Fixed spelling issues and added 7 new words to known list

Author: antonwolfy

doc/known_words.txt

@@ -13,11 +13,13 @@ dpctl
 dpnp
 dtype
 dtypes
+einsum
 endian
 eps
 epsneg
 Extrema
 finfo
+finiteness
 Fortran
 Frobenius
 Hypergeometric
@@ -34,7 +36,9 @@ NaT
 ndarray
 ndarrays
 ndim
+normed
 oneAPI
+orthonormal
 Penrose
 Polyutils
 prepend
@@ -48,13 +52,16 @@ subarray
 subarrays
 subclasses
 subtype
+SyclDevice
+SyclQueue
 th
 ufunc
 ufuncs
 Unary
 unicode
 usm
 Vandermonde
+vectorized
 von
 Weibull
 whitespace

dpnp/dpnp_iface_arraycreation.py

@@ -2148,7 +2148,7 @@ def geomspace(
     Returns
     -------
     out : dpnp.ndarray
-        num samples, equally spaced on a log scale.
+        `num` samples, equally spaced on a log scale.
 
     See Also
     --------
@@ -2340,8 +2340,8 @@ def linspace(
         The end value of the sequence, in any form that can be converted to
         an array. This includes scalars, lists, lists of tuples, tuples, tuples
         of tuples, tuples of lists, and ndarrays. If `endpoint` is set to
-        ``False`` the sequence consists of all but the last of num + 1 evenly
-        spaced samples, so that `stop` is excluded.
+        ``False`` the sequence consists of all but the last of ``num + 1``
+        evenly spaced samples, so that `stop` is excluded.
     dtype : dtype, optional
         The desired dtype for the array. If not given, a default dtype will be
         used that can represent the values (by considering Promotion Type Rule
@@ -2372,7 +2372,7 @@ def linspace(
     Returns
     -------
     out : dpnp.ndarray
-        There are num equally spaced samples in the closed interval
+        There are `num` equally spaced samples in the closed interval
         [`start`, `stop`] or the half-open interval [`start`, `stop`)
         (depending on whether `endpoint` is ``True`` or ``False``).
     step : float, optional
@@ -2381,12 +2381,12 @@ def linspace(
 
     See Also
     --------
-    :obj:`dpnp.arange` : Similar to `linspace`, but uses a step size (instead
-                         of the number of samples).
-    :obj:`dpnp.geomspace` : Similar to `linspace`, but with numbers spaced
-                            evenly on a log scale (a geometric progression).
-    :obj:`dpnp.logspace` : Similar to `geomspace`, but with the end points
-                           specified as logarithms.
+    :obj:`dpnp.arange` : Similar to :obj:`dpnp.linspace`, but uses a step size
+                         (instead of the number of samples).
+    :obj:`dpnp.geomspace` : Similar to :obj:`dpnp.linspace`, but with numbers
+                        spaced evenly on a log scale (a geometric progression).
+    :obj:`dpnp.logspace` : Similar to :obj:`dpnp.geomspace`, but with the end
+                           points specified as logarithms.
 
     Examples
     --------
@@ -2559,7 +2559,7 @@ def logspace(
         Input data, in any form that can be converted to an array. This
         includes scalars, lists, lists of tuples, tuples, tuples of tuples,
         tuples of lists, and ndarrays. `base` ** `stop` is the final value of
-        the sequence, unless `endpoint` is ``False``. In that case, num + 1
+        the sequence, unless `endpoint` is ``False``. In that case, ``num + 1``
         values are spaced over the interval in log-space, of which all but
         the last (a sequence of length num) are returned.
     num : int, optional
@@ -2585,7 +2585,7 @@ def logspace(
         tuples of lists, and ndarrays. The base of the log space, in any form
         that can be converted to an array.This includes scalars, lists, lists
         of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays.
-        The `step` size between the elements in ln(samples) / ln(base)
+        The `step` size between the elements in ``ln(samples) / ln(base)``
         (or log_base(samples)) is uniform. Default is 10.0.
     dtype : dtype, optional
         The desired dtype for the array. If not given, a default dtype will be
@@ -2599,18 +2599,17 @@ def logspace(
     Returns
     -------
     out: dpnp.ndarray
-        num samples, equally spaced on a log scale.
+        `num` samples, equally spaced on a log scale.
 
     See Also
     --------
-    :obj:`dpnp.arange` : Similar to linspace, with the step size specified
-                         instead of the number of samples. Note that, when used
-                         with a float endpoint, the endpoint may or may not be
-                         included.
-    :obj:`dpnp.linspace` : Similar to logspace, but with the samples uniformly
-                           distributed in linear space, instead of log space.
-    :obj:`dpnp.geomspace` : Similar to logspace, but with endpoints specified
-                            directly.
+    :obj:`dpnp.arange` : Similar to :obj:`dpnp.linspace`, with the step size
+        specified instead of the number of samples. Note that, when used with
+        a float endpoint, the endpoint may or may not be included.
+    :obj:`dpnp.linspace` : Similar to :obj:`dpnp.logspace`, but with the
+        samples uniformly distributed in linear space, instead of log space.
+    :obj:`dpnp.geomspace` : Similar to :obj:`dpnp.logspace`, but with endpoints
+                            specified directly.
 
     Examples
     --------
@@ -2665,7 +2664,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
 
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
-    one-dimensional coordinate arrays x1, x2,..., xn.
+    one-dimensional coordinate arrays ``x1, x2,..., xn``.
 
     For full documentation refer to :obj:`numpy.meshgrid`.
 
@@ -2674,7 +2673,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
     x1, x2,..., xn : {dpnp.ndarray, usm_ndarray}
         1-D arrays representing the coordinates of a grid.
     indexing : {'xy', 'ij'}, optional
-        Cartesian ('xy', default) or matrix ('ij') indexing of output.
+        Cartesian (``'xy'``, default) or matrix (``'ij'``) indexing of output.
     sparse : bool, optional
         If True the shape of the returned coordinate array for dimension `i`
         is reduced from ``(N1, ..., Ni, ... Nn)`` to
@@ -2686,11 +2685,11 @@ def meshgrid(*xi, copy=True, sparse=False, indexing="xy"):
     Returns
     -------
     X1, X2,..., XN : tuple of dpnp.ndarrays
-        For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
-        returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
-        or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
-        with the elements of `xi` repeated to fill the matrix along
-        the first dimension for `x1`, the second for `x2` and so on.
+        For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``, returns
+        ``(N1, N2, N3,..., Nn)`` shaped arrays if ``indexing='ij'`` or
+        ``(N2, N1, N3,..., Nn)`` shaped arrays if ``indexing='xy'`` with
+        the elements of `xi` repeated to fill the matrix along the first
+        dimension for `x1`, the second for `x2` and so on.
 
     Examples
     --------
@@ -2850,7 +2849,7 @@ class OGridClass:
     out : one dpnp.ndarray or tuple of dpnp.ndarray
         Returns a tuple of arrays,
         with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)
-        with dimensions[i] in the ith place.
+        with dimensions[i] in the i-th place.
 
     Examples
     --------

dpnp/dpnp_iface_indexing.py

@@ -424,7 +424,7 @@ def indices(
         If sparse is ``True``:
         Returns a tuple of arrays,
         with grid[i].shape = (1, ..., 1, dimensions[i], 1, ..., 1)
-        with dimensions[i] in the ith place.
+        with dimensions[i] in the i-th place.
 
     Examples
     --------

dpnp/dpnp_iface_nanfunctions.py

@@ -305,7 +305,7 @@ def nancumsum(a, axis=None, dtype=None, out=None):
         Input array.
     axis : int, optional
         Axis along which the cumulative sum is computed. The default (``None``)
-        is to compute the cumsum over the flattened array.
+        is to compute the cumulative sum over the flattened array.
     dtype : dtype, optional
         Type of the returned array and of the accumulator in which the elements
         are summed. If `dtype` is not specified, it defaults to the dtype of
@@ -862,7 +862,7 @@ def nanstd(
     out : dpnp.ndarray
         An array containing the standard deviations. If the standard
         deviation was computed over the entire array, a zero-dimensional
-        array is returned. If ddof is >= the number of non-NaN elements
+        array is returned. If `ddof` is >= the number of non-NaN elements
         in a slice or the slice contains only NaNs, then the result for
         that slice is NaN.
 
@@ -962,7 +962,7 @@ def nanvar(
     out : dpnp.ndarray
         An array containing the variances. If the variance was computed
         over the entire array, a zero-dimensional array is returned.
-        If ddof is >= the number of non-NaN elements in a slice or the
+        If `ddof` is >= the number of non-NaN elements in a slice or the
         slice contains only NaNs, then the result for that slice is NaN.
 
     Limitations

dpnp/linalg/dpnp_iface_linalg.py

@@ -546,8 +546,8 @@ def inv(a):
     """
     Compute the (multiplicative) inverse of a matrix.
 
-    Given a square matrix a, return the matrix ainv
-    satisfying ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
+    Given a square matrix `a`, return the matrix `ainv` satisfying
+    ``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
 
     For full documentation refer to :obj:`numpy.linalg.inv`.
 
@@ -848,7 +848,7 @@ def norm(x, ord=None, axis=None, keepdims=False):
         The default is ``None``.
     keepdims : bool, optional
         If this is set to ``True``, the axes which are normed over are left in
-        the result as dimensions with size one.  With this option the result
+        the result as dimensions with size one. With this option the result
         will broadcast correctly against the original `x`.
 
     Returns

dpnp/random/dpnp_iface_random.py

@@ -1474,17 +1474,19 @@ def seed(seed=None, device=None, sycl_queue=None):
     Parameters
     ----------
     device : {None, string, SyclDevice, SyclQueue}, optional
-        An array API concept of device where an array with generated numbers will be created.
-        The `device` can be ``None`` (the default), an OneAPI filter selector string,
-        an instance of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL device,
-        an instance of :class:`dpctl.SyclQueue`, or a `Device` object returned by
+        An array API concept of device where an array with generated numbers
+        will be created. The `device` can be ``None`` (the default), an OneAPI
+        filter selector string, an instance of :class:`dpctl.SyclDevice`
+        corresponding to a non-partitioned SYCL device, an instance of
+        :class:`dpctl.SyclQueue`, or a `Device` object returned by
         :obj:`dpnp.dpnp_array.dpnp_array.device` property.
     sycl_queue : {None, SyclQueue}, optional
         A SYCL queue to use for an array with generated numbers.
 
     Limitations
     -----------
-    Parameter `seed` is supported as either a scalar or an array of maximumum three integer scalars.
+    The `seed` parameter is supported as a scalar or an array of at most three
+    integer scalars.
 
     """
 

dpnp/random/dpnp_random_state.py

@@ -60,12 +60,13 @@ class RandomState:
     seed : {None, int, array_like}, optional
         A random seed to initialize the pseudo-random number generator.
         The `seed` can be ``None`` (the default), an integer scalar, or
-        an array_like of maximumum three integer scalars.
+        an array of at most three integer scalars.
     device : {None, string, SyclDevice, SyclQueue}, optional
         An array API concept of device where the output array is created.
-        The `device` can be ``None`` (the default), an OneAPI filter selector string,
-        an instance of :class:`dpctl.SyclDevice` corresponding to a non-partitioned SYCL device,
-        an instance of :class:`dpctl.SyclQueue`, or a `Device` object returned by
+        The `device` can be ``None`` (the default), an OneAPI filter selector
+        string, an instance of :class:`dpctl.SyclDevice` corresponding to
+        a non-partitioned SYCL device, an instance of :class:`dpctl.SyclQueue`,
+        or a `Device` object returned by
         :obj:`dpnp.dpnp_array.dpnp_array.device` property.
     sycl_queue : {None, SyclQueue}, optional
         A SYCL queue to use for output array allocation and copying.
@@ -498,7 +499,7 @@ def random_sample(self, size=None, usm_type="device"):
 
     def standard_normal(self, size=None, usm_type="device"):
         """
-        Draw samples from a standard Normal distribution (mean=0, stdev=1).
+        Draw samples from a standard Normal distribution ``(mean=0, stdev=1)``.
 
         For full documentation refer to :obj:`numpy.random.RandomState.standard_normal`.