Merge branch 'master' into feat/exposing_quantity_types

Author: a-bouth

.ci/build_wheel.py

@@ -15,6 +15,10 @@
     "win": "win_amd64",
     "manylinux1": "manylinux1_x86_64",
     "manylinux_2_17": "manylinux_2_17_x86_64",
+    # Accommodate tox.ini automatic platform substitutions
+    "linux": "manylinux_2_17_x86_64",
+    "win32": "win_amd64",
+    "darwin": "any",
 }
 
 argParser = argparse.ArgumentParser()

.github/workflows/pydpf-post.yml

@@ -159,7 +159,7 @@ jobs:
         shell: bash
         working-directory: pydpf-post/tests
         run: |
-          pytest $DEBUG --reruns 2 .
+          pytest $DEBUG --maxfail=5 --reruns 2 .
         if: always()
         timeout-minutes: 60
 

doc/make.bat

@@ -9,6 +9,7 @@ if "%SPHINXBUILD%" == "" (
 )
 set SOURCEDIR=source
 set BUILDDIR=build
+set SPHINXOPTS=-n
 
 if "%1" == "" goto help
 if "%1" == "clean" goto clean

doc/source/_static/dpf_operators.html

@@ -1,4 +1,4 @@
-Here's how you would open a result file generated by Ansys MAPDL (or another Ansys solver) and 
+The following example shows how to open a result file generated by Ansys MAPDL (or another Ansys solver) and 
 extract results:
 
 .. code-block:: default

doc/source/_static/simple_example.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 2024R2 (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
@@ -23,8 +23,8 @@ 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 4 latest Ansys versions. For example, ``ansys-dpf-core`` module with 0.8.0 version has been
-developed for Ansys 2023 R2 pre1 release, for 2023 R2 Ansys version. It is compatible with
+the four latest Ansys versions. For example, the ``ansys-dpf-core`` module 0.8.0 has been
+developed for the Ansys 2023 R2 version. It is compatible with
 2023 R2, 2023 R1, 2022 R2 and 2022 R1 Ansys versions.
 
 Starting with version ``0.10`` of ``ansys-dpf-core``, the packages ``ansys-dpf-gate``,
@@ -34,8 +34,8 @@ and prevent synchronization issues between the PyDPF libraries, requiring to dro
 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.
+with the server version you want to run. Considering Ansys 2023 R1 for example, if ``ansys-dpf-core``
+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/compatibility.rst

@@ -11,12 +11,12 @@ simulation workflow.
 The DPF Server is packaged within the **Ansys installer** in Ansys 2021 R1 and later.
 
 It is also available as a standalone package that contains all the necessary files to run, enabling DPF capabilities.
-The standalone DPF Server is available on the `DPF Pre-Release page <https://download.ansys.com/Others/DPF%20Pre-Release>`_ of the Ansys Customer Portal.
+The standalone DPF Server is available on the `DPF Pre-Release page <https://download-archive.ansys.com/Others/DPF%20Pre-Release>`_ of the Ansys Customer Portal.
 The first standalone version of DPF Server available is 6.0 (2023 R2).
 
 The sections on this page describe how to install and use a standalone DPF Server.
 
-* For a quick start on using PyDPF, see :ref:`ref_getting_started`.
+* For a brief overview on using PyDPF, see :ref:`ref_getting_started`.
 * For more information on DPF and its use, see :ref:`ref_user_guide`.
 
 
@@ -65,7 +65,7 @@ PyDPF-Core is a Python client API communicating with a **DPF Server**, either
 through the network using gRPC or directly in the same process. PyDPF-Post is a Python
 module for postprocessing based on PyDPF-Core. 
 
-Both PyDPF-Core and PyDPF-Post can be used with DPF Server. Installation instructions
+Both PyDPF-Core and PyDPF-Post can be used with the DPF Server. Installation instructions
 for PyDPF-Core are available in the PyDPF-Core `Getting started <https://dpf.docs.pyansys.com/version/stable/getting_started/install.html>`_.
 Installation instructions for PyDPF-Post are available in the PyDPF-Post `Getting started <https://post.docs.pyansys.com/version/stable/getting_started/install.html>`_.
 
@@ -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.
-- if 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
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -111,7 +111,8 @@ DPF Server can be run in a Docker container.
    in :ref:`Install DPF Server <target_installing_server>`, download the ``Dockerfile`` file.
 #. Optional: download any other plugin ZIP file as appropriate. For example, to access the ``composites`` plugin for Linux, 
    download ``ansys_dpf_composites_lin_v2025.1.pre0.zip``.
-#. Copy all the ZIP files and ``Dockerfile`` file in a folder and navigate into that folder.
+#. Copy all the ZIP files and the ``Dockerfile`` file into a folder. 
+#. Navigate into the folder used in the previous step.
 #. To build the DPF Docker container, run the following command:
 
 .. code::

doc/source/getting_started/dpf_server.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 (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/index.rst

@@ -16,8 +16,8 @@ with this command:
 
    pip install ansys-dpf-core
 
-PyDPF-Core plotting capabilities require to have `PyVista <https://pyvista.org/>`_ installed.
-To install PyDPF-Core with its optional plotting functionalities, use:
+PyDPF-Core plotting capabilities require you to have `PyVista <https://pyvista.org/>`_ installed.
+To install PyDPF-Core with its optional plotting functionalities, run this command:
 
 .. code::
 
@@ -62,10 +62,10 @@ then use the following command from within this local directory:
 
    pip install --no-index --find-links=. ansys-dpf-core
 
-Beware that PyDPF-Core wheelhouses do not include the optional plotting dependencies.
-To allow for plotting capabilities, also download the wheels corresponding to your platform and Python interpreter version
+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/getting_started/install.rst

@@ -4,11 +4,10 @@
 Licensing
 =========
 
-This section details how to properly set up licensing, as well as what the user should expect in
-terms of limitations or license usage when running PyDPF scripts.
+This section describes how to properly set up licensing, as well as limitations and license usage when running PyDPF scripts.
 
-DPF follows a client-server architecture, which means that the PyDPF client library must interact with a running DPF Server.
-It either starts a DPF Server via a local installation of DPF Server, or it connects to an already running local or remote DPF Server.
+DPF follows a client-server architecture, so the PyDPF client library must interact with a running DPF Server.
+It either starts a DPF Server via a local DPF Server installation, or it connects to an already running local or remote DPF Server.
 
 DPF Server is packaged within the **Ansys installer** in Ansys 2021 R1 and later.
 It is also available as a standalone application.
@@ -20,12 +19,12 @@ For more information on installing DPF Server, see :ref:`ref_dpf_server`.
 License terms
 -------------
 
-When using the DPF Server from an Ansys installation, the user has already agreed to the licensing
+When using the DPF Server from an Ansys installation, you have already agreed to the licensing
 terms when installing Ansys.
 
-When using a standalone DPF Server, the user must accept the ``DPF Preview License Agreement``
+When using a standalone DPF Server, you must accept the ``DPF Preview License Agreement``
 by following the indications below.
-Starting a DPF Server without agreeing to the ``DPF Preview License Agreement`` throws an exception.
+Starting a DPF Server without agreeing to the ``DPF Preview License Agreement`` creates an exception.
 
 DPF Preview License Agreement
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -51,7 +50,7 @@ existing license for the edition and version of DPF Server that you intend to us
 Configure licensing
 -------------------
 
-If your machine does not have a local Ansys installation, you need to define where DPF should look for a valid license.
+If your machine does not have a local Ansys installation, you must define where DPF should look for a valid license.
 
 To use a local license file, set the ``ANSYSLMD_LICENSE_FILE`` environment
 variable to point to an Ansys license file ``<license_file_to_use>``:
@@ -85,12 +84,12 @@ License checks and usage
 ------------------------
 
 Some DPF operators require DPF to check for an existing license
-and some require DPF to check-out a compatible license increment.
+and some require DPF to checkout a compatible license increment.
 
-DPF is by default allowed to check-out license increments as needed.
+DPF is by default allowed to checkout license increments as needed.
 To change this behavior, see :ref:`here <licensing_server_context>`.
 
-To know if operators require a license increment check-out to run, check their ``license``
+To know if operators require a license increment checkout to run, check their ``license``
 attribute in :ref:`ref_dpf_operators_reference` or directly in Python by checking the operator's
 properties for a ``license`` key:
 
@@ -109,13 +108,13 @@ properties for a ``license`` key:
 
 
 To check which Ansys licensing increments correspond to ``any_dpf_supported_increments``,
-see :ref:`here<target_to_ansys_license_increments_list>`.
+see :ref:`Compatible Ansys license increments<target_to_ansys_license_increments_list>`.
 
-Even if an operator does not require a license check-out to run, most DPF operators still require
+Even if an operator does not require a license checkout to run, most DPF operators still require
 DPF to check for a reachable license server or license file.
 
-Operators which do not perform any kind of license check are source operators (data extraction
-operators) which do not perform any data transformation.
+Operators that do not perform any kind of license check are source operators (data extraction
+operators). These operators do not perform any data transformation.
 
 For example, when considering result operators, they perform data transformation if the requested
 location is not the native result location. In that case, averaging occurs which is considered