Patch v0.14.1 (#332)

* bumb version to v0.15.0-dev (#307)

* examples: fix identifiablity analysis grid values (#314)

* remove line that should be substituted by the subsequent GHA (#313)

* Use Scipy's `eigh` instead of Numpy's to avoid convergence error (#311)

* Use Scipy's eigh instead of Numpy's to avoid convergence error

* fix error

* Enable the pickling of DeerLab's objects  (#312)

* fix pickling of fit result objects

* add save and load utility functions for pickling, add test

* improve test

* fix pickling of model objects

* fix pickling of merged and linearly combined models

* fix pickling of model objects with linked parameters

* fix some tests

* fix pickling of model objects with functionalized parameters

- introduces the dill package as a dependency

* fix error

* improve docstrings and add to API reference

* rename save to store_pickle and load to load_pickle

add safety warning about reading pickles

* rename imports

* remove unused import

* fix another import error

* `docs`: add important note to user guide on experimental deadtime correction  (#320)

* Fix dependency of DeerLab's documentation logo on system fonts (#328)

* add specific web font of DeerLab's title font

* docs: make web logo independent of system fonts

* docs: minor CSS fixes

* fix minor bug when printing fit results with most parameters frozen (#329)

* Fix bug when propagating bootstrapped uncertainty in presence of round-off errors (#325)

* UQresult: expand join() method to work with bootstrapped results

* fix bug when propagating bootstrapped results with round-off errors

* docs: add docstring for evaluate and propagate methods (#321)

* bump version to v0.14.1

* update changelog

* docs: minor edits, CSS fixes, and one 404 fix (#331)

* docs: minor edits, CSS fixes, and one 404 fix

* add missing functions to reference

* minor edit and fix

* update changelog

.github/workflows/package_upload.yml

@@ -20,10 +20,9 @@ jobs:
           python -m pip install setuptools --user
           python -m pip install build --user
           python -m pip install twine --user
-    - name: PyPI - Build and publish
+    - name: Build distribution
       run: |
           python setup.py sdist
-          python -m twine upload dist/*
     - name: Publish distribution to PyPI
       uses: pypa/gh-action-pypi-publish@master
       with:

VERSION

@@ -1 +1 @@
-v0.14.0
+v0.14.1

deerlab/__init__.py

@@ -19,3 +19,4 @@
 from .classes import FitResult, UQResult
 from .diststats import diststats
 from .profile_analysis import profile_analysis
+from .utils import store_pickle, read_pickle

deerlab/classes.py

@@ -209,25 +209,34 @@ def join(self,*args):
             Joined uncertainty quantification object with a total of ``M + N1 + N2 + ... + Nn`` parameters. 
             The parameter vectors are concatenated on the order they are passed. 
         """
-        # Original metadata
-        mean = self.mean
-        covmat = self.covmat
-        lbm = self.__lb
-        ubm = self.__ub
-
+        newargs = []
+        if self.type=='covariance':
+            # Original metadata
+            newargs.append(self.mean)
+            newargs.append(self.covmat)
+            newargs.append(self.__lb)
+            newargs.append(self.__ub)
+        elif self.type=='bootstrap':
+            newargs.append(self.samples)
+
         for uq in args:
             if not isinstance(uq, UQResult):
                 raise TypeError('Only UQResult objects can be joined.')
+            if uq.type!=self.type:
+                raise TypeError(f'A UQResult of type ({uq.type}) and another of type ({self.type}) cannot be joined.')
             if uq.type=='void':
                 raise TypeError('Void UQResults cannot be joined.')
             # Concatenate metadata of external UQResult objects
-            mean = np.concatenate([mean, uq.mean])
-            covmat = block_diag(covmat, uq.covmat)
-            lbm = np.concatenate([lbm, uq.__lb])
-            ubm = np.concatenate([ubm, uq.__ub])
-
+            if self.type=='covariance':
+                newargs[0] = np.concatenate([newargs[0], uq.mean])
+                newargs[1] = block_diag(newargs[1], uq.covmat)
+                newargs[2] = np.concatenate([newargs[2], uq.__lb])
+                newargs[3] = np.concatenate([newargs[3], uq.__ub])
+            elif self.type=='bootstrap':
+                newargs[0] = np.concatenate([newargs[0],uq.samples],axis=1) 
+
         # Return new UQResult object with combined information    
-        return UQResult('covariance',mean,covmat,lbm,ubm) 
+        return UQResult(self.type,*newargs) 
     #--------------------------------------------------------------------------------
 
 
@@ -264,6 +273,9 @@ def pardist(self,n=0):
             # Get bw using silverman's rule (1D only)
             samplen = self.samples[:, n].real
 
+            # Take limited precision into account to avoid round-off errors
+            samplen = np.round(samplen,200)
+
             if np.all(samplen == samplen[0]):
                 # Dirac's delta distribution 
                 x = np.array([0.9*samplen[0],samplen[0],1.1*samplen[0]])