Use gather_scatter as default method so library works without numba

README.md

@@ -53,10 +53,10 @@ Four methods, each with selectable backends:
 | `lazy` | yes | n/a | yes |
 | `gather_scatter` | yes | yes | yes |
 
-- **`direct`** (default): Batch-parallel scatter convolution with thread-local dense buffers (numba only). For each image in parallel, scatters kernel-weighted input values into an L2-cache-sized accumulator buffer, then extracts nonzeros into CSR format. Uses a two-phase approach: a lightweight boolean counting pass (1-byte flags, no float arithmetic) determines exact output sizes, then the scatter pass writes directly to right-sized arrays with zero waste. Interior pixels (~92-100%) skip bounds checking entirely via precomputed safe regions. O(nnz × K) per image with no init overhead. Fastest method across nearly all configurations. Requires `numba`.
+- **`direct`**: Batch-parallel scatter convolution with thread-local dense buffers (numba only). For each image in parallel, scatters kernel-weighted input values into an L2-cache-sized accumulator buffer, then extracts nonzeros into CSR format. Uses a two-phase approach: a lightweight boolean counting pass (1-byte flags, no float arithmetic) determines exact output sizes, then the scatter pass writes directly to right-sized arrays with zero waste. Interior pixels (~92-100%) skip bounds checking entirely via precomputed safe regions. O(nnz × K) per image with no init overhead. Fastest method across nearly all configurations. Requires `numba`.
 - **`precomputed`**: Builds a sparse Toeplitz matrix at init; fast batched matmul. Best for large batches with the same kernel when numba is not available.
 - **`lazy`**: COO broadcasting, no init cost. Best for very sparse inputs with small batches.
-- **`gather_scatter`**: Per-kernel-position scatter into a dense accumulator. General-purpose method for sparse batched inputs.
+- **`gather_scatter`** (default): Per-kernel-position scatter into a dense accumulator. General-purpose method for sparse batched inputs. Uses `numba` automatically when available, and falls back to `numpy` otherwise.
 
 Backend selection:
 - **`numpy`**: scipy/numpy ops. Always available.
@@ -68,12 +68,12 @@ conv = sc.Toeplitz_convolution2d(
     x_shape=(100, 100),
     k=k,
     mode='same',
-    method='direct',       # default
-    backend='numba',       # auto-selected for direct
+    method='gather_scatter',  # default
+    backend=None,             # numba if installed, otherwise numpy
 )
 ```
 
-If `backend=None` (default), auto-selects `numba` for `direct` and `gather_scatter` (if installed), `numpy` otherwise.
+If `backend=None` (default), `gather_scatter` auto-selects `numba` when installed and falls back to `numpy` otherwise. Use `method='direct'` explicitly for the fastest numba-only implementation.
 
 ## References
 - Toeplitz convolution: [stackoverflow.com/a/51865516](https://stackoverflow.com/a/51865516), [alisaaalehi/convolution_as_multiplication](https://github.com/alisaaalehi/convolution_as_multiplication)

sparse_convolution/sparse_convolution.py

@@ -110,8 +110,9 @@ class Toeplitz_convolution2d():
             * ``'direct'``: ``'numba'`` (only option)
             \\n
             If ``None``, auto-selects the best available backend:
-            ``'numba'`` for ``'gather_scatter'`` and ``'direct'`` (if
-            installed), ``'numpy'`` otherwise.
+            ``'numba'`` for ``'gather_scatter'`` (if installed), ``'numpy'``
+            otherwise. ``'direct'`` requires ``backend='numba'`` and numba to
+            be installed.
         max_buffer_bytes (int):
             Maximum memory (bytes) for the dense accumulator buffer used by
             ``'gather_scatter'``. Controls chunk size for batch processing.
@@ -144,7 +145,7 @@ def __init__(
         mode: str = 'same',
         dtype: Optional[np.dtype] = None,
         verbose: Union[bool, int] = False,
-        method: str = 'direct',
+        method: str = 'gather_scatter',
         max_buffer_bytes: int = 256 * 1024 * 1024,
         backend: Optional[str] = None,
         device: Optional[str] = None,

tests/test_unit.py

@@ -118,3 +118,20 @@ def test_gather_scatter_numpy_all_zero_kernel_returns_empty_sparse_output():
     assert scipy.sparse.isspmatrix_csr(out)
     assert out.shape == x.shape
     assert out.nnz == 0
+
+
+def test_default_method_falls_back_without_numba(monkeypatch):
+    """Default construction should work in minimal scipy/numpy installs."""
+    import sparse_convolution.sparse_convolution as sc_module
+
+    monkeypatch.setattr(sc_module, "HAS_NUMBA", False)
+
+    x = np.array([[1.0, 0.0], [0.0, 2.0]])
+    k = np.array([[0.5]])
+
+    conv = Toeplitz_convolution2d(x_shape=x.shape, k=k, mode="same")
+    out = conv(x, batching=False)
+
+    assert conv.method == "gather_scatter"
+    assert conv.backend == "numpy"
+    assert np.allclose(out, scipy.signal.convolve2d(x, k, mode="same"))