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@codeflash-ai codeflash-ai bot commented Oct 25, 2025

📄 8% (0.08x) speedup for GDAL2Tiles.generate_openlayers in opendm/tiles/gdal2tiles.py

⏱️ Runtime : 660 microseconds 613 microseconds (best of 28 runs)

📝 Explanation and details

The optimized code achieves a 7% speedup by replacing inefficient string concatenation with a more performance-friendly approach using list accumulation and a single join operation.

Key optimizations applied:

  1. Dictionary initialization consolidation: The args dictionary is now constructed in one go using dictionary literal syntax instead of individual key assignments, reducing dictionary operations from ~10 separate assignments to a single initialization.

  2. Conditional expression for tmsoffset: Replaced an if-else block with a ternary expression ("-1" if self.options.tmscompatible else ""), eliminating a conditional branch.

  3. List-based string building: Instead of using repeated string concatenation (s += ...), which creates new string objects each time, the optimized version uses html_parts = [] and html_parts.append() to collect string fragments, then performs a single ''.join(html_parts) at the end.

  4. Profile variable caching: Stores self.options.profile in a local variable profile to avoid repeated attribute lookups in conditional statements.

Why this is faster:

  • String concatenation in Python creates new string objects for each += operation, leading to O(n²) behavior for n concatenations
  • List append operations are O(1), and the final join is O(n), making the overall complexity O(n)
  • Fewer dictionary key assignments reduce overhead during initialization
  • Local variable access (profile) is faster than repeated attribute access (self.options.profile)

Performance characteristics:
The optimization shows consistent 6-14% improvements across test cases, with particularly strong gains for edge cases like empty strings (13.5% faster) and large-scale operations (12.5% faster for large tile sizes). This suggests the optimization scales well with both simple and complex HTML generation scenarios.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 146 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import re

# imports
import pytest
from opendm.tiles.gdal2tiles import GDAL2Tiles

# function to test (GDAL2Tiles.generate_openlayers is defined above)

# Helper: Minimal mock options class for tests
class MockOptions:
    def __init__(
        self,
        title="Test Map",
        bingkey="BINGKEY",
        url="http://localhost/tiles",
        copyright="(c) Test",
        tmscompatible=False,
        profile="mercator",
        zoom="0-5",
        kml=False,
        resampling="near"
    ):
        self.title = title
        self.bingkey = bingkey
        self.url = url
        self.copyright = copyright
        self.tmscompatible = tmscompatible
        self.profile = profile
        self.zoom = zoom
        self.kml = kml
        self.resampling = resampling

# Helper: Minimal GDAL2Tiles instance setup for tests
def make_gdal2tiles(
    options=None,
    swne=(10.0, 20.0, 30.0, 40.0),
    tminz=0,
    tmaxz=5,
    tilesize=256,
    tileext="png",
    nativezoom=5,
    out_gt=(0, 1, 0, 0, 0, -1)
):
    if options is None:
        options = MockOptions()
    g = GDAL2Tiles("input.tif", "output", options)
    g.swne = swne
    g.tminz = tminz
    g.tmaxz = tmaxz
    g.tilesize = tilesize
    g.tileext = tileext
    g.nativezoom = nativezoom
    g.out_gt = out_gt
    return g

# ---------------------------
# 1. BASIC TEST CASES
# ---------------------------

def test_basic_mercator_html_contains_expected_strings():
    """Test that basic mercator profile output contains expected HTML and JS fragments."""
    g = make_gdal2tiles()
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 15.6μs -> 13.7μs (14.4% faster)



def test_basic_tmscompatible_flag():
    """Test that tmscompatible flag sets tmsoffset in geodetic profile."""
    options = MockOptions(profile="geodetic", tmscompatible=True)
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 11.6μs -> 10.5μs (10.0% faster)

def test_basic_custom_title_and_url():
    """Test that custom title and url are rendered correctly."""
    options = MockOptions(title="Custom Title", url="http://example.com/tiles")
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.6μs -> 11.4μs (10.6% faster)

# ---------------------------
# 2. EDGE TEST CASES
# ---------------------------

def test_edge_empty_title_and_copyright():
    """Test that empty title and copyright do not break output."""
    options = MockOptions(title="", copyright="")
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.9μs -> 11.4μs (13.5% faster)

def test_edge_extreme_swne_coordinates():
    """Test with extreme SWNE coordinates (world bounds)."""
    swne = (-90.0, -180.0, 90.0, 180.0)
    g = make_gdal2tiles(swne=swne)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.8μs -> 11.8μs (8.72% faster)

def test_edge_minmax_zoom_equal():
    """Test with minzoom == maxzoom (single zoom level)."""
    options = MockOptions(zoom="3")
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.2μs -> 11.5μs (5.84% faster)

def test_edge_tileext_variants():
    """Test with different tile extensions (jpg, webp)."""
    for ext in ["jpg", "webp"]:
        g = make_gdal2tiles(tileext=ext)
        codeflash_output = g.generate_openlayers(); html = codeflash_output # 21.9μs -> 20.2μs (8.06% faster)



def test_edge_invalid_zoom_string():
    """Test with invalid zoom string, should raise ValueError."""
    options = MockOptions(zoom="notazoom")
    with pytest.raises(ValueError):
        make_gdal2tiles(options=options)

def test_edge_large_tile_size():
    """Test with a very large tile size."""
    g = make_gdal2tiles(tilesize=1024)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.8μs -> 11.8μs (8.34% faster)

def test_edge_negative_zoom_levels():
    """Test negative zoom levels (should raise ValueError)."""
    options = MockOptions(zoom="-1--1")
    with pytest.raises(ValueError):
        make_gdal2tiles(options=options)

def test_edge_no_url():
    """Test with empty publish URL."""
    options = MockOptions(url="")
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 13.8μs -> 12.4μs (11.5% faster)

# ---------------------------
# 3. LARGE SCALE TEST CASES
# ---------------------------

def test_large_number_of_zoom_levels():
    """Test with a large number of zoom levels (0-20)."""
    options = MockOptions(zoom="0-20")
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.7μs -> 11.6μs (9.75% faster)

def test_large_tile_size_and_zoom():
    """Test with large tile size and zoom levels."""
    g = make_gdal2tiles(tilesize=512, tminz=0, tmaxz=10)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.6μs -> 11.2μs (12.5% faster)

def test_large_raster_profile():
    """Test raster profile with large zoom and tile size."""
    options = MockOptions(profile="raster", zoom="0-10")
    g = make_gdal2tiles(options=options)
    g.nativezoom = 10
    g.tmaxz = 10
    g.out_gt = (0, 1, 0, 0, 0, -1)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 13.9μs -> 12.2μs (13.8% faster)

def test_large_swne_coordinates_precision():
    """Test with SWNE coordinates with high floating point precision."""
    swne = (10.123456789, 20.987654321, 30.123456789, 40.987654321)
    g = make_gdal2tiles(swne=swne)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 14.1μs -> 13.2μs (6.88% faster)

def test_large_custom_copyright():
    """Test with a very long copyright string."""
    long_copyright = "Copyright " + "X" * 500
    options = MockOptions(copyright=long_copyright)
    g = make_gdal2tiles(options=options)
    codeflash_output = g.generate_openlayers(); html = codeflash_output # 12.2μs -> 11.3μs (7.89% faster)

def test_large_profile_switching():
    """Test switching between all profiles in a loop."""
    for profile in ["mercator", "geodetic", "raster"]:
        options = MockOptions(profile=profile)
        g = make_gdal2tiles(options=options)
        codeflash_output = g.generate_openlayers(); html = codeflash_output # 31.4μs -> 29.2μs (7.37% faster)
        # Each profile should include its unique JS
        if profile == "mercator":
            pass
        elif profile == "geodetic":
            pass
        elif profile == "raster":
            pass

def test_large_multiple_instances():
    """Test generating many instances and outputs in a loop for memory leaks or errors."""
    for i in range(50):
        options = MockOptions(title=f"Map {i}", url=f"http://example.com/tiles/{i}")
        g = make_gdal2tiles(options=options, swne=(i, i, i+1, i+1), tminz=i, tmaxz=i+5)
        codeflash_output = g.generate_openlayers(); html = codeflash_output # 390μs -> 367μs (6.27% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import re

# imports
import pytest
from opendm.tiles.gdal2tiles import GDAL2Tiles


# function to test
class Options:
    """Helper class to mock the options object used by GDAL2Tiles."""
    def __init__(
        self,
        title="Test Title",
        bingkey="BINGKEY",
        url="http://localhost/",
        copyright="(c) Test",
        tmscompatible=False,
        profile="mercator",
        zoom=None,
        kml=False
    ):
        self.title = title
        self.bingkey = bingkey
        self.url = url
        self.copyright = copyright
        self.tmscompatible = tmscompatible
        self.profile = profile
        self.zoom = zoom
        self.kml = kml
        self.resampling = "near"
from opendm.tiles.gdal2tiles import GDAL2Tiles

# -----------------------
# Unit tests start here
# -----------------------

# 1. Basic Test Cases

























#------------------------------------------------
from opendm.tiles.gdal2tiles import GDAL2Tiles

To edit these changes git checkout codeflash/optimize-GDAL2Tiles.generate_openlayers-mh5qykto and push.

Codeflash

The optimized code achieves a 7% speedup by replacing inefficient string concatenation with a more performance-friendly approach using list accumulation and a single join operation.

**Key optimizations applied:**

1. **Dictionary initialization consolidation**: The `args` dictionary is now constructed in one go using dictionary literal syntax instead of individual key assignments, reducing dictionary operations from ~10 separate assignments to a single initialization.

2. **Conditional expression for `tmsoffset`**: Replaced an if-else block with a ternary expression (`"-1" if self.options.tmscompatible else ""`), eliminating a conditional branch.

3. **List-based string building**: Instead of using repeated string concatenation (`s += ...`), which creates new string objects each time, the optimized version uses `html_parts = []` and `html_parts.append()` to collect string fragments, then performs a single `''.join(html_parts)` at the end.

4. **Profile variable caching**: Stores `self.options.profile` in a local variable `profile` to avoid repeated attribute lookups in conditional statements.

**Why this is faster:**
- String concatenation in Python creates new string objects for each `+=` operation, leading to O(n²) behavior for n concatenations
- List append operations are O(1), and the final join is O(n), making the overall complexity O(n)
- Fewer dictionary key assignments reduce overhead during initialization
- Local variable access (`profile`) is faster than repeated attribute access (`self.options.profile`)

**Performance characteristics:**
The optimization shows consistent 6-14% improvements across test cases, with particularly strong gains for edge cases like empty strings (13.5% faster) and large-scale operations (12.5% faster for large tile sizes). This suggests the optimization scales well with both simple and complex HTML generation scenarios.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 25, 2025 03:55
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 25, 2025
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