a few doc updates

Author: newville

doc/faq.rst

@@ -4,34 +4,29 @@
 Frequently Asked Questions
 ==========================
 
-A list of common questions.
+A list of common questions and responses.
+
 
 What's the best way to ask for help or submit a bug report?
 ===========================================================
 
 See :ref:`support_chapter`.
 
 
-Why did my script break when upgrading from lmfit 0.8.3 to 0.9.0?
-=================================================================
+How should I cite LMFIT?
+========================
 
-See :ref:`whatsnew_090_label`.
+To cite LMFIT, see https://doi.org/10.5281/zenodo.598352
 
+Each release will have its own DOI, including:
 
-I get import errors from IPython
-================================
+Version 1.3.3  https://zenodo.org/records/12785036
+Version 1.3.2  https://zenodo.org/records/12785036
 
-If you see something like::
-
-    from IPython.html.widgets import Dropdown
-
-    ImportError: No module named 'widgets'
-
-then you need to install the ``ipywidgets`` package, try: ``pip install ipywidgets``.
 
 
 How can I fit multi-dimensional data?
-=====================================
+=========================================
 
 The fitting routines accept data arrays that are one-dimensional and double
 precision. So you need to convert the data and model (or the value
@@ -45,7 +40,7 @@ do this is to use :numpydoc:`ndarray.flatten`, for example::
 
 
 How can I fit multiple data sets?
-=================================
+==================================
 
 As above, the fitting routines accept data arrays that are one-dimensional
 and double precision. So you need to convert the sets of data and models
@@ -89,18 +84,9 @@ is that you also get access to the plot routines from the ModelResult
 class, which are also complex-aware.
 
 
-How should I cite LMFIT?
-========================
-
-For LMFIT 1.3.2, use https://doi.org/10.5281/zenodo.12785036.
-
-For other versions, see https://zenodo.org/records/12785036
-
-
-
 
 I get errors from NaN in my fit. What can I do?
-================================================
+===================================================
 
 The solvers used by lmfit use NaN (see
 https://en.wikipedia.org/wiki/NaN) values as signals that the calculation
@@ -217,7 +203,7 @@ used as discrete values.  This is discussed below in :ref:`faq_discrete_params`.
 .. _faq_params_no_uncertainties:
 
 Why are uncertainties in Parameters sometimes not determined?
-=============================================================
+================================================================
 
 In order for Parameter uncertainties to be estimated, each variable
 Parameter must actually change the fit, and cannot be stuck at an initial
@@ -327,3 +313,21 @@ look like this and give better fit results:
 The natural width of the error function is about 2 ``x`` units, but you can
 adjust this, shortening it with ``erf((x-x0)*2)`` to give a sharper
 transition for example.
+
+
+Why did my script break when upgrading from lmfit 0.8.3 to 0.9.0?
+=================================================================
+
+See :ref:`whatsnew_090_label`.
+
+
+I get import errors from IPython
+================================
+
+If you see something like::
+
+    from IPython.html.widgets import Dropdown
+
+    ImportError: No module named 'widgets'
+
+then you need to install the ``ipywidgets`` package, try: ``pip install ipywidgets``.

doc/support.rst

@@ -5,26 +5,30 @@ Getting Help
 ============
 
 .. _mailing list: https://groups.google.com/group/lmfit-py
+.. _github discussion: https://github.com/orgs/lmfit/discussions
 .. _github issues: https://github.com/lmfit/lmfit-py/issues
 
-If you have questions, comments, or suggestions for LMFIT, please use the
-`mailing list`_. This provides an on-line conversation that is both
-archived well and can be searched easily with standard web searches. If you
-find a bug in the code or documentation, use `GitHub Issues`_ to submit a
-report. If you have an idea for how to solve the problem and are familiar
-with Python and GitHub, submitting a GitHub Pull Request would be greatly
-appreciated.
+If you have questions, comments, or suggestions for LMFIT, please use
+the `mailing list`_ or `github discussion`_. These provide on-line
+conversation that are archived and can be searched easily with
+standard web searches.
+
+If you do find a bug in the code or documentation, use `GitHub
+Issues`_ to submit a report.  But if you have not submitted a bug
+report for LMFIT before, or if you are not sure that you have found a
+bug in LMFIT, please start a conversation on the `mailing list`_ or
+`github discussion`_.  A problem you are having may or may not be due
+to a bug in LMFIT. If it is due to a bug, creating an Issue from the
+conversation is easy. If it is not a bug, the problem will be
+discussed and then the Issue will be closed.  Starting the
+conversation on the mailing list or Discussions page with "How do I do
+this?" or "Why didn't this work the way I expected?"  instead of "This
+did not work as I expected, so your code must be broken" is generally
+preferred, and will better help others with similar questions.
 
-If you are unsure whether to use the mailing list or the Issue tracker,
-please start a conversation on the `mailing list`_. That is, the problem
-you're having may or may not be due to a bug. If it is due to a bug,
-creating an Issue from the conversation is easy. If it is not a bug, the
-problem will be discussed and then the Issue will be closed. While one
-*can* search through closed Issues on GitHub, these are not so easily
-searched, and the conversation is not easily useful to others later.
-Starting the conversation on the mailing list with "How do I do this?" or
-"Why didn't this work?" instead of "This should work and doesn't" is
-generally preferred, and will better help others with similar questions.
-Of course, there is not always an obvious way to decide if something is a
-Question or an Issue, and we will try our best to engage in all
-discussions.
+If you do submit a bug report, we expect that you understand how bug
+reporting works, and will provide the information needed to understand
+and reproduce that bug, such as a minimal verifiable example.  If you
+have an idea for how to solve the problem and are familiar with Python
+and GitHub, submitting a GitHub Pull Request would be greatly
+appreciated.