Merge pull request #858 from lmfit/v120_rc1

update whatsnew, add discussion of data coercion

doc/Makefile

@@ -20,7 +20,6 @@ ALLSPHINXOPTS   = $(SPHINX_OUTPUT) $(SPHINXOPTS) .
 html:  gallery
 	cp sphinx/ext_mathjax.py extensions.py
 	$(SPHINXBUILD) -b html $(SPHINX_OUTPUT) $(SPHINX_OPTS) . $(BUILDDIR)/html
-	./filter_spurious_link_from_html.py
 	@echo
 	@echo "html build finished: $(BUILDDIR)/html."
 
@@ -38,7 +37,6 @@ examples/index.rst:
 htmlzip: html
 	cp sphinx/ext_mathjax.py extensions.py
 	$(SPHINXBUILD) -b html $(SPHINX_OUTPUT) $(SPHINX_OPTS) . $(BUILDDIR)/lmfit_doc
-	./filter_spurious_link_from_html.py
 	cd $(BUILDDIR) && zip -pur html/lmfit_doc.zip lmfit_doc
 
 epub: gallery

doc/model.rst

@@ -598,6 +598,59 @@ of:
 With that definition, the value (and uncertainty) of the ``fwhm`` parameter
 will be reported in the output of any fit done with that model.
 
+.. _model_data_coercion_section:
+
+Data Types for data  and independent data with ``Model``
+-------------------------------------------------------------
+
+The model as defined by your model function will use the independent
+variable(s) you specify to best match the data you provide.  The model is meant
+to be an abstract representation for data, but when you do a fit with
+:meth:`Model.fit`, you really need to pass in values for the data to be modeled
+and the independent data used to calculate that data.
+
+The mathematical solvers used by ``lmfit`` all work exclusively with
+1-dimensional numpy arrays of datatype (dtype) ``float64``.  The value of the
+calculation ``(model-data)*weights`` using the calculation of your model
+function, and the data and weights you pass in *will be coerced* to an
+1-dimensional ndarray with dtype ``float64`` when it is passed to the solver.
+
+If the data you pass to :meth:`Model.fit` is not an ndarray of dtype
+``float64`` but is instead a tuples of numbers, a list of numbers, or a
+``pandas.Series``, it will be coerced into an ndarray.  If your data is a list,
+tuple, or Series of complex numbers, it *will be coerced* to an ndarray with
+dtype ``complex128``.
+
+If your data is a numpy array of dtype ``float32``, it *will not be coerced* to
+``float64``, as we assume this was an intentional choice.  That may make all of
+the calculations done in your model function be in single-precision which may
+make fits less sensitive, but the values will be converted to ``float64``
+before being sent to the solver, so the fit should work.
+
+The independent data for models using ``Model`` are meant to be truly
+independent, and not **not** required to be strictly numerical or objects that
+are easily converted to arrays of numbers.  That is, independent data for a
+model could be a dictionary, an instance of a user-defined class, or other type
+of structured data.  You can use independent data any way you want in your
+model function.
+
+But, as with almost all the examples given here, independent data is often also
+a 1-dimensonal array of values, say ``x``, and a simple view of the fit would be
+to plot the data as ``y`` as a function of ``x``.  Again, this is not required, but
+it is very common.  Because of this very common usage, if your independent data
+is a tuple or list of numbers or ``pandas.Series``, it *will be coerced* to be
+an ndarray of dtype ``float64``.  But as with the primary data, if your
+independent data is an ndarray of some different dtype (``float32``,
+``uint16``, etc), it *will not be coerced* to ``float64``, as we assume this
+was intentional.
+
+.. note::
+
+  Data and independent data that are tuples or lists of numbers, or
+  ``panda.Series`` will be coerced to an ndarray of dtype ``float64`` before
+  passing to the model function.  Data with other dtypes (or independent data
+  of other object types such as dicts) will not be coerced to ``float64``.
+
 
 .. _model_saveload_sec:
 

doc/whatsnew.rst

@@ -13,22 +13,37 @@ consult the `lmfit GitHub repository`_.
 
 .. _whatsnew_120_label:
 
-Version 1.2.0 Release Notes (March XX, 2023)
+Version 1.2.0 Release Notes (April 05, 2023)
 =================================================
 
 New features:
 
 - add ``create_params`` function (PR #844)
+- add ``chi2_out`` and ``nsigma`` options to ``conf_interval2d()``
+- add ``ModelResult.summary()`` to return many resulting fit statistics and attributes into a JSON-able dict.
+- add ``correl_table()`` function to ``lmfit.printfuncs`` and ``correl_mode`` option to ``fit_report()`` and
+  ``ModelResult.fit_report()`` to optionally display a RST-formatted table of a correlation matrix.
 
 Bug fixes/enhancements:
 
-- fix deepcopy of ``Parameters`` (mguhyo; PR #837)
+- fix bug in reported uncertainties for constrained parameters by better propating uncertainties (Issue #855; PR #856)
+- Coercing of user input data and independent data for ``Model`` to float64 ndarrays is somewhat less aggressive and
+  will not increase the precision of numpy ndarrays (see :ref:`model_data_coercion_section` for details). The resulting
+  calculation from a model or objective function is more aggressively coerced to float64.  (Issue #850; PR #853)
+- the default value of ``epsfcn`` is increased to 1.e-10 to allow for handling of data with precision less than float64
+  (Issue #850; PR #853)
+- fix ``conf_interval2d`` to use "increase chi-square by sigma**2*reduced chi-square" to give the ``sigma``-level
+  probabilities (Issue #848; PR #852)
+- fix reading of older ``ModelResult`` (Issue #845; included in PR #844)
+- fix deepcopy of ``Parameters`` and user data (mguhyo; PR #837)
 - improve ``Model.make_params`` and ``create_params`` to take optional dict of Parameter attributes (PR #844)
-- fix reporting of ``nfev`` from ``least_squares`` to better reflect actual number of function calls (PR #844)
+- fix reporting of ``nfev`` from ``least_squares`` to better reflect actual number of function calls (Issue #842; PR #844)
 - fix bug in ``Model.eval`` when mixing parameters and keyword arguments (PR #844, #839)
 - re-adds ``residual`` to saved ``Model`` result (PR #844, #830)
-- ``ConstantModel`` and ``ComplexConstantModel`` will return an ndarray of the same shape as the independent variable ``x`` (JeppeKlitgaard, Issue #840; PR #841)
+- ``ConstantModel`` and ``ComplexConstantModel`` will return an ndarray of the same shape as the independent variable
+  ``x`` (JeppeKlitgaard, Issue #840; PR #841)
 - update tests for latest versions of NumPy and SciPy.
+- many fixes of doc typos and updates of dependencies, pre-commit hooks, and CI.
 
 .. _whatsnew_110_label: