V3.2.1

README.rst

@@ -3,7 +3,6 @@ uncertainties
 
 .. image:: https://readthedocs.org/projects/uncertainties/badge/?version=latest
    :target: https://uncertainties.readthedocs.io/en/latest/?badge=latest
-   :alt: Documentation Status
 .. image:: https://img.shields.io/pypi/v/uncertainties.svg
    :target: https://pypi.org/project/uncertainties/
 .. image:: https://pepy.tech/badge/uncertainties/week
@@ -13,29 +12,22 @@ uncertainties
 .. image:: https://img.shields.io/github/actions/workflow/status/lmfit/uncertainties/python-package.yml?logo=github%20actions
    :target: https://github.com/lmfit/uncertainties/actions/workflows/python-package.yml
 
-``uncertainties`` allows **calculations** such as (2 +/- 0.1)*2 = 4 +/-
-0.2 to be **performed transparently**.  Much more complex mathematical
-expressions involving numbers with uncertainties can also be evaluated
-directly.
-
-The ``uncertainties`` package **takes the pain and complexity out**
-of uncertainty calculations.
-
-**Detailed information** about this package can be found on its `main
-website`_.
+The ``uncertainties`` package allows calculations with values that have
+uncertaintes, such as (2 +/- 0.1)*2 = 4 +/- 0.2.  ``uncertainties`` takes the
+pain and complexity out of error propagation and calculations of values with
+uncertainties.  For more information, see https://uncertainties.readthedocs.io/
 
 Basic examples
 --------------
 
 .. code-block:: python
 
     >>> from uncertainties import ufloat
-
     >>> x = ufloat(2, 0.25)
     >>> x
     2.0+/-0.25
 
-    >>> square = x**2  # Transparent calculations
+    >>> square = x**2
     >>> square
     4.0+/-1.0
     >>> square.nominal_value
@@ -46,37 +38,33 @@ Basic examples
     >>> square - x*x
     0.0  # Exactly 0: correlations taken into account
 
-    >>> from uncertainties.umath import *  # sin(), etc.
+    >>> from uncertainties.umath import sin, cos  # and many more.
     >>> sin(1+x**2)
     -0.95892427466313845+/-0.2836621854632263
 
     >>> print (2*x+1000).derivatives[x]  # Automatic calculation of derivatives
     2.0
 
     >>> from uncertainties import unumpy  # Array manipulation
-    >>> random_vars = unumpy.uarray([1, 2], [0.1, 0.2])
-    >>> print random_vars
+    >>> varr = unumpy.uarray([1, 2], [0.1, 0.2])
+    >>> print(varr)
     [1.0+/-0.1 2.0+/-0.2]
-    >>> print random_vars.mean()
+    >>> print(varr.mean())
     1.50+/-0.11
-    >>> print unumpy.cos(random_vars)
+    >>> print(unumpy.cos(varr))
     [0.540302305868+/-0.0841470984808 -0.416146836547+/-0.181859485365]
 
 Main features
 -------------
 
-- **Transparent calculations with uncertainties**: **no or little
-  modification of existing code** is needed.  Similarly, the Python_ (or
-  IPython_) shell can be used as **a powerful calculator** that
-  handles quantities with uncertainties (``print`` statements are
-  optional, which is convenient).
+- **Transparent calculations with uncertainties**: Liittle or
+  no modification of existing code is needed to convert calculations of floats
+  to calculations of values with uncertainties.
 
 - **Correlations** between expressions are correctly taken into
-  account.  Thus, ``x-x`` is exactly zero, for instance (most
-  implementations found on the web yield a non-zero uncertainty for
-  ``x-x``, which is incorrect).
+  account.  Thus, ``x-x`` is exactly zero.
 
-- **Almost all mathematical operations** are supported, including most
+- **Most  mathematical operations** are supported, including most
   functions from the standard math_ module (sin,...).  Comparison
   operators (``>``, ``==``, etc.) are supported too.
 
@@ -112,12 +100,12 @@ Further details are in the `on-line documentation
 Git branches
 ------------
 
-The GitHub ``master`` branch is the latest development version. It should
-represent a stable pre-release and should pass the tests, but will be more
-experimental.  Tagged releases will be available on GitHub, and correspond to
-the releases to PyPI.  The GitHub ``gh-pages`` branch will contain a stable
-version of the documentation that can be viewed at
-<https://lmfit.github.io/uncertainties/`_.  Other Github branches should be
+The GitHub ``master`` branch is the latest development version, and is intended
+to be a stable pre-release version. It will be experimental, but should pass
+all tests..  Tagged releases will be available on GitHub, and correspond to the
+releases to PyPI.  The GitHub ``gh-pages`` branch will contain a stable test version
+of the documentation that can be viewed at
+`<https://lmfit.github.io/uncertainties/>`_.  Other Github branches should be
 treated as unstable and in-progress development branches.
 
 
@@ -127,10 +115,6 @@ License
 This package and its documentation are released under the `Revised BSD
 License <LICENSE.txt>`_.
 
-Voluntary donations
--------------------
-If you find this open-source software useful (e.g. in saving you time or helping you produce
-something valuable), please consider `donating $10 or more <https://www.paypal.com/donate/?cmd=_s-xclick&hosted_button_id=4TK7KNDTEDT4S>`_.
 
 History
 -------

pyproject.toml

@@ -8,11 +8,7 @@ version = "3.2.1"
 authors = [
     {name = "Eric O. LEBIGOT (EOL)", email = "[email protected]"},
 ]
-description = """\
-    Transparent calculations with uncertainties on the \
-    quantities involved (aka error propagation); \
-    fast calculation of derivatives').\
-    """
+description = "calculations with values with uncertainties, error propagation"
 readme = "README.rst"
 requires-python = ">=3.8"
 keywords = [