Move _write_array to Python Array class

Author: kounelisagis

tiledb/array.py

@@ -6,6 +6,7 @@
 import tiledb.cc as lt
 
 from .ctx import Config, Ctx, default_ctx
+from .datatypes import DataType
 from .domain_indexer import DomainIndexer
 from .enumeration import Enumeration
 from .metadata import Metadata
@@ -769,6 +770,177 @@ def domain_index(self):
     def dindex(self):
         return self.domain_index
 
+    def _write_array(
+        tiledb_array,
+        subarray,
+        coordinates: list,
+        buffer_names: list,
+        values: list,
+        labels: dict,
+        nullmaps: dict,
+        issparse: bool,
+    ):
+        # used for buffer conversion (local import to avoid circularity)
+        from .main import array_to_buffer
+
+        isfortran = False
+        nattr = len(buffer_names)
+        nlabel = len(labels)
+
+        # Create arrays to hold buffer sizes
+        nbuffer = nattr + nlabel
+        if issparse:
+            nbuffer += tiledb_array.schema.ndim
+        buffer_sizes = np.zeros((nbuffer,), dtype=np.uint64)
+
+        # Create lists for data and offset buffers
+        output_values = list()
+        output_offsets = list()
+
+        # Set data and offset buffers for attributes
+        for i in range(nattr):
+            # if dtype is ASCII, ensure all characters are valid
+            if tiledb_array.schema.attr(i).isascii:
+                try:
+                    values[i] = np.asarray(values[i], dtype=np.bytes_)
+                except Exception as exc:
+                    raise tiledb.TileDBError(
+                        f'dtype of attr {tiledb_array.schema.attr(i).name} is "ascii" but attr_val contains invalid ASCII characters'
+                    )
+
+            attr = tiledb_array.schema.attr(i)
+
+            if attr.isvar:
+                try:
+                    if attr.isnullable:
+                        if np.issubdtype(attr.dtype, np.str_) or np.issubdtype(
+                            attr.dtype, np.bytes_
+                        ):
+                            attr_val = np.array(
+                                ["" if v is None else v for v in values[i]]
+                            )
+                        else:
+                            attr_val = np.nan_to_num(values[i])
+                    else:
+                        attr_val = values[i]
+                    buffer, offsets = array_to_buffer(attr_val, True, False)
+                except Exception as exc:
+                    raise type(exc)(
+                        f"Failed to convert buffer for attribute: '{attr.name}'"
+                    ) from exc
+            else:
+                buffer, offsets = values[i], None
+
+            buffer_sizes[i] = buffer.nbytes // (attr.dtype.itemsize or 1)
+            output_values.append(buffer)
+            output_offsets.append(offsets)
+
+        # Check value layouts
+        if len(values) and nattr > 1:
+            value = output_values[0]
+            isfortran = value.ndim > 1 and value.flags.f_contiguous
+            for value in values:
+                if value.ndim > 1 and value.flags.f_contiguous and not isfortran:
+                    raise ValueError("mixed C and Fortran array layouts")
+
+        # Set data and offsets buffers for dimensions (sparse arrays only)
+        ibuffer = nattr
+        if issparse:
+            for dim_idx, coords in enumerate(coordinates):
+                dim = tiledb_array.schema.domain.dim(dim_idx)
+                if dim.isvar:
+                    buffer, offsets = array_to_buffer(coords, True, False)
+                    buffer_sizes[ibuffer] = buffer.nbytes // (dim.dtype.itemsize or 1)
+                else:
+                    buffer, offsets = coords, None
+                    buffer_sizes[ibuffer] = buffer.nbytes // (dim.dtype.itemsize or 1)
+                output_values.append(buffer)
+                output_offsets.append(offsets)
+
+                name = dim.name
+                buffer_names.append(name)
+
+                ibuffer = ibuffer + 1
+
+        for label_name, label_values in labels.items():
+            # Append buffer name
+            buffer_names.append(label_name)
+            # Get label data buffer and offsets buffer for the labels
+            dim_label = tiledb_array.schema.dim_label(label_name)
+            if dim_label.isvar:
+                buffer, offsets = array_to_buffer(label_values, True, False)
+                buffer_sizes[ibuffer] = buffer.nbytes // (dim_label.dtype.itemsize or 1)
+            else:
+                buffer, offsets = label_values, None
+                buffer_sizes[ibuffer] = buffer.nbytes // (dim_label.dtype.itemsize or 1)
+            # Append the buffers
+            output_values.append(buffer)
+            output_offsets.append(offsets)
+
+            ibuffer = ibuffer + 1
+
+        # Allocate the query
+        ctx = lt.Context(tiledb_array.ctx)
+        q = lt.Query(ctx, tiledb_array.array, lt.QueryType.WRITE)
+
+        # Set the layout
+        layout = (
+            lt.LayoutType.UNORDERED
+            if issparse
+            else (lt.LayoutType.COL_MAJOR if isfortran else lt.LayoutType.ROW_MAJOR)
+        )
+        q.layout = layout
+
+        # Create and set the subarray for the query (dense arrays only)
+        if not issparse:
+            q.set_subarray(subarray)
+
+        # Set buffers on the query
+        for i, buffer_name in enumerate(buffer_names):
+            # Set data buffer
+            ncells = DataType.from_numpy(output_values[i].dtype).ncells
+            q.set_data_buffer(
+                buffer_name,
+                output_values[i],
+                buffer_sizes[i] * ncells,
+            )
+
+            # Set offsets buffer
+            if output_offsets[i] is not None:
+                output_offsets[i] = output_offsets[i].astype(np.uint64)
+                q.set_offsets_buffer(
+                    buffer_name, output_offsets[i], output_offsets[i].size
+                )
+
+            # Set validity buffer
+            if buffer_name in nullmaps:
+                nulmap = nullmaps[buffer_name]
+                q.set_validity_buffer(buffer_name, nulmap, nulmap.size)
+
+        q._submit()
+        q.finalize()
+
+        fragment_info = tiledb_array.last_fragment_info
+        if fragment_info is not False:
+            if not isinstance(fragment_info, dict):
+                raise ValueError(
+                    f"Expected fragment_info to be a dict, got {type(fragment_info)}"
+                )
+            fragment_info.clear()
+
+            result = dict()
+            num_fragments = q.fragment_num()
+
+            if num_fragments < 1:
+                return result
+
+            for fragment_idx in range(0, num_fragments):
+                fragment_uri = q.fragment_uri(fragment_idx)
+                fragment_t1, fragment_t2 = q.fragment_timestamp_range(fragment_idx)
+                result[fragment_uri] = (fragment_t1, fragment_t2)
+
+            fragment_info.update(result)
+
     def label_index(self, labels):
         """Retrieve data cells with multi-range, domain-inclusive indexing by label.
         Returns the cross-product of the ranges.

tiledb/dense_array.py

@@ -575,11 +575,7 @@ def _setitem_impl(self, selection, val, nullmaps: dict):
                         f"validity bitmap, got {type(val)}"
                     )
 
-        from .libtiledb import _write_array_wrapper
-
-        _write_array_wrapper(
-            self, subarray, [], attributes, values, labels, nullmaps, False
-        )
+        self._write_array(subarray, [], attributes, values, labels, nullmaps, False)
 
     def __array__(self, dtype=None, **kw):
         """Implementation of numpy __array__ protocol (internal).