Add vstack implementation

PROTO_tests/tests/array_manipulation_tests.py

@@ -0,0 +1,24 @@
+import arkouda as ak
+import numpy as np
+
+import pytest
+import json
+
+
+def get_server_max_array_dims():
+    try:
+        return json.load(open('serverConfig.json', 'r'))['max_array_dims']
+    except (ValueError, FileNotFoundError, TypeError, KeyError):
+        return 1
+
+
+class TestManipulationFunctions:
+    @pytest.mark.skipif(get_server_max_array_dims() < 2, reason="vstack requires server with 'max_array_dims' >= 2")
+    def test_vstack(self):
+        a = [ak.random.randint(0, 10, 25) for _ in range(4)]
+        n = [x.to_ndarray() for x in a]
+
+        n_vstack = np.vstack(n)
+        a_vstack = ak.vstack(a)
+
+        assert n_vstack.tolist() == a_vstack.to_list()

ServerModules.cfg

@@ -19,6 +19,7 @@ IndexingMsg
 JoinEqWithDTMsg
 KExtremeMsg
 LogMsg
+# ManipulationMsg
 OperatorMsg
 ParquetMsg
 RandMsg

arkouda/__init__.py

@@ -13,6 +13,7 @@
 from arkouda.sorting import *
 from arkouda.pdarraysetops import *
 from arkouda.pdarraycreation import *
+from arkouda.pdarraymanipulation import *
 from arkouda.numeric import *
 from arkouda.groupbyclass import *
 from arkouda.strings import *

arkouda/pdarraymanipulation.py

@@ -0,0 +1,73 @@
+from typing import Tuple, List, Literal, Union, Optional
+from typeguard import typechecked
+
+from arkouda.client import generic_msg
+from arkouda.pdarrayclass import pdarray, create_pdarray
+from arkouda.dtypes import dtype as akdtype
+
+import numpy as np
+
+__all__ = ["vstack"]
+
+
+@typechecked
+def vstack(
+    tup: Union[Tuple[pdarray], List[pdarray]],
+    *,
+    dtype: Optional[Union[type, str]] = None,
+    casting: Literal["no", "equiv", "safe", "same_kind", "unsafe"] = "same_kind",
+) -> pdarray:
+    """
+    Stack a sequence of arrays vertically (row-wise).
+
+    This is equivalent to concatenation along the first axis after 1-D arrays of
+    shape `(N,)` have been reshaped to `(1,N)`.
+
+    Parameters
+    ----------
+    tup : Tuple[pdarray]
+        The arrays to be stacked
+    dtype : Optional[Union[type, str]], optional
+        The data-type of the output array. If not provided, the output
+        array will be determined using `np.common_type` on the
+        input arrays Defaults to None
+    casting : {"no", "equiv", "safe", "same_kind", "unsafe"], optional
+        Controls what kind of data casting may occur - currently unused
+
+    Returns
+    -------
+
+    pdarray
+        The stacked array
+    """
+
+    if casting != "same_kind":
+        # TODO: wasn't clear from the docs what each of the casting options does
+        raise NotImplementedError(f"casting={casting} is not yet supported")
+
+    # ensure all arrays have the same number of dimensions
+    ndim = tup[0].ndim
+    for a in tup:
+        if a.ndim != ndim:
+            raise ValueError("all input arrays must have the same number of dimensions")
+
+    # establish the dtype of the output array
+    if dtype is None:
+        dtype_ = np.common_type(*[np.empty(0, dtype=a.dtype) for a in tup])
+    else:
+        dtype_ = akdtype(dtype)
+
+    # cast the input arrays to the output dtype if necessary
+    arrays = [a.astype(dtype_) if a.dtype != dtype_ else a for a in tup]
+
+    # stack the arrays along the first axis
+    return create_pdarray(
+        generic_msg(
+            cmd=f"stack{ndim}D",
+            args={
+                "names": list(arrays),
+                "n": len(arrays),
+                "axis": 0,
+            },
+        )
+    )

tests/base_test.py

@@ -11,6 +11,7 @@
     stop_arkouda_server,
 )
 
+
 """
 ArkoudaTest defines the base Arkouda test logic for starting up the arkouda_server at the
 launch of a unittest TestCase and shutting down the arkouda_server at the completion of