Tweak copy keyword logic

src/finch/tensor.py

@@ -1,5 +1,6 @@
 import builtins
 from typing import Any, Callable, Optional, Iterable, Literal
+import warnings
 
 import numpy as np
 from numpy.core.numeric import normalize_axis_index, normalize_axis_tuple
@@ -91,7 +92,9 @@ def __init__(
         copy: bool | None = None,
     ):
         if isinstance(obj, (int, float, complex, bool, list)):
-            obj = np.array(obj, copy=copy)
+            if copy is False:
+                raise ValueError("copy=False isn't supported for scalar inputs and Python lists")
+            obj = np.asarray(obj)
         if fill_value is None:
             fill_value = 0.0
 
@@ -424,12 +427,12 @@ def _from_scipy_sparse(
         fill_value: np.number | None = None,
         copy: bool | None = None,
     ) -> JuliaObj:
-        if copy is False and not (x.has_canonical_format and x.format in ("coo", "csr", "csc")):
+        if copy is False and not (x.format in ("coo", "csr", "csc") and x.has_canonical_format):
             raise ValueError("Unable to avoid copy while creating an array as requested.")
-        if copy or not x.has_canonical_format:
-            x = x.copy()
         if x.format not in ("coo", "csr", "csc"):
             x = x.asformat("coo")
+        if copy:
+            x = x.copy()
         if not x.has_canonical_format:
             x.sum_duplicates()
             assert x.has_canonical_format

tests/test_scipy_constructors.py

@@ -112,9 +112,10 @@ def test_from_scipy_sparse(format_with_pattern, fill_value):
 def test_non_canonical_format(format):
     sp_arr = sp.random(3, 4, density=0.5, format=format)
 
-    with pytest.warns(
-        UserWarning, match="SciPy sparse input must be in a canonical format."
+    with pytest.raises(
+        ValueError, match="Unable to avoid copy while creating an array"
     ):
-        finch_arr = finch.asarray(sp_arr)
+        finch.asarray(sp_arr, copy=False)
 
+    finch_arr = finch.asarray(sp_arr)
     assert_equal(finch_arr.todense(), sp_arr.toarray())

tests/test_sparse.py

@@ -45,14 +45,16 @@ def test_wrappers(dtype, jl_dtype, order):
 @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.complex128])
 @pytest.mark.parametrize("order", ["C", "F", None])
 @pytest.mark.parametrize("copy", [True, False, None])
-def test_no_copy_fully_dense(dtype, order, copy, arr3d):
+def test_copy_fully_dense(dtype, order, copy, arr3d):
     arr = np.array(arr3d, dtype=dtype, order=order)
     arr_finch = finch.Tensor(arr, copy=copy)
     arr_todense = arr_finch.todense()
 
     assert_equal(arr_todense, arr)
-    assert np.shares_memory(arr_todense, arr)
-
+    if copy:
+        assert not np.shares_memory(arr_todense, arr)
+    else:
+        assert np.shares_memory(arr_todense, arr)
 
 def test_coo(rng):
     coords = (