Update README.md

README.md

@@ -14,13 +14,14 @@ This enables the user to freely mix+match operations from both projects, getting
   * [Motivation](#motivation)
     * [Primary Use](#primary-use)
     * [Secondary Uses](#secondary-uses)
+  * [Principles](#principles)
   * [Working Usage Examples](#code-examples) 
   * [API documentation](#api-documentation)
+  * [Installation](#installation)
   * [Project Status](#project-status)
     * [Code stability](#code-stability)
     * [Iris and Xarray version compatibility](#iris-and-xarray-compatibility)
     * [Current Limitations](#known-limitations)
-  * [Principles](#principles)
   * [References](#references)
   * [Developer Notes](#developer-notes)
 
@@ -102,60 +103,72 @@ This could be added in future, but it is also true that many such operations (li
 indexing) may be better done using Iris/Xarray.
 
 
+# Principles
+  * ncdata represents NetCDF data as Python objects
+  * ncdata objects can be freely manipulated, independent of any data file
+  * ncdata variables can contain either real (numpy) or lazy (Dask) arrays
+  * ncdata can be losslessly converted to and from actual NetCDF files
+  * Iris or Xarray objects can be converted to and from ncdata, in the same way that 
+    they are read from and saved to NetCDF files
+  * **_translation_** between Xarray and Iris is based on conversion to ncdata, which
+    is in turn equivalent to file i/o
+     * thus, Iris/Xarray translation is equivalent to _saving_ from one
+       package into a file, then _loading_ the file in the other package
+  * ncdata exchanges variable data directly with Iris/Xarray, with no copying of real
+    data or computing of lazy data
+  * ncdata exchanges lazy arrays with files using Dask 'streaming', thus allowing
+    transfer of arrays larger than memory  
+
+
 # Code Examples
   * mostly TBD
   * proof-of-concept script for
     [netCDF4 file i/o](https://github.com/pp-mo/ncdata/blob/main/tests/integration/example_scripts/ex_ncdata_netcdf_conversion.py)
   * proof-of-concept script for
     [iris-xarray conversions](https://github.com/pp-mo/ncdata/blob/main/tests/integration/example_scripts/ex_iris_xarray_conversion.py)    
 
+
 # API documentation
   * see the [ReadTheDocs build](https://ncdata.readthedocs.io/en/latest/index.html)
 
+
+# Installation
+Not yet building as a package, or uploading to PyPI / conda-forge.  Though this is planned for future "proper" releases.
+
+Code is pure Python.  Download and add repo>/lib to PYTHONPATH.  
+An installation process will be provided shortly (Jan 2024) ...
+
 # Project Status
 ## Code Stability
+We intend to follow [PEP 440](https://peps.python.org/pep-0440/) or (older) [SemVer](https://semver.org/) versioning principles.
 
-Code here is all still experimental.   
-Current APIs are unsupported and not stable.  
-There is no release yet.
+Release version is at **"v0.0.1"**.  
+This is a first complete implementation, with functional operational of all public APIs.  
+A **release "v0.1.0"** will follow when build and deployment mechanisms are sorted out.  
+The code is however still experimental, and APIs are not stable (hence no major version yet).  
 
 ## Iris and Xarray Compatibility
 ### Iris
-  * **currently** functions with a test branch of Iris
-    * branch in pp-mo fork : https://github.com/pp-mo/iris/tree/bridge_support , *or*
-    * this PR in main Iris repo : https://github.com/SciTools/iris/pull/5216
-  * **in future** the necessary changes should be included and released with
-    (planned) [Iris 3.7](https://github.com/SciTools/iris/discussions/5209).
+  * tested working with latest Iris
+  * compatible with iris >= v3.7.0
+    * see : [support added in v3.7.0](https://scitools-iris.readthedocs.io/en/stable/whatsnew/3.7.html#internal)
 ### Xarray
   * appears working against 'current' Xarray at time of writing
-    * [v2023.03.0 (March 22, 2023)](https://docs.xarray.dev/en/latest/whats-new.html#v2023-03-0-march-22-2023)
+    * [v2023.11.0 (November 16, 2023)](https://docs.xarray.dev/en/latest/whats-new.html#v2023-11-0-nov-16-2023)
 
 ## Known limitations
 Unsupported features : not planned 
  * user-defined datatypes are not supported
    * this includes compound and variable-length types
 
 Unsupported features : planned for future release 
- * groups (not yet fully supported)
- * unlimited dimensions (not yet fully supported)
- * file compression and encoding options
+ * groups (not yet fully supported ?)
  * file output chunking control
 
-# Principles
-  * ncdata represents NetCDF data as Python objects
-  * ncdata objects can be freely manipulated, independent of any data file
-  * ncdata variables can contain either real (numpy) or lazy (Dask) arrays
-  * ncdata can be losslessly converted to and from actual NetCDF files
-  * Iris or Xarray objects can be converted to and from ncdata, in the same way that 
-    they are read from and saved to NetCDF files
-  * **_translation_** between Xarray and Iris is based on conversion to ncdata, which
-    is in turn equivalent to file i/o
-     * thus, Iris/Xarray translation is equivalent to _saving_ from one
-       package into a file, then _loading_ the file in the other package
-  * ncdata exchanges variable data directly with Iris/Xarray, with no copying of real
-    data or computing of lazy data
-  * ncdata exchanges lazy arrays with files using Dask 'streaming', thus allowing
-    transfer of arrays larger than memory  
+Untested features : probably done, pending test
+ * unlimited dimensions (not yet fully supported)
+ * file compression and encoding options
+ * iris and xarray load/save keywords generally
 
 # References
   * Iris issue : https://github.com/SciTools/iris/issues/4994