Use --no-build-isolation for build instructions to avoid downloading …

…code without permission; also various documentation fixes

README.md

@@ -12,7 +12,9 @@ For basic information see: doc/source/about.rst
 For installation instructions see: doc/source/installation.rst
 For a quickstart guide see: doc/source/quickstart.rst
 
-Basic installation is "pip install --no-deps ."
+Basic requirements are Python v3.8 or above with the following packages: numpy, setuptools, wheel
+
+Basic installation is "pip install --no-deps --no-build-isolation ."
 
 More detailed documentation is also available in doc/source/
 

demos/ande_viewer.dgp

@@ -34,7 +34,7 @@ else:
 
 print("The recordings from the file are located in a globalrevision \"g\".")
 print("You can interact with them in the viewer, or use them from Python:")
-print("You can list them with g.list_recordings() and g.list_recording_refs())")
+print("You can list them with g.list_recordings() and g.list_ndarray_refs())")
 print(" ")
 print("You can also use matplotlib, e.g.:")
 print("plt.plot(g.get_ndarray_ref(\"/my_channel\").data())")

doc/source/about.rst

@@ -103,6 +103,8 @@ Prerequisite dependencies
 Dataguzzler-Python is built on Python (tested on 3.8 and newer) on the following common and widely-used tools
 and libraries:
 
+  * setuptools: Python packaging support
+  * wheel: Python packaging support
   * numpy: Python numerical toolkit
   * pint: Python units library
   * PySide2 QT bindings (optional): GUI Python integration

doc/source/installation.rst

@@ -2,9 +2,9 @@ Installation
 ============
 
 Dataguzzler-Python is distributed as a Python source tree. You will
-need a Python installation (at least v3.8). Also install the
-prerequisite dependencies listed in the about section (above or
-in ``about.rst``.
+need a Python installation (at least v3.8) with the setuptools and
+wheel packages installed. Also install the prerequisite dependencies
+listed in the about section (above or in ``about.rst``).
 
 If you are also installing SpatialNDE2 (highly recommended), you
 may want to do that first, as on some platforms (Windows)
@@ -14,14 +14,14 @@ into the same environment.
 
 
 On Anaconda you will probably want at least the following packages:
-numpy scipy matplotlib cython ipython pip opencv pint lxml setuptools pyreadline pyserial pyside2 pyvisa git. If you considering installing SpatialNDE2, you will also want: 
+numpy scipy matplotlib cython ipython pip opencv pint lxml setuptools pyreadline pyserial pyside2 pyvisa git wheel. If you considering installing SpatialNDE2, you will also want: 
 clhpp pyopencl  hdf5 h5py  netcdf4 cmake openscenegraph pyopengl glfw freeglut glew mesa eigen swig.
 
 To create a new Anaconda environment with all these packages, run, for example:
 
 ::
    
-   conda create -n SNDE -c conda-forge python=3.9 numpy scipy matplotlib cython ipython pip opencv clhpp pyopencl pint hdf5 h5py lxml setuptools netcdf4 cmake openscenegraph pyopengl glfw freeglut glew mesa eigen swig pyreadline pyserial pyside2 hdf5 pyvisa git
+   conda create -n SNDE -c conda-forge python=3.9 numpy scipy matplotlib cython ipython pip opencv clhpp pyopencl pint hdf5 h5py lxml setuptools netcdf4 cmake openscenegraph pyopengl glfw freeglut glew mesa eigen swig pyreadline pyserial pyside2 hdf5 pyvisa git wheel
 
 You can then activate the Anaconda environment with:
 
@@ -36,7 +36,7 @@ Installation of Dataguzzler-Python is accomplished by running
 
 ::
 
-   pip install --no-deps .
+   pip install --no-deps --no-build-isolation .
 
 from a suitable terminal, command prompt, or Anaconda prompt corresponding
 to the desired installation enviroment.  In

doc/source/modules.rst

@@ -515,7 +515,7 @@ to an equivalent C++ shared pointer and then to a templated ``ndtyped_recording_
 
 Given variables from the SpatialNDE2 Python wrappers::
    g = recdb.latest_globalrev()
-   waveform = g.get_recording_ref("waveform")
+   waveform = g.get_ndarray_ref("waveform")
 
 Cython ``cdef`` C++ shared pointers can be obtained as follows using the produce/consume methods of the SpatialNDE2 Python wrappers::
 

doc/source/quickstart.rst

@@ -269,7 +269,7 @@ arrays, so if we want to access data arrays we usually need to access the
 recording data array reference ("recording ref") corresponding to the
 recording: ::
 
-   dgpy> r = g.get_recording_ref("/ss_greensinversion")
+   dgpy> r = g.get_ndarray_ref("/ss_greensinversion")
    200 000000000139 <spatialnde2.ndarray_recording_ref; proxy of <Swig Object of type 'std::shared_ptr< snde::ndarray_recording_ref > *' at 0x7f44c592e540> >
    dgpy>
    
@@ -432,10 +432,10 @@ arrays in memory so you can inspect them at your leisure.
 
 Given a global revision object stored in the variable ``globalrev``,
 you can list the recordings in a global revision with
-``globalrev.list_recordings()`` or the available array recording
-references with ``globalrev.list_recording_refs()``. Likewise you can
-obtain a recording or an array reference with
-``globalrev.get_recording()`` or ``globalrev.get_recording_ref()``
+``globalrev.list_recordings()`` or the available n-dimensional array recording
+references with ``globalrev.list_ndarray_refs()``. Likewise you can
+obtain a recording or an n-dimensional array reference with
+``globalrev.get_recording()`` or ``globalrev.get_ndarray_ref()``
 respectively. You can get an array reference from a recording
 with the ``.reference_ndarray()`` method of the recording.
 
@@ -447,7 +447,7 @@ details by converting it to a string, e.g.::
    str(rec.metadata)
 
 
-SpatialNDE2 metadata always immutable once the array is complete. With rare exceptions,
+SpatialNDE2 metadata is always immutable once the array is complete. With rare exceptions,
 SpatialNDE2 array data is supposed to be immutable once the array is complete
 ready so the return from ``.data()`` should be considered read-only.