Changeset 9a5097c in sasview for src/sas/sascalc/pr/fit/BumpsFitting.py

Timestamp:

Mar 26, 2017 11:33:16 PM (8 years ago)

Author:

andyfaff

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

ed2276f

Parents:

9146ed9

Message:

MAINT: import numpy as np

File:

• src/sas/sascalc/pr/fit/BumpsFitting.py (modified) (6 diffs)

src/sas/sascalc/pr/fit/BumpsFitting.py

@@ -5 +5 @@
 from datetime import timedelta, datetime
 
-import numpy
+import numpy as np
 
 from bumps import fitters
@@ -96 +96 @@
         try:
             p = history.population_values[0]
-            n,p = len(p), numpy.sort(p)
+            n,p = len(p), np.sort(p)
             QI,Qmid, = int(0.2*n),int(0.5*n)
             self.convergence.append((best, p[0],p[QI],p[Qmid],p[-1-QI],p[-1]))
@@ -193 +193 @@
 
     def numpoints(self):
-        return numpy.sum(self.data.idx) # number of fitted points
+        return np.sum(self.data.idx) # number of fitted points
 
     def nllf(self):
-        return 0.5*numpy.sum(self.residuals()**2)
+        return 0.5*np.sum(self.residuals()**2)
 
     def theory(self):
@@ -293 +293 @@
             R.success = result['success']
             if R.success:
-                R.stderr = numpy.hstack((result['stderr'][fitted_index],
-                                         numpy.NaN*numpy.ones(len(fitness.computed_pars))))
-                R.pvec = numpy.hstack((result['value'][fitted_index],
+                R.stderr = np.hstack((result['stderr'][fitted_index],
+                                      np.NaN*np.ones(len(fitness.computed_pars))))
+                R.pvec = np.hstack((result['value'][fitted_index],
                                       [p.value for p in fitness.computed_pars]))
-                R.fitness = numpy.sum(R.residuals**2)/(fitness.numpoints() - len(fitted_index))
+                R.fitness = np.sum(R.residuals**2)/(fitness.numpoints() - len(fitted_index))
             else:
-                R.stderr = numpy.NaN*numpy.ones(len(param_list))
-                R.pvec = numpy.asarray( [p.value for p in fitness.fitted_pars+fitness.computed_pars])
-                R.fitness = numpy.NaN
+                R.stderr = np.NaN*np.ones(len(param_list))
+                R.pvec = np.asarray( [p.value for p in fitness.fitted_pars+fitness.computed_pars])
+                R.fitness = np.NaN
             R.convergence = result['convergence']
             if result['uncertainty'] is not None:
@@ -331 +331 @@
     max_step = steps + options.get('burn', 0)
     pars = [p.name for p in problem._parameters]
-    #x0 = numpy.asarray([p.value for p in problem._parameters])
+    #x0 = np.asarray([p.value for p in problem._parameters])
     options['monitors'] = [
         BumpsMonitor(handler, max_step, pars, problem.dof),
@@ -352 +352 @@
 
     convergence_list = options['monitors'][-1].convergence
-    convergence = (2*numpy.asarray(convergence_list)/problem.dof
-                   if convergence_list else numpy.empty((0,1),'d'))
+    convergence = (2*np.asarray(convergence_list)/problem.dof
+                   if convergence_list else np.empty((0,1),'d'))
 
     success = best is not None