Minor bugs fix

VERSION

@@ -1 +1 @@
-v1.0.0
+v1.0.2

deerlab/solvers.py

@@ -136,7 +136,6 @@ def _plot(ys,yfits,yuqs,noiselvls,axis=None,xlabel=None,gof=False,fontsize=13):
     # Adjust fontsize
     for ax in axs:
         for label in (ax.get_xticklabels() + ax.get_yticklabels()):
-            label.set_fontname('Calibri')
             label.set_fontsize(fontsize)
 
     return fig

docsrc/source/changelog.rst

@@ -14,6 +14,35 @@ Release Notes
 - |fix| : Something which was not working as expected or leading to errors has been fixed.
 - |api| : This will require changes in your scripts or code.
 
+Release ``v1.0.2`` - July 2023
+------------------------------------------
+- |fix| : Fixes errors in documentation (:pr:`429`).
+
+  * Changes the file name of figures 'modelling*.png` to `modeling*.png`. To keep all spelling consistent with american english.
+  * Adds a missing `)` in `fitting_guide`
+  * Corrects the time axis in `echo_crossing` example.
+
+- |fix| : Fixes an errors in tests (:pr:`429`).
+
+  * The test `test_algorithms` had an incorrect search range.
+
+- |fix| : Removes the default font from the `fit` function due to conflicts on some systems (:pr:`429`).
+
+Release ``v1.0.1`` - March 2023
+------------------------------------------
+- |fix| : Fixes some minor bugs in the documentation. 
+
+  * The file modelling_guide.rst is renamed to modeling_guide.rst to keep spelling consistency. 
+  * The "Simulating a two-pathway 5-pulse DEER signal" and "Simulating a three-pathway 4-pulse DEER signal" examples now run correctly.
+  * 
+- |fix| : Fixes issues with CVXOPT in tests. 
+
+  * The testing will now use quadprog as the default solver. To account for the change tested values are now generated using the grid method
+
+- |fix| : Updates GitHub actions to use latest packages.
+- |api| : Hardcodes out Python 3.11 support. This will remain until quadprog is fixed.
+- |api| : Removes hard-wired RNG seeding
+
 Release ``v1.0.0`` - December 2022
 ------------------------------------------
 

docsrc/source/fitting_guide.rst

@@ -66,7 +66,7 @@ Fitting multi-dataset models
 Models merged using the ``merge`` function (see :ref:`here <modeling_merging>` for details) can describe multiple datasets with a single model object and a common parameter set. To fit such a merged model to multiple datasets, the ``fit`` function can be used as above by passing a list of datasets ``[y1,y2,...,yN]`` instead of a single dataset  ::
 
     # Fit the model to multiple datasets
-    result = dl.fit(model, [y1,y2,y3]
+    result = dl.fit(model, [y1,y2,y3])
 
 The number of datasets must match the number of responses returned by the model. Additionally, the ordering in the list of datasets must match the order of responses from the model, i.e. ``response1`` of ``model`` will be fitted to ``y1``, and so on. 
 

examples/intermediate/ex_crossing_echoes_masking.py

@@ -27,6 +27,8 @@
 # Load the experimental data
 t,Vexp = dl.deerload('../data/example_4pdeer_5.DTA')
 
+t *= 1e3 # convert from ms to us
+
 # Experimental parameters
 tau1 = 0.5  # First inter-pulse time delay, μs
 tau2 = 4.5  # Second inter-pulse time delay, μs

test/test_selregparam.py

@@ -53,8 +53,8 @@ def test_selection_criteria_values(dataset, design_matrix, regularization_matrix
 #=======================================================================
 def test_algorithms(dataset, design_matrix, regularization_matrix):
     "Check that the value returned by the the grid and Brent algorithms coincide"
-    alpha_grid = selregparam(dataset,design_matrix,qpnnls,method='aic',algorithm='grid',regop=regularization_matrix)
-    alpha_brent = selregparam(dataset,design_matrix,qpnnls,method='aic',algorithm='brent',regop=regularization_matrix)
+    alpha_grid = selregparam(dataset,design_matrix,qpnnls,method='aic',algorithm='grid',regop=regularization_matrix, searchrange=[1e-10,1e-6])
+    alpha_brent = selregparam(dataset,design_matrix,qpnnls,method='aic',algorithm='brent',regop=regularization_matrix, searchrange=[1e-10,1e-6])
 
     assert abs(1-alpha_grid/alpha_brent) < 0.2
 #=======================================================================