static method called frequently during PDF path rendering.

  2. Variable localization in push() and pop(): The optimized code stores self.file and self.parent in local variables to avoid repeated attribute lookups. In pop(), parent = self.parent eliminates multiple self.parent accesses, which is especially beneficial since pop() can be called many times in loops.

  3. Optimized tuple comparisons in clip_cmd(): The original code repeatedly accessed self._cliprect and self._clippath in tuple comparisons. The optimized version caches these values as cr, cp and trg_cr, trg_cp, reducing attribute access overhead in the tight loop where clipping state is checked.

  4. Precomputed Name cache for procsets: The optimized code moves the Name object creation for PDF procsets outside the resource dictionary construction, avoiding repeated object instantiation during PDF initialization.

Performance impact: The line profiler shows that while the total time for individual functions remains similar, the optimizations reduce per-hit costs for frequently executed operations. The test results demonstrate 11.8-14.9% improvements in clipping operations, which are critical for PDF rendering performance where graphics contexts are frequently pushed/popped during complex drawing operations.

These optimizations are particularly valuable for matplotlib's PDF backend, which handles numerous graphics state changes during plot rendering, making the cumulative effect of these micro-optimizations significant for complex figures with many elements.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 6 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
# imports
from matplotlib.backends.backend_pdf import GraphicsContextPdf

# Minimal stubs and mocks for dependencies


class Op:
    # Simulate the enum Op used in the code
    gsave = "gsave"
    grestore = "grestore"
    rectangle = "rectangle"
    clip = "clip"
    endpath = "endpath"


class Verbatim(str):
    # Dummy Verbatim string for PDF path
    pass


class DummyPath:
    # Dummy path object for clippath
    def __init__(self, id=0):
        self.id = id

    def get_transformed_path_and_affine(self):
        # Return a dummy path and affine
        return f"path-{self.id}", f"affine-{self.id}"


# ========== BASIC TEST CASES ==========


def test_clip_cmd_no_change_returns_nothing():
    # If the current cliprect and clippath are already set, no commands should be returned.
    ctx = GraphicsContextPdf(file=None)
    cliprect = (0, 0, 10, 10)
    clippath = DummyPath(1)
    ctx._cliprect = cliprect
    ctx._clippath = clippath
    codeflash_output = ctx.clip_cmd(cliprect, clippath)
    cmds = codeflash_output  # 10.8μs -> 9.44μs (14.9% faster)


def test_clip_cmd_cliprect_change_only():
    # If only cliprect changes, rectangle/clip/endpath should be issued
    ctx = GraphicsContextPdf(file=None)
    old_cliprect = (0, 0, 10, 10)
    new_cliprect = (1, 2, 3, 4)
    clippath = DummyPath(1)
    ctx._cliprect = old_cliprect
    ctx._clippath = clippath
    codeflash_output = ctx.clip_cmd(new_cliprect, clippath)
    cmds = codeflash_output  # 10.4μs -> 9.32μs (11.8% faster)

To edit these changes git checkout codeflash/optimize-GraphicsContextPdf.clip_cmd-miyoj9t7 and push.

Codeflash Static Badge

The optimized code achieves a **13% speedup** through several targeted micro-optimizations that reduce Python's attribute lookup overhead and object creation costs:

**Key optimizations:**

1. **Reduced attribute lookups in `pathOperations`**: The original code performed repeated `Op.moveto.value`, `Op.lineto.value`, etc. lookups inside the list literal on every call. The optimized version extracts these values into local variables once, eliminating redundant attribute access. This is particularly effective since `pathOperations` is a static method called frequently during PDF path rendering.

2. **Variable localization in `push()` and `pop()`**: The optimized code stores `self.file` and `self.parent` in local variables to avoid repeated attribute lookups. In `pop()`, `parent = self.parent` eliminates multiple `self.parent` accesses, which is especially beneficial since `pop()` can be called many times in loops.

3. **Optimized tuple comparisons in `clip_cmd()`**: The original code repeatedly accessed `self._cliprect` and `self._clippath` in tuple comparisons. The optimized version caches these values as `cr, cp` and `trg_cr, trg_cp`, reducing attribute access overhead in the tight loop where clipping state is checked.

4. **Precomputed Name cache for procsets**: The optimized code moves the `Name` object creation for PDF procsets outside the resource dictionary construction, avoiding repeated object instantiation during PDF initialization.

**Performance impact**: The line profiler shows that while the total time for individual functions remains similar, the optimizations reduce per-hit costs for frequently executed operations. The test results demonstrate **11.8-14.9% improvements** in clipping operations, which are critical for PDF rendering performance where graphics contexts are frequently pushed/popped during complex drawing operations.

These optimizations are particularly valuable for matplotlib's PDF backend, which handles numerous graphics state changes during plot rendering, making the cumulative effect of these micro-optimizations significant for complex figures with many elements.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 December 9, 2025 14:32
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Dec 9, 2025
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