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

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

Location:

src/sas/sascalc/pr/fit

Files:

• AbstractFitEngine.py (modified) (9 diffs)
• BumpsFitting.py (modified) (6 diffs)
• Loader.py (modified) (2 diffs)
• expression.py (modified) (4 diffs)

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

@@ -4 +4 @@
 import sys
 import math
-import numpy
+import numpy as np
 
 from sas.sascalc.dataloader.data_info import Data1D
@@ -162 +162 @@
         # constant, or dy data
         if dy is None or dy == [] or dy.all() == 0:
-            self.dy = numpy.ones(len(y))
+            self.dy = np.ones(len(y))
         else:
-            self.dy = numpy.asarray(dy).copy()
+            self.dy = np.asarray(dy).copy()
 
         ## Min Q-value
         #Skip the Q=0 point, especially when y(q=0)=None at x[0].
         if min(self.x) == 0.0 and self.x[0] == 0 and\
-                     not numpy.isfinite(self.y[0]):
+                     not np.isfinite(self.y[0]):
             self.qmin = min(self.x[self.x != 0])
         else:
@@ -188 +188 @@
         # Skip Q=0 point, (especially for y(q=0)=None at x[0]).
         # ToDo: Find better way to do it.
-        if qmin == 0.0 and not numpy.isfinite(self.y[qmin]):
+        if qmin == 0.0 and not np.isfinite(self.y[qmin]):
             self.qmin = min(self.x[self.x != 0])
         elif qmin != None:
@@ -239 +239 @@
         """
         # Compute theory data f(x)
-        fx = numpy.zeros(len(self.x))
+        fx = np.zeros(len(self.x))
         fx[self.idx_unsmeared] = fn(self.x[self.idx_unsmeared])
        
@@ -247 +247 @@
                               self._last_unsmeared_bin)
         ## Sanity check
-        if numpy.size(self.dy) != numpy.size(fx):
+        if np.size(self.dy) != np.size(fx):
             msg = "FitData1D: invalid error array "
-            msg += "%d <> %d" % (numpy.shape(self.dy), numpy.size(fx))
+            msg += "%d <> %d" % (np.shape(self.dy), np.size(fx))
             raise RuntimeError, msg
         return (self.y[self.idx] - fx[self.idx]) / self.dy[self.idx], fx[self.idx]
@@ -300 +300 @@
         ## new error image for fitting purpose
         if self.err_data == None or self.err_data == []:
-            self.res_err_data = numpy.ones(len(self.data))
+            self.res_err_data = np.ones(len(self.data))
         else:
             self.res_err_data = copy.deepcopy(self.err_data)
         #self.res_err_data[self.res_err_data==0]=1
         
-        self.radius = numpy.sqrt(self.qx_data**2 + self.qy_data**2)
+        self.radius = np.sqrt(self.qx_data**2 + self.qy_data**2)
         
         # Note: mask = True: for MASK while mask = False for NOT to mask
@@ -311 +311 @@
                             (self.radius <= self.qmax))
         self.idx = (self.idx) & (self.mask)
-        self.idx = (self.idx) & (numpy.isfinite(self.data))
-        self.num_points = numpy.sum(self.idx)
+        self.idx = (self.idx) & (np.isfinite(self.data))
+        self.num_points = np.sum(self.idx)
 
     def set_smearer(self, smearer):
@@ -334 +334 @@
         if qmax != None:
             self.qmax = qmax
-        self.radius = numpy.sqrt(self.qx_data**2 + self.qy_data**2)
+        self.radius = np.sqrt(self.qx_data**2 + self.qy_data**2)
         self.idx = ((self.qmin <= self.radius) &\
                             (self.radius <= self.qmax))
         self.idx = (self.idx) & (self.mask)
-        self.idx = (self.idx) & (numpy.isfinite(self.data))
+        self.idx = (self.idx) & (np.isfinite(self.data))
         self.idx = (self.idx) & (self.res_err_data != 0)
 
@@ -351 +351 @@
         Number of measurement points in data set after masking, etc.
         """
-        return numpy.sum(self.idx)
+        return np.sum(self.idx)
 
     def residuals(self, fn):

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

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

@@ -2 +2 @@
 #import wx
 #import string
-import numpy
+import numpy as np
 
 class Load:
@@ -52 +52 @@
                     self.y.append(y)
                     self.dy.append(dy)
-                    self.dx = numpy.zeros(len(self.x))
+                    self.dx = np.zeros(len(self.x))
                 except:
                     print "READ ERROR", line

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

@@ -271 +271 @@
 
 def test_deps():
-    import numpy
+    import numpy as np
 
     # Null case
@@ -279 +279 @@
     _check("test1",[(2,7),(1,5),(1,4),(2,1),(3,1),(5,6)])
     _check("test1 renumbered",[(6,1),(7,3),(7,4),(6,7),(5,7),(3,2)])
-    _check("test1 numpy",numpy.array([(2,7),(1,5),(1,4),(2,1),(3,1),(5,6)]))
+    _check("test1 numpy",np.array([(2,7),(1,5),(1,4),(2,1),(3,1),(5,6)]))
 
     # No dependencies
@@ -291 +291 @@
 
     # large test for gross speed check
-    A = numpy.random.randint(4000,size=(1000,2))
+    A = np.random.randint(4000,size=(1000,2))
     A[:,1] += 4000  # Avoid cycles
     _check("test-large",A)
@@ -297 +297 @@
     # depth tests
     k = 200
-    A = numpy.array([range(0,k),range(1,k+1)]).T
+    A = np.array([range(0,k),range(1,k+1)]).T
     _check("depth-1",A)
 
-    A = numpy.array([range(1,k+1),range(0,k)]).T
+    A = np.array([range(1,k+1),range(0,k)]).T
     _check("depth-2",A)