fix docstring

Author: vtavana

dpnp/dpnp_iface_arraycreation.py

@@ -787,7 +787,7 @@ def empty_like(
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`
     Parameter `order` is supported with values ``"C"`` or ``"F"``.
     Parameter `subok` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1049,7 +1049,7 @@ def full_like(
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`
     Parameter `order` is supported only with values ``"C"`` and ``"F"``.
     Parameter `subok` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1395,7 +1395,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
 
     Limitations
     -----------
-    Each array instance from `xi` is supported as either :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`.
+    Each array instance from `xi` is supported as either :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
     Parameter `copy` is supported only with default value ``True``.
     Parameter `sparse` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1583,7 +1583,7 @@ def ones_like(
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`
     Parameter `order` is supported with values ``"C"`` or ``"F"``.
     Parameter `subok` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.
@@ -1753,7 +1753,7 @@ def tril(x1, /, *, k=0):
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray` with two or more dimensions.
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray` with two or more dimensions.
     Parameter `k` is supported only of integer data type.
     Otherwise the function will be executed sequentially on CPU.
 
@@ -1797,7 +1797,7 @@ def triu(x1, /, *, k=0):
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray` with two or more dimensions.
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray` with two or more dimensions.
     Parameter `k` is supported only of integer data type.
     Otherwise the function will be executed sequentially on CPU.
 
@@ -1985,7 +1985,7 @@ def zeros_like(
 
     Limitations
     -----------
-    Parameter `x1` is supported as :class:`dpnp.dpnp_array` or :class:`dpctl.tensor.usm_ndarray`
+    Parameter `x1` is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`
     Parameter `order` is supported with values ``"C"`` or ``"F"``.
     Parameter `subok` is supported only with default value ``False``.
     Otherwise the function will be executed sequentially on CPU.

dpnp/dpnp_iface_manipulation.py

@@ -1174,7 +1174,7 @@ def ravel(a, order="C"):
 
     Parameters
     ----------
-    x : {dpnp.array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array. The elements in `a` are read in the order specified by order,
         and packed as a 1-D array.
     order : {'C', 'F'}, optional
@@ -1187,7 +1187,7 @@ def ravel(a, order="C"):
 
     Returns
     -------
-    out : dpnp.array
+    out : dpnp.ndarray
         A contiguous 1-D array of the same subtype as `a`, with shape (a.size,).
 
     See Also
@@ -1220,7 +1220,7 @@ def repeat(a, repeats, axis=None):
 
     Parameters
     ----------
-    x : {dpnp.array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array.
     repeat : int or array of int
         The number of repetitions for each element. `repeats` is broadcasted to fit
@@ -1231,7 +1231,7 @@ def repeat(a, repeats, axis=None):
 
     Returns
     -------
-    out : dpnp.array
+    out : dpnp.ndarray
         Output array which has the same shape as `a`, except along the given axis.
 
     See Also

dpnp/dpnp_iface_mathematical.py

@@ -301,7 +301,7 @@ def angle(z, deg=False):
 
     Parameters
     ----------
-    x : {dpnp.array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array, expected to have a complex-valued floating-point data type.
     deg : bool, optional
         Return angle in degrees if True, radians if False (default).
@@ -448,12 +448,12 @@ def clip(a, a_min, a_max, *, out=None, order="K", **kwargs):
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}
+    a : {dpnp.ndarray, usm_ndarray}
         Array containing elements to clip.
-    a_min, a_max : {dpnp.array, usm_ndarray, None}
+    a_min, a_max : {dpnp.ndarray, usm_ndarray, None}
         Minimum and maximum value. If ``None``, clipping is not performed on the corresponding edge.
         Only one of `a_min` and `a_max` may be ``None``. Both are broadcast against `a`.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         The results will be placed in this array. It may be the input array for in-place clipping.
         `out` must be of the right shape to hold the output. Its type is preserved.
     order : {"C", "F", "A", "K", None}, optional
@@ -462,7 +462,7 @@ def clip(a, a_min, a_max, *, out=None, order="K", **kwargs):
 
     Returns
     -------
-    out : dpnp.array
+    out : dpnp.ndarray
         An array with the elements of `a`, but where values < `a_min` are replaced with `a_min`,
         and those > `a_max` with `a_max`.
 

dpnp/dpnp_iface_nanfunctions.py

@@ -70,14 +70,14 @@ def _replace_nan(a, val):
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}
+    a : {dpnp.ndarray, usm_ndarray}
         Input array.
     val : float
         NaN values are set to `val` before doing the operation.
 
     Returns
     -------
-    out : {dpnp.array}
+    out : {dpnp.ndarray}
         If `a` is of inexact type, return a copy of `a` with the NaNs
         replaced by the fill value, otherwise return `a`.
     mask: {bool, None}
@@ -108,13 +108,13 @@ def nanargmax(a, axis=None, out=None, *, keepdims=False):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int, optional
         Axis along which to search. If ``None``, the function must return
         the index of the maximum value of the flattened array.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool
@@ -176,13 +176,13 @@ def nanargmin(a, axis=None, out=None, *, keepdims=False):
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}
+    a : {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int, optional
         Axis along which to search. If ``None``, the function must return
         the index of the minimum value of the flattened array.
         Default: ``None``.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool
@@ -318,14 +318,14 @@ def nanmax(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int or tuple of ints, optional
         Axis or axes along which maximum values must be computed. By default,
         the maximum value must be computed over the entire array. If a tuple of integers,
         maximum values must be computed over multiple axes.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool
@@ -413,14 +413,14 @@ def nanmin(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int or tuple of ints, optional
         Axis or axes along which minimum values must be computed. By default,
         the minimum value must be computed over the entire array. If a tuple of integers,
         minimum values must be computed over multiple axes.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool, optional
@@ -603,7 +603,7 @@ def nanvar(
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}:
+    a : {dpnp.ndarray, usm_ndarray}:
         Input array.
     axis : int or tuple of ints, optional
         axis or axes along which the variances must be computed. If a tuple
@@ -614,7 +614,7 @@ def nanvar(
         Type to use in computing the standard deviation. For arrays of
         integer type the default real-valued floating-point data type is used,
         for arrays of float types it is the same as the array type.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         Alternative output array in which to place the result. It must have
         the same shape as the expected output but the type (of the calculated
         values) will be cast if necessary.

dpnp/dpnp_iface_searching.py

@@ -57,13 +57,13 @@ def argmax(a, axis=None, out=None, *, keepdims=False):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int, optional
         Axis along which to search. If ``None``, the function must return
         the index of the maximum value of the flattened array.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool
@@ -141,13 +141,13 @@ def argmin(a, axis=None, out=None, *, keepdims=False):
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}
+    a : {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int, optional
         Axis along which to search. If ``None``, the function must return
         the index of the minimum value of the flattened array.
         Default: ``None``.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool, optional

dpnp/dpnp_iface_statistics.py

@@ -72,7 +72,7 @@ def _count_reduce_items(arr, axis, where=True):
 
     Parameters
     ----------
-    arr : {dpnp.array, usm_ndarray}
+    arr : {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int or tuple of ints, optional
         axis or axes along which the number of items used in a reduction operation must be counted.
@@ -376,14 +376,14 @@ def max(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int or tuple of ints, optional
         Axis or axes along which maximum values must be computed. By default,
         the maximum value must be computed over the entire array. If a tuple of integers,
         maximum values must be computed over multiple axes.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool
@@ -573,14 +573,14 @@ def min(a, axis=None, out=None, keepdims=False, initial=None, where=True):
 
     Parameters
     ----------
-    a :  {dpnp.array, usm_ndarray}
+    a :  {dpnp.ndarray, usm_ndarray}
         Input array.
     axis : int or tuple of ints, optional
         Axis or axes along which minimum values must be computed. By default,
         the minimum value must be computed over the entire array. If a tuple of integers,
         minimum values must be computed over multiple axes.
         Default: ``None``.
-    out :  {dpnp.array, usm_ndarray}, optional
+    out :  {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     keepdims : bool, optional
@@ -672,7 +672,7 @@ def ptp(
 
     Limitations
     -----------
-    Input array is supported as :class:`dpnp.dpnp.array` or :class:`dpctl.tensor.usm_ndarray`.
+    Input array is supported as :class:`dpnp.dpnp.ndarray` or :class:`dpctl.tensor.usm_ndarray`.
 
     Examples
     --------
@@ -706,7 +706,7 @@ def std(
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}:
+    a : {dpnp.ndarray, usm_ndarray}:
         nput array.
     axis : int or tuple of ints, optional
         Axis or axes along which the variances must be computed. If a tuple
@@ -717,7 +717,7 @@ def std(
         Type to use in computing the standard deviation. For arrays of
         integer type the default real-valued floating-point data type is used,
         for arrays of float types it is the same as the array type.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         Alternative output array in which to place the result. It must have
         the same shape as the expected output but the type (of the calculated
         values) will be cast if necessary.
@@ -828,7 +828,7 @@ def var(
 
     Parameters
     ----------
-    a : {dpnp.array, usm_ndarray}:
+    a : {dpnp.ndarray, usm_ndarray}:
         Input array.
     axis : int or tuple of ints, optional
         axis or axes along which the variances must be computed. If a tuple
@@ -839,7 +839,7 @@ def var(
         Type to use in computing the variance. For arrays of integer type
         the default real-valued floating-point data type is used,
         for arrays of float types it is the same as the array type.
-    out : {dpnp.array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         Alternative output array in which to place the result. It must have
         the same shape as the expected output but the type (of the calculated
         values) will be cast if necessary.

dpnp/dpnp_iface_trigonometric.py

@@ -1345,14 +1345,14 @@ def logsumexp(x, axis=None, out=None, dtype=None, keepdims=False):
 
     Parameters
     ----------
-    x : {dpnp_array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array, expected to have a real-valued data type.
     axis : int or tuple of ints, optional
         Axis or axes along which values must be computed. If a tuple
         of unique integers, values are computed over multiple axes.
         If ``None``, the result is computed over the entire array.
         Default: ``None``.
-    out : {dpnp_array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     dtype : data type, optional
@@ -1432,7 +1432,7 @@ def reciprocal(
 
     Parameters
     ----------
-    x : {dpnp.array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array.
     out : {None, dpnp.ndarray}, optional
         Output array to populate.
@@ -1480,14 +1480,14 @@ def reduce_hypot(x, axis=None, out=None, dtype=None, keepdims=False):
 
     Parameters
     ----------
-    x : {dpnp_array, usm_ndarray}
+    x : {dpnp.ndarray, usm_ndarray}
         Input array, expected to have a real-valued data type.
     axis : int or tuple of ints, optional
         Axis or axes along which values must be computed. If a tuple
         of unique integers, values are computed over multiple axes.
         If ``None``, the result is computed over the entire array.
         Default: ``None``.
-    out : {dpnp_array, usm_ndarray}, optional
+    out : {dpnp.ndarray, usm_ndarray}, optional
         If provided, the result will be inserted into this array. It should
         be of the appropriate shape and dtype.
     dtype : data type, optional