Merge pull request #546 from lmfit/fix_modelresult_loads

add MinimizerResult when loading ModelResult

.travis.yml

@@ -32,6 +32,7 @@ install:
     - if [[ $version == latest && $TRAVIS_PYTHON_VERSION == 3.7-dev ]]; then conda create -q -n test_env python=3.7 numpy scipy six pandas matplotlib dill sphinx; fi
     - source activate test_env
     - pip install pytest
+    - pip install codecov
     - pip install asteval
     - pip install uncertainties
     - if [[ $version == latest ]]; then pip install emcee numdifftools; fi
@@ -41,4 +42,10 @@ install:
 script:
     - cd tests
     - pytest
-    - if [[ $version == latest && $TRAVIS_PYTHON_VERSION == 3.7-dev ]]; then cd ../doc ; make ; fi  # test building the documentation
+    - coverage run --source=lmfit -m pytest
+    - coverage report -m
+    # test building the documentation
+    - if [[ $version == latest && $TRAVIS_PYTHON_VERSION == 3.7-dev ]]; then cd ../doc ; make ; fi
+
+after_success:
+    - codecov

README.rst

@@ -4,6 +4,9 @@ LMfit-py
 .. image:: https://travis-ci.org/lmfit/lmfit-py.svg
    :target: https://travis-ci.org/lmfit/lmfit-py
 
+.. image:: https://codecov.io/gh/lmfit/lmfit-py/branch/master/graph/badge.svg
+  :target: https://codecov.io/gh/lmfit/lmfit-py
+
 .. image:: 	https://img.shields.io/pypi/v/lmfit.svg
    :target: https://pypi.org/project/lmfit
 

lmfit/model.py

@@ -14,7 +14,7 @@
 from . import Minimizer, Parameter, Parameters, lineshapes
 from .confidence import conf_interval
 from .jsonutils import HAS_DILL, decode4js, encode4js
-from .minimizer import validate_nan_policy
+from .minimizer import validate_nan_policy, MinimizerResult
 from .printfuncs import ci_report, fit_report
 
 # Use pandas.isnull for aligning missing data if pandas is available.
@@ -1259,9 +1259,6 @@ def load_modelresult(fname, funcdefs=None):
     modres = ModelResult(Model(lambda x: x, None), params)
     with open(fname) as fh:
         mresult = modres.load(fh, funcdefs=funcdefs)
-        mresult.data = mresult.userargs[0]
-        mresult.weights = mresult.userargs[1]
-        mresult.init_params = mresult.model.make_params(**mresult.init_values)
     return mresult
 
 
@@ -1682,6 +1679,13 @@ def loads(self, s, funcdefs=None, **kws):
             setattr(self, attr, decode4js(modres.get(attr, None)))
 
         self.best_fit = self.model.eval(self.params, **self.userkws)
+        if len(self.userargs) == 2:
+            self.data = self.userargs[0]
+            self.weights = self.userargs[1]
+        self.init_params = self.model.make_params(**self.init_values)
+        self.result = MinimizerResult()
+        self.result.params = self.params
+        self.init_vals = list(self.init_values.items())
         return self
 
     def load(self, fp, funcdefs=None, **kws):

lmfit/printfuncs.py

@@ -75,7 +75,7 @@ def gformat(val, length=11):
         if expon > 0:
             prec -= expon
     fmt = '{0: %i.%i%s}' % (length, prec, form)
-    return fmt.format(val)
+    return fmt.format(val)[:length]
 
 
 CORREL_HEAD = '[[Correlations]] (unreported correlations are < %.3f)'

tests/test_saveload.py

@@ -7,8 +7,9 @@
 import pytest
 
 import lmfit.jsonutils
+from lmfit import Parameters
 from lmfit.model import (load_model, load_modelresult, save_model,
-                         save_modelresult)
+                         save_modelresult, Model, ModelResult)
 from lmfit.models import ExponentialModel, GaussianModel
 from lmfit_testutils import assert_between, assert_param_between
 
@@ -181,3 +182,29 @@ def test_saveload_modelresult_exception():
     with pytest.raises(AttributeError, match=r'needs saved ModelResult'):
         load_modelresult(SAVE_MODEL)
     clear_savefile(SAVE_MODEL)
+
+
+def test_saveload_modelresult_roundtrip():
+    """Test for modelresult.loads()/dumps() and repeating that"""
+    def mfunc(x, a, b):
+        return a * (x-b)
+
+    model = Model(mfunc)
+    params = model.make_params(a=0.0, b=3.0)
+
+    xx = np.linspace(-5, 5, 201)
+    yy = 0.5 * (xx - 0.22) + np.random.normal(scale=0.01, size=len(xx))
+
+    result1 = model.fit(yy, params, x=xx)
+
+    result2 = ModelResult(model, Parameters())
+    result2.loads(result1.dumps(), funcdefs={'mfunc': mfunc})
+
+    result3 = ModelResult(model, Parameters())
+    result3.loads(result2.dumps(), funcdefs={'mfunc': mfunc})
+
+    assert result3 is not None
+    assert_param_between(result2.params['a'], 0.48, 0.52)
+    assert_param_between(result2.params['b'], 0.20, 0.25)
+    assert_param_between(result3.params['a'], 0.48, 0.52)
+    assert_param_between(result3.params['b'], 0.20, 0.25)