Merge pull request #640 from reneeotten/prepare_for_release

prepare for release

Author: newville

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ exclude: 'versioneer.py|lmfit/_version|doc/conf.py'
 
 repos:
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v2.1.0
+    rev: v2.3.0
     hooks:
     -   id: pyupgrade
         # for now don't force to change from %-operator to {}

INSTALL

@@ -6,12 +6,12 @@ To install the lmfit Python module, use::
    python setup.py build
    python setup.py install
 
-For lmfit 1.0, the following versions are required:
+For lmfit 1.0.1, the following versions are required:
   Python: 3.5 or higher
   NumPy: 1.16 or higher
   SciPy: 1.2 or higher
   asteval: 0.9.16 or higher
   uncertainties: 3.0.1 or higher
 
 Matt Newville <[email protected]>
-Last Update: 2019-December-4
+Last Update: 2020-April-29

THANKS.txt

@@ -52,7 +52,7 @@ Additional patches, bug fixes, and suggestions have come from Faustin
 Carter, Christoph Deil, Francois Boulogne, Thomas Caswell, Colin Brosseau,
 nmearl, Gustavo Pasquevich, Clemens Prescher, LiCode, Ben Gamari, Yoav
 Roam, Alexander Stark, Alexandre Beelen, Andrey Aristov, Nicholas Zobrist,
-Ethan Welty, Julius Zimmermann, and many others.
+Ethan Welty, Julius Zimmermann, Mark Dean, Arun Persaud, and many others.
 
 The lmfit code obviously depends on, and owes a very large debt to the code
 in scipy.optimize.  Several discussions on the SciPy-user and lmfit mailing

doc/whatsnew.rst

@@ -12,6 +12,40 @@ to be a comprehensive list of changes.  For such a complete record,
 consult the `lmfit GitHub repository`_.
 
 
+.. _whatsnew_101_label:
+
+Version 1.0.1 Release Notes
+============================
+
+**Version 1.0.1 is the last release that supports Python 3.5**. All newer version will
+require 3.6+ so that we can use formatting-strings and rely on dictionaries being ordered.
+
+New features:
+
+- added thermal distribution model and lineshape (PR #620; @mpmdean)
+- introduced a new argument ``max_nfev`` to uniformly specify the maximum number of function evalutions (PR #610)
+  **Please note: all other arguments (e.g., ``maxfev``, ``maxiter``, ...) will no longer be passed to the underlying
+  solver. A warning will be emitted stating that one should use ``max_nfev``.**
+- the attribute ``call_kws`` was added to the ``MinimizerResult`` class and contains the keyword arguments that are
+  supplied to the solver in SciPy.
+
+Bug fixes:
+
+- fixes to the ``load`` and ``__setstate__`` methods of the Parameter class
+- fixed failure of ModelResult.dump() due to missing attributes (Issue #611, PR #623; @mpmdean)
+- ``guess_from_peak`` function now also works correctly with decreasing x-values or when using
+  pandas (PRs #627 and #629; @mpmdean)
+- the ``Parameter.set()`` method now correctly first updates the boundaries and then the value (Issue #636, PR #637; @arunpersaud)
+
+Various:
+
+- fixed typo for the use of expressions in the documentation (Issue #610; @jkrogager)
+- removal of PY2-compatibility and unused code and improved test coverage (PRs #619, #631, and #633)
+- removed deprecated ``isParameter`` function and automatic conversion of an ``uncertainties`` object (PR #626)
+- inaccurate FWHM calculations were removed from built-in models, others labeled as estimates (Issue #616 and PR #630)
+- corrected spelling mistake for the Doniach lineshape and model (Issue #634; @rayosborn)
+
+
 .. _whatsnew_100_label:
 
 Version 1.0.0 Release Notes

lmfit/minimizer.py

@@ -14,10 +14,10 @@
 """
 from collections import namedtuple
 from copy import deepcopy
+import inspect
 import multiprocessing
 import numbers
 import warnings
-import inspect
 
 import numpy as np
 from numpy import ndarray, ones_like, sqrt
@@ -77,11 +77,11 @@
 # define the namedtuple here so pickle will work with the MinimizerResult
 Candidate = namedtuple('Candidate', ['params', 'score'])
 
-MAXEVAL_Warning = """ignoring `%s` argument to `%s()`. Use `max_nfev` instead."""
+MAXEVAL_Warning = "ignoring `%s` argument to `%s()`. Use `max_nfev` instead."
 
 
 def thisfuncname():
-    """Return name of calling function"""
+    """Return the name of calling function."""
     try:
         return inspect.stack()[1].function
     except AttributeError:
@@ -268,7 +268,7 @@ class MinimizerResult:
     message : str
         Message about fit success.
     call_kws : dict
-        Dict of keyword arguments sent to underlying solver method.
+        Keyword arguments sent to underlying solver.
     ier : int
         Integer error value from :scipydoc:`optimize.leastsq` (`leastsq` only).
     lmdif_message : str
@@ -380,9 +380,9 @@ def _repr_html_(self, show_correl=True, min_correl=0.1):
 class Minimizer:
     """A general minimizer for curve fitting and optimization."""
 
-    _err_nonparam = ("params must be a minimizer.Parameters() instance or list "
-                     "of Parameters()")
-    _err_max_evals = ("Too many function calls (max set to %i)!  Use:"
+    _err_nonparam = ("params must be a minimizer.Parameters() instance or"
+                     " list of Parameters()")
+    _err_max_evals = ("Too many function calls (max set to %i)! Use:"
                       " minimize(func, params, ..., max_nfev=NNN)"
                       " to increase this maximum.")
 
@@ -405,9 +405,9 @@ def __init__(self, userfcn, params, fcn_args=None, fcn_kws=None,
             Positional arguments to pass to `userfcn`.
         fcn_kws : dict, optional
             Keyword arguments to pass to `userfcn`.
-        max_nfev: int or None
-           Maximum number of function evaluations. The default value will
-           change with fitting method.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations (default is None). The
+            default value depends on the fitting method.
         iter_cb : callable, optional
             Function to be called at each fit iteration. This function should
             have the signature::
@@ -509,11 +509,12 @@ def __init__(self, userfcn, params, fcn_args=None, fcn_kws=None,
         self.nan_policy = nan_policy
 
     def set_max_nfev(self, max_nfev=None, default_value=100000):
-        """
-        Set maximum number of function evaluations, possibly setting
-        to the provided default_value
+        """Set maximum number of function evaluations.
+
+        If `max_nfev` is None, use the provided `default_value`.
 
         >>> self.set_max_nfev(max_nfev, 1000*(result.nvarys+1))
+
         """
         if max_nfev is not None:
             self.max_nfev = max_nfev
@@ -889,10 +890,10 @@ def scalar_minimize(self, method='Nelder-Mead', params=None, max_nfev=None,
             - 'differential_evolution'
 
         params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        max_nfev: int or None
-           Maximum number of function evaluations.  Defaults to 1000*(nvars+1)
-           where nvars is the number of variable parameters.
+            Parameters to use as starting point.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations. Defaults to 1000*(nvars+1),
+            where nvars is the number of variable parameters.
         **kws : dict, optional
             Minimizer options pass to :scipydoc:`optimize.minimize`.
 
@@ -904,7 +905,7 @@ def scalar_minimize(self, method='Nelder-Mead', params=None, max_nfev=None,
 
 
         .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
+            Return value changed to :class:`MinimizerResult`.
 
         Notes
         -----
@@ -1490,10 +1491,10 @@ def least_squares(self, params=None, max_nfev=None, **kws):
         Parameters
         ----------
         params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        max_nfev: int or None
-           Maximum number of function evaluations.  Defaults to 1000*(nvars+1)
-           where nvars is the number of variable parameters.
+            Parameters to use as starting point.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations. Defaults to 1000*(nvars+1),
+            where nvars is the number of variable parameters.
         **kws : dict, optional
             Minimizer options to pass to :scipydoc:`optimize.least_squares`.
 
@@ -1505,7 +1506,7 @@ def least_squares(self, params=None, max_nfev=None, **kws):
 
 
         .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
+            Return value changed to :class:`MinimizerResult`.
 
         """
         result = self.prepare_fit(params)
@@ -1595,10 +1596,10 @@ def leastsq(self, params=None, max_nfev=None, **kws):
         Parameters
         ----------
         params : :class:`~lmfit.parameter.Parameters`, optional
-           Parameters to use as starting point.
-        max_nfev: int or None
-           Maximum number of function evaluations.  Defaults to 2000*(nvars+1)
-           where nvars is the number of variable parameters.
+            Parameters to use as starting point.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations. Defaults to 2000*(nvars+1),
+            where nvars is the number of variable parameters.
         **kws : dict, optional
             Minimizer options to pass to :scipydoc:`optimize.leastsq`.
 
@@ -1610,7 +1611,7 @@ def leastsq(self, params=None, max_nfev=None, **kws):
 
 
         .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
+            Return value changed to :class:`MinimizerResult`.
 
         """
         result = self.prepare_fit(params=params)
@@ -1986,7 +1987,7 @@ def ampgo(self, params=None, max_nfev=None, **kws):
 
 
         Notes
-        ----
+        -----
         The Python implementation was written by Andrea Gavana in 2014
         (http://infinity77.net/global_optimization/index.html).
 
@@ -2051,8 +2052,10 @@ def shgo(self, params=None, max_nfev=None, **kws):
         params : :class:`~lmfit.parameter.Parameters`, optional
             Contains the Parameters for the model. If None, then the
             Parameters used to initialize the Minimizer object are used.
-        max_nfev: int (default is None)
-            Maximum number of total function evaluations.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations. Defaults to
+            1e6*(result.nvarys+1), where nvars is the number of variable
+            parameters.
         **kws : dict, optional
             Minimizer options to pass to the SHGO algorithm.
 
@@ -2121,8 +2124,8 @@ def dual_annealing(self, params=None, max_nfev=None, **kws):
         params : :class:`~lmfit.parameter.Parameters`, optional
             Contains the Parameters for the model. If None, then the
             Parameters used to initialize the Minimizer object are used.
-        max_nfev: int or None
-           Maximum number of function evaluations. Default is 1e7.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations. Defaults to 1e7.
         **kws : dict, optional
             Minimizer options to pass to the dual_annealing algorithm.
 
@@ -2242,7 +2245,7 @@ def minimize(self, method='leastsq', params=None, **kws):
 
 
         .. versionchanged:: 0.9.0
-           Return value changed to :class:`MinimizerResult`.
+            Return value changed to :class:`MinimizerResult`.
 
         """
         function = self.leastsq
@@ -2449,9 +2452,9 @@ def minimize(fcn, params, method='leastsq', args=None, kws=None, iter_cb=None,
         Whether to calculate the covariance matrix (default is True) for
         solvers other than `leastsq` and `least_squares`. Requires the
         `numdifftools` package to be installed.
-    max_nfev: int or None
-        Maximum number of function evaluations. Default is different for each
-        fitting method.
+    max_nfev: int or None, optional
+        Maximum number of function evaluations (default is None). The
+        default value depends on the fitting method.
     **fit_kws : dict, optional
         Options to pass to the minimizer being used.
 

lmfit/model.py

@@ -894,8 +894,9 @@ def fit(self, data, params=None, weights=None, method='leastsq',
             Whether to calculate the covariance matrix (default is True) for
             solvers other than `leastsq` and `least_squares`. Requires the
             `numdifftools` package to be installed.
-        max_nfev: int or None
-            Maximum number of function evaluations.  Default varies with solver.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations (default is None). The
+            default value depends on the fitting method.
         **kwargs: optional
             Arguments to pass to the  model function, possibly overriding
             params.
@@ -1307,8 +1308,9 @@ def __init__(self, model, params, data=None, weights=None,
             Whether to calculate the covariance matrix (default is True) for
             solvers other than `leastsq` and `least_squares`. Requires the
             `numdifftools` package to be installed.
-        max_nfev: int or None
-            Maximum number of function evaluations.  Default varies with solver.
+        max_nfev: int or None, optional
+            Maximum number of function evaluations (default is None). The
+            default value depends on the fitting method.
         **fit_kws : optional
             Keyword arguments to send to minimization routine.
 

tests/test_itercb.py

@@ -1,4 +1,5 @@
 """Tests for the Iteration Callback Function."""
+
 import numpy as np
 import pytest
 
@@ -47,14 +48,3 @@ def test_itercb(method, calc_covar):
     assert out.aborted
     assert not out.errorbars
     assert not out.success
-
-@pytest.mark.parametrize("method", ['leastsq', 'least_squares', 'nelder',
-                                    'brute', 'ampgo', 'basinopping', 'differential_evolution'])
-def test_max_nfev(method):
-    """Test the iteration callback for all solvers."""
-    out = mod.fit(y, pars, x=x, method=method, max_nfev=10)
-
-    assert out.nfev < 15
-    assert out.aborted
-    assert not out.errorbars
-    assert not out.success