AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead. #180
Description
Tutorial: MC/SA in GSAS-II
Step 7. Monte Carlo/Simulated Annealing
C:\Windows\System32>call C:\g2\Scripts\activate
Adding GSAS-II location to Python system path
5 values read from C:\Users\mnowa.GSASII\config.ini
GSAS-II binary directory: C:\g2\GSAS-II\GSASII-bin\win_64_p3.13_n2.2
Python/module versions loaded:
Python: 3.13.3 from C:\g2\python.exe.
wx: 4.2.2
matplotlib: 3.10.1
numpy: 2.2.5
scipy: 1.15.2
OpenGL: 3.1.9
Image: 11.1.0 (PIL or Pillow)
Platform: win32 64bit AMD64
Binary ver: 5805, v5.2.0
GSAS-II: 75ce746, 04-May-2025 10:52 (0.3 days old). Tag: #5810, v5.4.3
loading from file: C:\Users\mnowa\Documents\G2tutorials\2-amino.gpx
GPX load successful. Last saved with GSAS-II revision #5810, v5.4.3 git 75ce746
Configuration settings saved as C:\Users\mnowa.GSASII\config.ini
MC/SA run:
Reflection type:PWDR Total No. reflections: 91
Minimum d-spacing used: 2.50 No. reflections used: 36
Number of parameters varied: 7
Traceback (most recent call last):
File "C:\g2\GSAS-II\GSASII\GSASIIphsGUI.py", line 15512, in OnRunSingleMCSA
RunMCSA('single')
~~~~~~~^^^^^^^^^^
File "C:\g2\GSAS-II\GSASII\GSASIIphsGUI.py", line 15574, in RunMCSA
Result,tsum,nsum,rcov = G2mth.mcsaSearch(data,RBdata,reflType,reflData,covData,pgbar)
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\g2\GSAS-II\GSASII\GSASIImath.py", line 5966, in mcsaSearch
results = anneal(mcsaCalc,x0,args=(refs,rcov,cosTable,ifInv,allFF,RBdata,varyList,parmDict),
schedule=MCSA['Algorithm'], dwell=MCSA['Annealing'][2],maxiter=10000,
...<2 lines>...
lower=lower, upper=upper, slope=MCSA['log slope'],ranStart=MCSA.get('ranStart',False),
ranRange=MCSA.get('ranRange',10.)/100.,autoRan=MCSA.get('autoRan',False),dlg=pgbar)
File "C:\g2\GSAS-II\GSASII\GSASIImath.py", line 5450, in anneal
best_state.cost = numpy.Inf
^^^^^^^^^
File "C:\g2\Lib\site-packages\numpy_init_.py", line 400, in getattr
raise AttributeError(
...<3 lines>...
)
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.