⚡️ Speed up function _combine_single_variable_hypercube by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for _combine_single_variable_hypercube in xarray/core/combine.py

⏱️ Runtime : 24.5 microseconds → 23.0 microseconds (best of 8 runs)

📝 Explanation and details

The optimized code achieves a 6% speedup through several targeted micro-optimizations that reduce Python overhead and improve data structure manipulation efficiency:

Key Optimizations:

1. Eliminated redundant attribute access: Pre-cached ds0.dims into ds0_dims to avoid repeated attribute lookups in the hot loop, and stored num_ds = len(datasets) upfront.

2. Replaced expensive built-in functions with manual loops:

   • Replaced any(index is None for index in indexes) with a manual loop that breaks early
   • Replaced all(index.equals(indexes[0]) for index in indexes[1:]) with a manual comparison loop that short-circuits on first mismatch
   • Both optimizations avoid generator overhead and enable early termination

3. Optimized pandas operations: Changed rank.astype(int).values - 1 to rank.to_numpy(int) - 1, which is more direct and avoids intermediate array creation.

4. Streamlined container operations:

   • Used generator expression pd.Index((index[0] for index in indexes)) instead of list comprehension to reduce memory allocation
   • Replaced next(iter(combined_ids.keys())) with next(iter(combined_ids)) since dict iteration defaults to keys
   • Changed (combined_ds,) = combined_ids.values() to combined_ds = next(iter(combined_ids.values())) for cleaner unpacking

5. Improved validation logic: In _check_dimension_depth_tile_ids(), replaced set(nesting_depths) != {nesting_depths[0]} with a manual loop that exits early, avoiding set creation overhead.

Performance Impact: These optimizations are particularly effective for the coordinate inference workflow where _infer_concat_order_from_coords processes multiple datasets with coordinate dimensions. Based on the function reference showing this is called from combine_by_coords - a public API function that handles multi-dimensional dataset combination - these micro-optimizations compound when processing larger numbers of datasets or when called repeatedly in data processing pipelines.

Test Case Benefits: The optimizations show consistent 6-10% improvements across different scenarios, with edge cases like empty input validation also benefiting from the streamlined control flow.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 3 Passed
⏪ Replay Tests	✅ 3 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	83.3%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from xarray.core.combine import _combine_single_variable_hypercube


# Minimal Dataset and Index mocks for testing
class MockIndex:
    """A minimal index class that mimics xarray's index API for testing."""

    def __init__(self, values):
        self._values = list(values)
        self.size = len(self._values)
        self.is_monotonic_increasing = all(
            self._values[i] <= self._values[i + 1] for i in range(len(self._values) - 1)
        )
        self.is_monotonic_decreasing = all(
            self._values[i] >= self._values[i + 1] for i in range(len(self._values) - 1)
        )

    def to_pandas_index(self):
        # Just return a tuple for testing purposes
        return type(
            "FakePandasIndex",
            (),
            {
                "equals": lambda self, other: tuple(self._values)
                == tuple(other._values),
                "is_monotonic_increasing": self.is_monotonic_increasing,
                "is_monotonic_decreasing": self.is_monotonic_decreasing,
                "size": self.size,
                "__getitem__": lambda self, idx: self._values[idx],
                "__len__": lambda self: self.size,
                "__iter__": lambda self: iter(self._values),
            },
        )()


class MockDataset:
    """A minimal Dataset class for testing _combine_single_variable_hypercube."""

    def __init__(self, dims, coords, data=None):
        # dims: tuple of dimension names
        # coords: dict of {dim: list of coordinate values}
        self.dims = dims
        self._coords = coords
        self._indexes = {dim: MockIndex(coords[dim]) for dim in dims if dim in coords}
        self.indexes = self._indexes  # For the final check
        self.data = data

    def __getitem__(self, key):
        # For checking if a dimension is a coordinate
        return self._coords[key]

    def __contains__(self, key):
        return key in self._coords


# Patch for _combine_1d, which is called deep inside the code
def _combine_1d(
    datasets, dim, compat, data_vars, coords, fill_value, join, combine_attrs
):
    # For testing, just return the first dataset
    # In reality, this would concatenate/merge datasets along dim
    # We'll just return a new MockDataset with merged coords
    if dim is None:
        # Merge, just return first
        return datasets[0]
    else:
        # Concatenate: stack coords along dim
        all_coords = []
        for ds in datasets:
            all_coords.extend(ds._coords[dim])
        # Remove duplicates, keep order
        seen = set()
        merged_coords = [x for x in all_coords if not (x in seen or seen.add(x))]
        # Create a new dataset with merged coords
        new_coords = dict(datasets[0]._coords)
        new_coords[dim] = merged_coords
        return MockDataset(datasets[0].dims, new_coords)


from xarray.core.combine import _combine_single_variable_hypercube

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

# ----------- Basic Test Cases -----------


def test_basic_single_dataset():
    """Test combining a single dataset returns itself."""
    ds1 = MockDataset(("x",), {"x": [0, 1, 2]})
    codeflash_output = _combine_single_variable_hypercube([ds1])
    result = codeflash_output  # 21.7μs -> 20.4μs (6.08% faster)


# ----------- Edge Test Cases -----------
def test_edge_empty_input():
    """Test ValueError is raised when no datasets are provided."""
    with pytest.raises(ValueError, match="At least one Dataset is required"):
        _combine_single_variable_hypercube([])  # 1.37μs -> 1.26μs (8.62% faster)

import itertools
from collections import Counter

import pandas as pd

# imports
import pytest
from xarray.core.combine import _combine_single_variable_hypercube


# Minimal Dataset mock for testing
class DummyIndex:
    """A dummy index class mimicking xarray's index behavior."""

    def __init__(self, values):
        self._values = list(values)
        self.size = len(self._values)
        self.is_monotonic_increasing = all(
            self._values[i] <= self._values[i + 1] for i in range(len(self._values) - 1)
        )
        self.is_monotonic_decreasing = all(
            self._values[i] >= self._values[i + 1] for i in range(len(self._values) - 1)
        )

    def to_pandas_index(self):
        return pd.Index(self._values)

    def equals(self, other):
        return list(self._values) == list(other._values)


class DummyDataset:
    """A minimal mock of xarray.Dataset for testing _combine_single_variable_hypercube."""

    def __init__(self, dims, coords):
        # dims: tuple of dimension names
        # coords: dict of {dim: list of coordinate values}
        self.dims = tuple(dims)
        self._indexes = {dim: DummyIndex(coords[dim]) for dim in dims}
        self.coords = coords
        self.indexes = self._indexes  # For final check
        # For _infer_concat_order_from_coords: treat dims as keys
        for dim in dims:
            setattr(self, dim, coords[dim])

    def __getitem__(self, key):
        # For checking if dim is a coordinate dimension
        return self.coords[key]


# Minimal dtypes mock
class dtypes:
    NA = None


# Minimal CombineAttrsOptions, CompatOptions, JoinOptions mocks
CombineAttrsOptions = str
CompatOptions = str
JoinOptions = str
from xarray.core.combine import _combine_single_variable_hypercube

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

# ----------- BASIC TEST CASES -----------


def test_empty_list_raises():
    # Should raise ValueError for empty input
    with pytest.raises(ValueError):
        _combine_single_variable_hypercube([])  # 1.43μs -> 1.30μs (10.3% faster)

⏪ Replay Tests and Runtime

To edit these changes git checkout codeflash/optimize-_combine_single_variable_hypercube-mi9pua8y and push.