Avoid cast to float when operating on uint64 indices in a SparseNdarray.

Author: LTLA

src/delayedarray/SparseNdarray.py

@@ -1207,7 +1207,7 @@ def _extract_sparse_vector_to_dense(indices: numpy.ndarray, values: numpy.ndarra
         pass
     elif subset_summary.consecutive:
         start_pos = 0
-        first = subset_summary.first_index
+        first = indices.dtype.type(subset_summary.first_index) # avoid casting of uint64s to floats during subtraction.
         if subset_summary.search_first:
             start_pos = bisect_left(indices, first)
 
@@ -1276,7 +1276,7 @@ def _extract_sparse_vector_to_sparse(indices: numpy.ndarray, values: numpy.ndarr
 
     elif subset_summary.consecutive:
         start_pos = 0
-        first = subset_summary.first_index
+        first = indices.dtype.type(subset_summary.first_index) # avoid casting of uint64's to floats during subtraction.
         if subset_summary.search_first:
             start_pos = bisect_left(indices, first)
 

tests/test_SparseNdarray.py

@@ -365,6 +365,19 @@ def test_SparseNdarray_empty(mask_rate):
     assert spout.shape == (3, 4, 5)
 
 
+def test_SparseNdarray_u64_index():
+    test_shape = (120, 50)
+    y = simulate_SparseNdarray(test_shape, mask_rate=0, index_dtype=numpy.uint64)
+    ref = convert_SparseNdarray_to_numpy(y)
+
+    slices = (slice(70, 120),slice(10, 40))
+    sliced = y[slices]
+    assert_identical_ndarrays(convert_SparseNdarray_to_numpy(sliced), ref[slices])
+
+    dout = delayedarray.extract_dense_array(y, slices2ranges(slices, test_shape))
+    assert_identical_ndarrays(dout, ref[slices])
+
+
 #######################################################
 #######################################################
 

tests/utils.py

@@ -68,6 +68,7 @@ def mock_SparseNdarray_contents(
     lower: float = -1, 
     upper: float = 1, 
     dtype: numpy.dtype = numpy.dtype("float64"), 
+    index_dtype: numpy.dtype = numpy.dtype("int32"),
     mask_rate: float = 0
 ):
     if len(shape) == 1:
@@ -78,7 +79,7 @@ def mock_SparseNdarray_contents(
                 new_indices.append(i)
                 new_values.append(random.uniform(lower, upper))
 
-        new_indices = numpy.array(new_indices, dtype=numpy.int32)
+        new_indices = numpy.array(new_indices, dtype=index_dtype)
         new_values = numpy.array(new_values, dtype=dtype)
         if mask_rate:
             new_mask = numpy.random.rand(len(new_values)) < mask_rate
@@ -101,6 +102,7 @@ def mock_SparseNdarray_contents(
                     lower=lower,
                     upper=upper,
                     dtype=dtype,
+                    index_dtype=index_dtype,
                     mask_rate=mask_rate,
                 )
             )