Restoring content for PyDPF-Core from my messed-up attempt to resolve…

… merge conflicts

docs/source/getting_started/install.rst

@@ -9,12 +9,12 @@ Install using ``pip``
 
 `pip <https://pypi.org/project/pip/>`_ is the package installer for Python.
 
-To use PyDPF-Core with Ansys 2021 R2 or later, install a 0.2.* PyDPF-Core
-version with:
+To use PyDPF-Core with Ansys 2021 R2 or later, install teh latest version
+with:
 
 .. code::
 
-   pip install ansys-dpf-core<0.3.0
+   pip install ansys-dpf-core
 
 
 To use PyDPF-Core with Ansys 2021 R1, install the latest version
@@ -33,6 +33,18 @@ network isolation, download the latest wheel file from `PyDPF-Post
 GitHub <https://github.com/pyansys/pydpf-post>`_ or
 `PyDPF-Post PyPi <https://pypi.org/project/ansys-dpf-post/>`_.
 
+Install for a quick tryout
+--------------------------
+
+For a quick tryou, use:
+
+.. code::
+
+   from ansys.dpf.core import Model
+   from ansys.dpf.core import examples
+   model = Model(examples.simple_bar)
+   print(model)
+
 
 Install in development mode
 ---------------------------

docs/source/index.rst

@@ -21,42 +21,34 @@ The data in DPF is defined based on physics-agnostic mathematical quantities
 described in self-sufficient entities called *fields*. This allows DPF to be 
 a modular and easy-to-use tool with a large range of capabilities. 
 
-.. image:: images/dpf-flow.png
+.. image:: images/drawings/dpf-flow.png
   :width: 670
   :alt: DPF flow
 
-The ``ansys.dpf.post`` package leverages the ``ansys.dpf.core`` package, which
-is available at `PyDPF-Core GitHub <https://github.com/pyansys/DPF-Core>`_. With
-PyDPF-Core, you can build more advanced and customized DPF workflows.
+The ``ansys.dpf.core`` package provides a Python interface to DPF, enabling
+rapid postprocessing of a variety of Ansys file formats and physics solutions
+without ever leaving the Python environment.
 
 
 Brief demo
 ~~~~~~~~~~
-Here is how you open and plot a result file generated by Ansys Workbench or
-MAPDL:
+Here is how you open a result file generated by MAPDL (or another ANSYS solver)
+and extract results:
 
 .. code:: python
 
-    >>> from ansys.dpf import post
-    >>> from ansys.dpf.post import examples
-    >>> solution = post.load_solution(examples.multishells_rst)
-    >>> stress = solution.stress()
-    >>> stress.xx.plot_contour(show_edges=False)
+    >>> from ansys.dpf.core import Model
+    >>> from ansys.dpf.core import examples
+    >>> model = Model(examples.simple_bar)
+    >>> print(model)
 
 
-.. figure:: ./images/main_example.png
-    :width: 300pt
-
-    Basic stress contour plot
-
-Here is how you extract the raw data as a :class:`numpy.ndarray` array:
+Here is how you plot displacement results:
 
 .. code:: python
 
-   >>> stress.xx.get_data_at_field(0)
-   array([-3.37871094e+10, -4.42471752e+10, -4.13249463e+10, ...,
-           3.66408342e+10,  1.40736914e+11,  1.38633557e+11])
-
+    >>> disp = model.results.displacement().X()
+    model.metadata.meshed_region.plot(disp.outputs.fields_container())
 
 For comprehensive demos, see :ref:`gallery`.
 
@@ -66,20 +58,22 @@ Key features
 
 **Computational efficiency**
 
-PyDPF-Post is based on DPF, whose data framework localizes loading and
-postprocessing on the DPF server, enabling rapid postprocessing workflows
-because they are written in C and FORTRAN. Because DPF-Post presents results
-in a Pythonic manner, you can rapidly develop simple or complex postprocessing
-scripts.
+DPF is a modern framework based on new hardware architectures. Thanks
+to continued development, new capabilities are frequently added.
+
+**Generic interface**
+
+DPF is physics-agnostic, which means that its use is not limited to a particular
+field, physics solution, or file format.
 
+**Extensibility and customization**
+DPF is developed around two core entities:
 
-**Easy to use**
+- Data represented as a *field*
+- An *operator* to act upon this data
 
-The PyDPF-Post API automates the use of DPF's chained operators to make
-postprocessing easier. The PyDPF-Post documentation describes how you can
-use operators to compute results. This allows you to build your own custom,
-low-level scripts to enable fast postprocessing of potentially multi-gigabyte
-models using `PyDPF-Core <https://github.com/pyansys/pydpf-core>`_.
+Each DPF capability is developed through operators that allow for componentization
+of the framework. Because DPF is plugin-based, new features or formats can be easily added.
 
 
 .. toctree::

docs/source/user_guide/index.rst

@@ -10,10 +10,8 @@ computation, customization, and remote postprocessing accessible in Python.
 
 This section has the following goals:
 
- - Describe basic DPF concepts, including terminology
-
- - Describe the most-used DPF entities and how they can help you to access and modify solver data
-
+ - Describe basic DPF concepts, including terminology.
+ - Describe the most-used DPF entities and how they can help you to access and modify solver data.
  - Provide simple how-tos for tackling most common use cases.
 
 Other sections of this guide include :ref:`ref_api_section`, :ref:`ref_dpf_operators_reference`,