Numpy2 support

deerlab/classes.py

@@ -381,7 +381,7 @@ def percentile(self,p):
             cdf = np.cumsum(pdf)
             cdf /= max(cdf)
             # Eliminate duplicates
-            cdf, index = np.lib.arraysetops.unique(cdf,return_index=True)
+            cdf, index = np.unique(cdf,return_index=True)
             # Interpolate requested percentile
             x[n] = np.interp(p/100,cdf,values[index])
 

deerlab/dd_models.py

@@ -110,7 +110,7 @@ def _multigaussfun(r,r0,sig):
     P = np.sqrt(1/(2*np.pi))*1/sig*np.exp(-0.5*((r.T-r0)/sig)**2)
     if not np.all(P==0):
         # Normalization
-        P = np.squeeze(P)/np.sum([np.trapz(c,r) for c in P.T])
+        P = np.squeeze(P)/np.sum([np.trapezoid(c,r) for c in P.T])
     else: 
         P = np.squeeze(P)
     return P

deerlab/diststats.py

@@ -109,7 +109,7 @@ def normalize(P):
     # Percentile function
     def pctile(r,P,p):
         cdf = cumtrapz(normalize(P),r,initial=0)
-        cdf, index = np.lib.arraysetops.unique(cdf,return_index=True)
+        cdf, index = np.unique(cdf,return_index=True)
         rpctile = np.interp(p/100,cdf,r[index])
         return rpctile
     # Expectation operator function

docsrc/source/changelog.rst

@@ -28,7 +28,9 @@ Release ``v1.1.3`` - Ongoing
 ------------------------------------------
 - |fix| : Removes unnecessary files from the docs
 - |efficiency| : Improves the performance of the ``dipolarkernel`` function by 10-30% (:pr:`473`), by caching the interpolation of he effective dipolar evolution time vector.
-- |fix| : add support for Python 3.12
+- |fix| : Add support for Python 3.12
+- |fix| : Adds support for Numpy 2.0
+
 
 Release ``v1.1.2`` - November 2023
 ------------------------------------------

test/test_dipolarkernel.py

@@ -1,6 +1,6 @@
 
 import numpy as np
-from numpy import pi, inf, NaN
+from numpy import pi, inf
 from deerlab.bg_models import bg_hom3d,bg_exp
 from deerlab.dd_models import dd_gauss
 from deerlab.dipolarkernel import dipolarkernel,elementarykernel_twospin