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

Timestamp:

Mar 26, 2017 11:33:16 PM (7 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/AbstractFitEngine.py (modified) (9 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):