Changeset 9a5097c in sasview for src/sas/sasgui/perspectives/fitting/model_thread.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/sasgui/perspectives/fitting/model_thread.py (modified) (8 diffs)

src/sas/sasgui/perspectives/fitting/model_thread.py

@@ -4 +4 @@
 
 import time
-import numpy
+import numpy as np
 import math
 from sas.sascalc.data_util.calcthread import CalcThread
@@ -68 +68 @@
 
         # Define matrix where data will be plotted
-        radius = numpy.sqrt((self.data.qx_data * self.data.qx_data) + \
+        radius = np.sqrt((self.data.qx_data * self.data.qx_data) + \
                     (self.data.qy_data * self.data.qy_data))
 
@@ -75 +75 @@
         index_model = (self.qmin <= radius) & (radius <= self.qmax)
         index_model = index_model & self.data.mask
-        index_model = index_model & numpy.isfinite(self.data.data)
+        index_model = index_model & np.isfinite(self.data.data)
 
         if self.smearer is not None:
@@ -91 +91 @@
                 self.data.qy_data[index_model]
             ])
-        output = numpy.zeros(len(self.data.qx_data))
+        output = np.zeros(len(self.data.qx_data))
         # output default is None
         # This method is to distinguish between masked
@@ -163 +163 @@
         """
         self.starttime = time.time()
-        output = numpy.zeros((len(self.data.x)))
+        output = np.zeros((len(self.data.x)))
         index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax)
 
@@ -175 +175 @@
                                                              self.qmax)
             mask = self.data.x[first_bin:last_bin+1]
-            unsmeared_output = numpy.zeros((len(self.data.x)))
+            unsmeared_output = np.zeros((len(self.data.x)))
             unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask)
             self.smearer.model = self.model
@@ -183 +183 @@
             # Check that the arrays are compatible. If we only have a model but no data,
             # the length of data.y will be zero.
-            if isinstance(self.data.y, numpy.ndarray) and output.shape == self.data.y.shape:
-                unsmeared_data = numpy.zeros((len(self.data.x)))
-                unsmeared_error = numpy.zeros((len(self.data.x)))
+            if isinstance(self.data.y, np.ndarray) and output.shape == self.data.y.shape:
+                unsmeared_data = np.zeros((len(self.data.x)))
+                unsmeared_error = np.zeros((len(self.data.x)))
                 unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\
                                                         * unsmeared_output[first_bin:last_bin+1]\
@@ -209 +209 @@
 
         if p_model is not None and s_model is not None:
-            sq_values = numpy.zeros((len(self.data.x)))
-            pq_values = numpy.zeros((len(self.data.x)))
+            sq_values = np.zeros((len(self.data.x)))
+            pq_values = np.zeros((len(self.data.x)))
             sq_values[index] = s_model.evalDistribution(self.data.x[index])
             pq_values[index] = p_model.evalDistribution(self.data.x[index])