Push patch into V1.1 (JeschkeLab#467)

* Increase version number and update changelog (JeschkeLab#455)

* Bugfixes 4th sep (JeschkeLab#460)

* Fix normalisation in rice model

Closes JeschkeLab#459

* Remove three spin anaysis

Closes JeschkeLab#427

* Improved installation instructions

* Caution about difference in definition of cost function

Closes JeschkeLab#450

* Bump Version

* Bug for non linearly constrained problems

When a problem is not linearly constrained and not non-negative, the linear solver outputs a result class not the solution.

* Keeping changelog up-to date

* Fixing Sophgrid bug (JeschkeLab#464)

* Fixing Sophgrid bug

* Add unit test for sophgrid

* Update changelog.rst for new release (JeschkeLab#466)

VERSION

@@ -1 +1 @@
-v1.1.0
+v1.1.2

deerlab/dd_models.py

@@ -125,7 +125,7 @@ def _multirice3dfun(r,nu,sig):
     r = r.T
     s2 = sig**2
     I_scaled = spc.ive(n/2-1, nu*r/s2)
-    P = nu**(n/2-1)/s2*r**(n/2)*np.exp(-(r**2+nu**2)/(2*s2)+nu*r/s2)*I_scaled
+    P = nu**(1-n/2)/s2*r**(n/2)*np.exp(-(r**2+nu**2)/(2*s2)+nu*r/s2)*I_scaled
     P[P<0] = 0
 
     # Normalization

deerlab/solvers.py

@@ -426,6 +426,11 @@ def snlls(y, Amodel, par0=None, lb=None, ub=None, lbl=None, ubl=None, nnlsSolver
             * ``0`` : Work silently (default).
             * ``1`` : Display progress including the non-linear least-squares' solver termination report.
             * ``2`` : Display progress including the non-linear least-squares' solver iteration details.
+        
+        .. caution::
+
+            The verbose output from the non-linear least-squares solver uses a different definition of the cost function than DeerLab.
+            DeerLab uses the sum of squares of the residuals divided by the number of data points, whereas the non-linear least-squares solver uses the sum of squares of the residuals divided by 2.
 
     Returns
     -------
@@ -594,7 +599,8 @@ def linear_problem(y,A,optimize_alpha,alpha):
 
 
         if optimize_alpha:
-            output = dl.selregparam((y-yfrozen)[mask], Ared[mask,:], linSolver, regparam, 
+            linsolver_result = lambda AtA, Aty: parseResult(linSolver(AtA, Aty))
+            output = dl.selregparam((y-yfrozen)[mask], Ared[mask,:], linsolver_result, regparam, 
                                         weights=weights[mask], regop=L, candidates=regparamrange, 
                                         noiselvl=noiselvl,searchrange=regparamrange,full_output=True)
             alpha = output[0]

deerlab/utils.py

@@ -833,15 +833,15 @@ def sophegrid(octants,maxphi,size):
         weights = np.zeros(nOrientations)
 
         sindth2 = np.sin(dtheta/2)
-        w1 = 0.5
+        w1 = 1.0
 
         # North pole (z orientation)
         phi[0] = 0
         theta[0] = 0
         weights[0] = maxphi*(1 - np.cos(dtheta/2))
 
         # All but equatorial slice
-        Start = 2
+        Start = 1
         for iSlice in np.arange(2,size):
             nPhi = nOct*(iSlice-1) + 1
             dPhi = maxphi/(nPhi - 1)
@@ -854,13 +854,13 @@ def sophegrid(octants,maxphi,size):
         # Equatorial slice
         nPhi = nOct*(size - 1) + 1
         dPhi = maxphi/(nPhi - 1)
-        idx = Start + (np.arange(0,nPhi) - 1)
+        idx = Start + np.arange(0,nPhi)
         phi[idx] = np.linspace(0,maxphi,nPhi)
         theta[idx] = np.pi/2
         weights[idx] = sindth2*dPhi*np.concatenate([[w1], np.ones(nPhi-2), [0.5]])
 
         # Border removal
-        rmv = np.cumsum(nOct*np.arange(1,size-1)+1)+1 
+        rmv = np.cumsum(nOct*np.arange(1,size)+1)
         phi = np.delete(phi,rmv)
         theta = np.delete(theta,rmv)
         weights = np.delete(weights,rmv)

docsrc/source/changelog.rst

@@ -24,6 +24,18 @@ 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 ``v1.1.2`` - November 2023
+------------------------------------------
+- |fix| : Fixes an issue with sophgrid (:pr:`463`).
+- |fix| : Fixes an error in the normalisation of the rice models (:pr:`459`).
+- |fix| : Removes the broken three spin example (:pr:`427`).
+- |fix| : Fixes an error in the linear solver for linearly constrained not non-negative problems.
+
+Release ``v1.1.1`` - August 2023
+------------------------------------------
+- |fix| : Fixes an error in the `FitResult.evaluate` function. (:pr:`454`)
+
+
 Release ``v1.1.0`` - August 2023
 ------------------------------------------
 - |api| : The definition of the dipolar time axis for RIDME has changed to match the one for 4-pulse DEER (:pr:`436`).

docsrc/source/installation.rst

@@ -45,7 +45,7 @@ DeerLab installs the following packages:
 * `joblib <https://joblib.readthedocs.io/en/latest/>`_ - Library lightweight pipelining and parallelization.
 * `tqdm <https://github.com/tqdm/tqdm>`_ - A lightweight package for smart progress meters.
 * `dill <https://github.com/uqfoundation/dill>`_ - An extension of Python's pickle module for serializing and de-serializing python objects.
-* `quadprog <https://pypi.org/project/quadprog/>`_ - A quadratic programming solver
+* `quadprog <https://pypi.org/project/quadprog/>`_ - A quadratic programming solver (Only for Python versions < 3.11)
 
 Importing DeerLab
 ------------------
@@ -74,9 +74,6 @@ Any DeerLab version released after v0.10.0 can be installed via ``pip`` using th
 
 		python -m pip install deerlab==x.y.z
         
-DeerLab version prior to 0.10 are written in MATLAB and are still available from an `archived repository <https://github.com/JeschkeLab/DeerLab-Matlab>`_. 
-Download and installation instruction for the MATLAB environment are provided in the released documentation. MATLAB releases have been deprecated and no further support is provided.
-
 
 Installing from source
 *****************************
@@ -130,10 +127,18 @@ On a **Windows** computer, if you are trying to run a DeerLab function, you migh
 
     ImportError: DLL load failed: The specified module could not be found.
 
-This happens when the MKL libraries have not been properly linked in ``numpy``, ``scipy`` or ``cvxopt`` 
-installations (typically ``numpy``). This can happen due to firewall restrictions,
-user rights, or internet connection issues during the DeerLab installation. To solve this, the
-best solution is to manually install as follows. 
+This issue can occur when a specific python package is not installled properly. This typicaly happens when installing the MKL linked libaries but can 
+occur when installing packages normaly from PyPI. This can happen due to firewall restrictions,
+user rights, or internet connection issues during the DeerLab installation.
+
+If your where installing packages from PyPI (i.e. via pip) then please uninstall the package and reinstall it.
+
+.. code-block:: text
+
+    python -m pip uninstall <package_name>
+    python -m pip install <package_name>
+
+If you were trying to install the MKL linked libraries then please follow the instructions below.
 
 1) Go to https://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
 
@@ -167,7 +172,7 @@ During installation on certain systems (e.g. some computation clusters) using on
 the following error might be raised during the installation:
 
 .. code-block:: text
-
+    
     Error while finding module specification for 'setup.py'
     (ModuleNotFoundError: __path__ attribute not found on 'setup' while trying to find 'setup.py')
 

test/test_utils.py

@@ -0,0 +1,16 @@
+import numpy as np
+from deerlab import sophegrid
+
+
+# ======================================================================
+# Test sophegrid function
+
+def test_sophegrid():
+    # Test that the function returns the values and weights summing to 1
+    # Comparison taken from EasySpin 6.0
+    phi, theta, weights = sophegrid(4,np.pi*2,3)
+
+    assert np.allclose(weights.sum(),1)
+    assert np.allclose(phi, np.array([0,0,1.57079632679490,3.14159265358979,4.71238898038469,0,0.785398163397448,1.57079632679490,2.35619449019235,3.14159265358979,3.92699081698724,4.71238898038469,5.49778714378214]))
+    assert np.allclose(theta, np.array([0,0.785398163397448,0.785398163397448,0.785398163397448,0.785398163397448,1.57079632679490,1.57079632679490,1.57079632679490,1.57079632679490,1.57079632679490,1.57079632679490,1.57079632679490,1.57079632679490]))
+    assert np.allclose(weights*4*np.pi, np.array([0.956558005801449,1.70021769237074,1.70021769237074,1.70021769237074,1.70021769237074,0.601117729884346,0.601117729884346,0.601117729884346,0.601117729884346,0.601117729884346,0.601117729884346,0.601117729884346,0.601117729884346]))