Release Notes¶
This section discusses changes between versions, especially changes significant to the use and behavior of the library. This is not meant to be a comprehensive list of changes. For such a complete record, consult the lmfit GitHub repository.
Version 1.0.0 Release Notes¶
Version 1.0.0 supports Python 3.5, 3.6, 3.7, and 3.8
New features:
no new features are introduced in 1.0.0.
Improvements:
support for Python 2 and use of the
six
package are removed. (PR #612)
Various:
documentation updates to clarify the use of
emcee
. (PR #614)
Version 0.9.15 Release Notes¶
Version 0.9.15 is the last release that supports Python 2.7; it now also fully suports Python 3.8.
New features, improvements, and bug fixes:
move application of parameter bounds to setter instead of getter (PR #587)
add support for non-array Jacobian types in least_squares (Issue #588, @ezwelty in PR #589)
add more information (i.e., acor and acceptance_fraction) about emcee fit (@j-zimmermann in PR #593)
“name” is now a required positional argument for Parameter class, update the magic methods (PR #595)
fix nvars count and bound handling in confidence interval calculations (Issue #597, PR #598)
support Python 3.8; requires asteval >= 0.9.16 (PR #599)
only support emcee version 3 (i.e., no PTSampler anymore) (PR #600)
fix and refactor prob_bunc in confidence interval calculations (PR #604)
fix adding Parameters with custom user-defined symbols (Issue #607, PR #608; thanks to @gbouvignies for the report)
Various:
bump requirements to LTS version of SciPy/ NumPy and code clean-up (PR #591)
documentation updates (PR #596, and others)
improve test coverage and Travis CI updates (PR #595, and others)
update pre-commit hooks and configuration in setup.cfg
To-be deprecated: - function Parameter.isParameter and conversion from uncertainties.core.Variable to value in _getval (PR #595)
Version 0.9.14 Release Notes¶
New features:
the global optimizers
shgo
anddual_annealing
(new in SciPy v1.2) are now supported (Issue #527; PRs #545 and #556)eval
method added to the Parameter class (PR #550 by @zobristnicholas)avoid ZeroDivisionError in
printfuncs.params_html_table
(PR #552 by @aaristov and PR #559)add parallelization to
brute
method (PR #564, requires SciPy v1.3)
Bug fixes:
consider only varying parameters when reporting potential issues with calculating errorbars (PR #549) and compare
value
to bothmin
andmax
(PR #571)guard against division by zero in lineshape functions and
FWHM
andheight
expression calculations (PR #545)fix issues with restoring a saved Model (Issue #553; PR #554)
always set
result.method
foremcee
algorithm (PR #558)more careful adding of parameters to handle out-of-order constraint expressions (Issue #560; PR #561)
make sure all parameters in Model.guess() use prefixes (PRs #567 and #569)
use
inspect.signature
for PY3 to support wrapped functions (Issue #570; PR #576)fix
result.nfev`
forbrute
method when using parallelization (Issue #578; PR #579)
Various:
remove “missing” in the Model class (replaced by nan_policy) and “drop” as option to nan_policy (replaced by omit) deprecated since 0.9 (PR #565).
deprecate ‘report_errors’ in printfuncs.py (PR #571)
updates to the documentation to use
jupyter-sphinx
to include examples/output (PRs #573 and #575)include a Gallery with examples in the documentation using
sphinx-gallery
(PR #574 and #583)improve test-coverage (PRs #571, #572 and #585)
add/clarify warning messages when NaN values are detected (PR #586)
several updates to docstrings (Issue #584; PR #583, and others)
update pre-commit hooks and several docstrings
Version 0.9.13 Release Notes¶
New features:
Clearer warning message in fit reports when uncertainties should but cannot be estimated, including guesses of which Parameters to examine (#521, #543)
SplitLorenztianModel and split_lorentzian function (#523)
HTML representations for Parameter, MinimizerResult, and Model so that they can be printed better with Jupyter (#524, #548)
support parallelization for differential evolution (#526)
Bug fixes:
delay import of matplotlib (and so, the selection of its backend) as late as possible (#528, #529)
fix for saving, loading, and reloading ModelResults (#534)
fix to leastsq to report the best-fit values, not the values tried last (#535, #536)
fix synchronization of all parameter values on Model.guess() (#539, #542)
improve deprecation warnings for outdated nan_policy keywords (#540)
fix for edge case in gformat() (#547)
Project management:
using pre-commit framework to improve and enforce coding style (#533)
added code coverage report to github main page
updated docs, github templates, added several tests.
dropped support and testing for Python 3.4.
Version 0.9.12 Release Notes¶
Lmfit package is now licensed under BSD-3.
New features:
SkewedVoigtModel was added as built-in model (Issue #493)
Parameter uncertainties and correlations are reported for least_squares
Plotting of complex-valued models is now handled in ModelResult class (PR #503)
A model’s independent variable is allowed to be an object (Issue #492)
Added
usersyms
to Parameters() initialization to make it easier to add custom functions and symbols (Issue #507)the
numdifftools
package can be used to calculate parameter uncertainties and correlations for all solvers that do not natively support this (PR #506)emcee
can now be used as method keyword-argument to Minimizer.minimize and minimize function, which allows for usingemcee
in the Model class (PR #512; seeexamples/example_emcee_with_Model.py
)
(Bug)fixes:
asteval errors are now flushed after raising (Issue #486)
max_time and evaluation time for ExpressionModel increased to 1 hour (Issue #489)
loading a saved ModelResult now restores all attributes (Issue #491)
development versions of scipy and emcee are now supported (Issue #497 and PR #496)
ModelResult.eval() do no longer overwrite the userkws dictionary (Issue #499)
running the test suite requires
pytest
only (Issue #504)improved FWHM calculation for VoigtModel (PR #514)
Version 0.9.10 Release Notes¶
Two new global algorithms were added: basinhopping and AMPGO.
Basinhopping wraps the method present in scipy
, and more information
can be found in the documentation (basinhopping()
and scipy.optimize.basinhopping).
The Adaptive Memory Programming for Global Optimization (AMPGO) algorithm
was adapted from Python code written by Andrea Gavana. A more detailed
explanation of the algorithm is available in the AMPGO paper and specifics
for lmfit can be found in the ampgo()
function.
Lmfit uses the external uncertainties (https://github.com/lebigot/uncertainties) package (available on PyPI), instead of distributing its own fork.
An AbortFitException
is now raised when the fit is aborted by the user (i.e., by
using iter_cb
).
Bugfixes:
all exceptions are allowed when trying to import matplotlib
simplify and fix corner-case errors when testing closeness of large integers
Version 0.9.9 Release Notes¶
Lmfit now uses the asteval (https://github.com/newville/asteval) package instead of distributing its own copy. The minimum required asteval version is 0.9.12, which is available on PyPI. If you see import errors related to asteval, please make sure that you actually have the latest version installed.
Version 0.9.6 Release Notes¶
Support for SciPy 0.14 has been dropped: SciPy 0.15 is now required. This is especially important for lmfit maintenance, as it means we can now rely on SciPy having code for differential evolution and do not need to keep a local copy.
A brute force method was added, which can be used either with
Minimizer.brute()
or using the method='brute'
option to
Minimizer.minimize()
. This method requires finite bounds on
all varying parameters, or that parameters have a finite
brute_step
attribute set to specify the step size.
Custom cost functions can now be used for the scalar minimizers using the
reduce_fcn
option.
Many improvements to documentation and docstrings in the code were made. As part of that effort, all API documentation in this main Sphinx documentation now derives from the docstrings.
Uncertainties in the resulting best-fit for a model can now be calculated from the uncertainties in the model parameters.
Parameters have two new attributes: brute_step
, to specify the step
size when using the brute
method, and user_data
, which is unused but
can be used to hold additional information the user may desire. This will
be preserved on copy and pickling.
Several bug fixes and cleanups.
Versioneer was updated to 0.18.
Tests can now be run either with nose or pytest.
Version 0.9.5 Release Notes¶
Support for Python 2.6 and SciPy 0.13 has been dropped.
Version 0.9.4 Release Notes¶
Some support for the new least_squares
routine from SciPy 0.17 has been
added.
Parameters can now be used directly in floating point or array expressions,
so that the Parameter value does not need sigma = params['sigma'].value
.
The older, explicit usage still works, but the docs, samples, and tests
have been updated to use the simpler usage.
Support for Python 2.6 and SciPy 0.13 is now explicitly deprecated and wil be dropped in version 0.9.5.
Version 0.9.3 Release Notes¶
Models involving complex numbers have been improved.
The emcee
module can now be used for uncertainty estimation.
Many bug fixes, and an important fix for performance slowdown on getting parameter values.
ASV benchmarking code added.
Version 0.9.0 Release Notes¶
This upgrade makes an important, non-backward-compatible change to the way many fitting scripts and programs will work. Scripts that work with version 0.8.3 will not work with version 0.9.0 and vice versa. The change was not made lightly or without ample discussion, and is really an improvement. Modifying scripts that did work with 0.8.3 to work with 0.9.0 is easy, but needs to be done.
Summary¶
The upgrade from 0.8.3 to 0.9.0 introduced the MinimizerResult
class (see MinimizerResult – the optimization result) which is now used to hold the return
value from minimize()
and Minimizer.minimize()
. This returned
object contains many goodness of fit statistics, and holds the optimized
parameters from the fit. Importantly, the parameters passed into
minimize()
and Minimizer.minimize()
are no longer modified by
the fit. Instead, a copy of the passed-in parameters is made which is
changed and returns as the params
attribute of the returned
MinimizerResult
.
Impact¶
This upgrade means that a script that does:
my_pars = Parameters()
my_pars.add('amp', value=300.0, min=0)
my_pars.add('center', value= 5.0, min=0, max=10)
my_pars.add('decay', value= 1.0, vary=False)
result = minimize(objfunc, my_pars)
will still work, but that my_pars
will NOT be changed by the fit.
Instead, my_pars
is copied to an internal set of parameters that is
changed in the fit, and this copy is then put in result.params
. To
look at fit results, use result.params
, not my_pars
.
This has the effect that my_pars
will still hold the starting parameter
values, while all of the results from the fit are held in the result
object returned by minimize()
.
If you want to do an initial fit, then refine that fit to, for example, do a pre-fit, then refine that result different fitting method, such as:
result1 = minimize(objfunc, my_pars, method='nelder')
result1.params['decay'].vary = True
result2 = minimize(objfunc, result1.params, method='leastsq')
and have access to all of the starting parameters my_pars
, the result of the
first fit result1
, and the result of the final fit result2
.
Discussion¶
The main goal for making this change were to
give a better return value to
minimize()
andMinimizer.minimize()
that can hold all of the information about a fit. By having the return value be an instance of theMinimizerResult
class, it can hold an arbitrary amount of information that is easily accessed by attribute name, and even be given methods. Using objects is good!To limit or even eliminate the amount of “state information” a
Minimizer
holds. By state information, we mean how much of the previous fit is remembered after a fit is done. Keeping (and especially using) such information about a previous fit means that aMinimizer
might give different results even for the same problem if run a second time. While it’s desirable to be able to adjust a set ofParameters
re-run a fit to get an improved result, doing this by changing an internal attribute (Minimizer.params
) has the undesirable side-effect of not being able to “go back”, and makes it somewhat cumbersome to keep track of changes made while adjusting parameters and re-running fits.