Merge from_sparse

nums/core/array/application.py

@@ -230,7 +230,11 @@ def loadtxt(
     def scalar(self, value):
         return BlockArray.from_scalar(value, self.km)
 
-    def array(self, array: Union[np.ndarray, sparse.COO, List[float]], block_shape: tuple = None):
+    def array(
+        self,
+        array: Union[np.ndarray, sparse.COO, List[float]],
+        block_shape: tuple = None,
+    ):
         if not isinstance(array, (np.ndarray, sparse.COO)):
             if array_utils.is_array_like(array):
                 array = np.array(array)

nums/core/array/sparse.py

@@ -307,10 +307,10 @@ def from_np(cls, arr, block_shape, copy, km):
         return rarr
 
     @classmethod
-    def from_sparse(cls, arr, block_shape, copy, km, fill_value=0):
+    def from_sparse(cls, arr, block_shape, copy, km):
         dtype_str = str(arr.dtype)
         grid = ArrayGrid(arr.shape, block_shape, dtype_str)
-        rarr = SparseBlockArray(grid, km, fill_value)
+        rarr = SparseBlockArray(grid, km)
         grid_entry_iterator = grid.get_entry_iterator()
         for grid_entry in grid_entry_iterator:
             grid_slice = grid.get_slice(grid_entry)

nums/numpy/api/creation.py

@@ -227,7 +227,7 @@ def array(object, dtype=None, copy=True, order="K", ndmin=0, subok=False) -> Blo
 
 
 def from_coo(a: sparse.COO):
-    assert(isinstance(a, sparse.COO))
+    assert isinstance(a, sparse.COO)
     dtype = np.__getattribute__(str(a.dtype))
     shape = a.shape
     app = _instance()

tests/core/array/test_sparse.py

@@ -11,20 +11,18 @@ def test_sparse_init(app_inst: ArrayApplication):
     x_ba = app_inst.array(x1, block_shape=(2, 2))
     x_sba = SparseBlockArray.from_ba(x_ba)
     assert x_sba.nnz == 8
-    assert x_sba.nbytes == 8 * 4 + 2 * 8 * 8
     y_ba = x_sba.to_ba()
     assert np.array_equal(x1, y_ba.get())
 
 
 def test_from_coo(app_inst: ArrayApplication):
     row_coords = [0, 1, 0, 1, 2, 2, 3, 3]
     col_coords = [3, 2, 2, 3, 0, 1, 0, 1]
-    values     = [1, 1, 1, 1, 2, 2, 2, 2]
+    values = [1, 1, 1, 1, 2, 2, 2, 2]
     x_sp = sparse.COO([row_coords, col_coords], values)
     x_de = x_sp.todense()
     x_sba = app_inst.array(x_sp, block_shape=(2, 2))
     assert x_sba.nnz == 8
-    assert x_sba.nbytes == 8 * 4 + 2 * 8 * 8
     y_ba = x_sba.to_ba()
     assert np.array_equal(x_de, y_ba.get())