Merge branch 'jp_documentation_edit' of https://github.com/ansys/pydp…

…f-core into jp_documentation_edit

doc/source/getting_started/compatibility.rst

@@ -8,7 +8,7 @@ Operating system
 ----------------
 
 DPF supports Windows 10 and Rocky Linux 8 and later.
-To run DPF on CentOS 7, use DPF for 2024 R2 (8.2) or older.
+To run DPF on CentOS 7, use DPF for 2024 R2 (8.2) or later.
 For more information, see `Ansys Platform Support <https://www.ansys.com/solutions/solutions-by-role/it-professionals/platform-support>`_.
 
 Client-server
@@ -23,7 +23,7 @@ version.
 
 As new features are developed, every attempt is made to ensure backward
 compatibility from the client to the server. Backward compatibility is generally ensured for
-the four latest Ansys versions. For example, the ``ansys-dpf-core`` module 0.8.0 version has been
+the four latest Ansys versions. For example, the ``ansys-dpf-core`` module 0.8.0 has been
 developed for Ansys 2023 R2 pre1 release, for 2023 R2 Ansys version. It is compatible with
 2023 R2, 2023 R1, 2022 R2 and 2022 R1 Ansys versions.
 
@@ -35,7 +35,7 @@ previous to 2022 R2.
 
 **Ansys strongly encourages you to use the latest packages available**, as far they are compatible
 with the server version you want to run. Considering Ansys 2023 R1 for example, if ``ansys-dpf-core``
-module with 0.10.0 version is the latest available compatible package, it should be used.
+module 0.10.0 is the latest available compatible package, it should be used.
 
 For ``ansys-dpf-core<0.10``, the `ansys.grpc.dpf <https://pypi.org/project/ansys-grpc-dpf/>`_
 package should also be synchronized with the server version.

doc/source/getting_started/dpf_server.rst

@@ -98,10 +98,10 @@ to use thanks to its ``ansys_path`` argument.
 PyDPF otherwise follows the logic below to automatically detect and choose which locally installed
 version of DPF Server to run:
 
-- it uses the ``ANSYS_DPF_PATH`` environment variable in priority if set and targeting a valid path to a DPF Server installation.
-- it then checks the currently active Python environment for any installed standalone DPF Server, and uses the latest version available.
-- it then checks for ``AWP_ROOTXXX`` environment variables, which are set by the **Ansys installer**, and uses the latest version available.
-- it then raises an error if all of the steps above failed to return a valid path to a DPF Server installation.
+- It uses the ``ANSYS_DPF_PATH`` environment variable in priority if set and targeting a valid path to a DPF Server installation.
+- It then checks the currently active Python environment for any installed standalone DPF Server, and uses the latest version available.
+- It then checks for ``AWP_ROOTXXX`` environment variables, which are set by the **Ansys installer**, and uses the latest version available.
+- It then raises an error if all of the preceding steps failed to return a valid path to a DPF Server installation.
 
 Run DPF Server in a Docker container
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

doc/source/getting_started/index.rst

@@ -6,7 +6,7 @@ Getting started
 
 The Data Processing Framework (DPF) provides numerical simulation users and engineers with a toolbox
 for accessing and transforming simulation data. DPF can access data from Ansys solver
-result files as well as from several neutral file formats (see :ref:`ref_main_index`).
+result files as well as from several neutral file formats. For more information, see :ref:`ref_main_index`.
 
 This **workflow-based** framework allows you to perform complex preprocessing and
 postprocessing operations on large amounts of simulation data.

doc/source/getting_started/install.rst

@@ -17,7 +17,7 @@ with this command:
    pip install ansys-dpf-core
 
 PyDPF-Core plotting capabilities require you to have `PyVista <https://pyvista.org/>`_ installed.
-To install PyDPF-Core with its optional plotting functionalities, use:
+To install PyDPF-Core with its optional plotting functionalities, run this command:
 
 .. code::
 
@@ -65,7 +65,7 @@ then use the following command from within this local directory:
 Note that PyDPF-Core wheelhouses do not include the optional plotting dependencies.
 To use the plotting capabilities, also download the wheels corresponding to your platform and Python interpreter version
 for `PyVista <https://pypi.org/project/pyvista/#files>`_ and 
-`matplotlib <https://pypi.org/project/matplotlib/#files>`_, then place them in the same previous local directory and run the command above.
+`matplotlib <https://pypi.org/project/matplotlib/#files>`_. Then, place them in the same local directory and run the preceding command.
 
 
 Install in development mode

doc/source/operator_reference.rst

@@ -29,11 +29,11 @@ server context.
 The available contexts are **Premium** and **Entry**.
 Licensed operators are marked as such in the documentation using the ``license`` property.
 Operators with the ``license`` property set to **None** do not require a license checkout.
-For more information about using these two contexts, see :ref:`user_guide_server_context`.
+For more information on using these two contexts, see :ref:`user_guide_server_context`.
 
 .. note::
 
-    For Ansys 2023 R1 and earlier, the context is equivalent to Premium, with all operators loaded.
+    For Ansys 2023 R1 and earlier, the context is equivalent to **Premium**, with all operators loaded.
     For DPF Server 2023.2.pre0 specifically, the server context defines which operators are loaded and
     accessible. Use the `PyDPF-Core 0.7 operator documentation <https://dpf.docs.pyansys.com/version/0.7/operator_reference.html>`_ to learn more.
     Some operators in the documentation might not be available for a particular server version.

doc/source/user_guide/concepts/waysofusing.rst

@@ -5,7 +5,7 @@ DPF capabilities and scripting languages
 ========================================
 DPF is a framework that provides data computation capabilities. 
 
-DPF as a Framework
+DPF as a framework
 ------------------
 
 DPF application: kernel and operator libraries
@@ -38,7 +38,7 @@ These capabilities can be accessed through client APIs, as shown here:
 
 Note that **IronPython and CPython APIs are different**, each has specific syntax.
 
-The **list of available operators when using DPF is independent from the language or API which is used**, it only depends
+The **list of available operators when using DPF is independent from the language or API which is used**. It depends only
 on how the DPF application has been initialized.
 
 Most of the DPF capabilities can be accessed using the operators. For more information on the existing operators, see :ref:`ref_dpf_operators_reference`.

doc/source/user_guide/custom_operators.rst

@@ -86,7 +86,7 @@ In the various properties for the class, specify the following:
   and whether it is optional and/or ellipsis (meaning that the specification is valid for pins going from pin
   number *x* to infinity)
 - List for operator properties, including name to use in the documentation and code generation and the
-  operator category. The optional ``license`` property allows you to define a required license to check out
+  operator category. The optional ``license`` property lets you define a required license to check out
   when running the operator. Set it equal to ``any_dpf_supported_increments`` to allow any license
   currently accepted by DPF (see :ref:`here<target_to_ansys_license_increments_list>`)