Changeset fa4af76 in sasview for src/sas/sascalc/dataloader/manipulations.py

Timestamp:

Apr 7, 2017 7:51:47 AM (7 years ago)

Author:

Ricardo Ferraz Leal <ricleal@…>

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:

168d359

Parents:

ccc7192

Message:

Refactoring

File:

• src/sas/sascalc/dataloader/manipulations.py (modified) (11 diffs)

src/sas/sascalc/dataloader/manipulations.py

@@ +1 @@
+from __future__ import division
 """
 Data manipulations for 2D data sets.
@@ -560 +561 @@
 
         dq_data = None
-        if data2D.dqx_data != None and data2D.dqy_data != None:
+        if data2D.dqx_data is not None and data2D.dqy_data is not None:
             dq_data = get_dq_data(data2D)
 
         #q_data_max = np.max(q_data)
-        if len(data2D.q_data) == None:
+        if len(data2D.q_data) is None:
             msg = "Circular averaging: invalid q_data: %g" % data2D.q_data
             raise RuntimeError(msg)
@@ -610 +611 @@
             else:
                 err_y[i_q] += frac * frac * err_data[npt] * err_data[npt]
-            if dq_data != None:
+            if dq_data is not None:
                 # To be consistent with dq calculation in 1d reduction,
                 # we need just the averages (not quadratures) because
@@ -625 +626 @@
                 err_y[n] = -err_y[n]
             err_y[n] = math.sqrt(err_y[n])
-            # if err_x != None:
+            # if err_x is not None:
             #    err_x[n] = math.sqrt(err_x[n])
 
@@ -634 +635 @@
         idx = (np.isfinite(y)) & (np.isfinite(x))
 
-        if err_x != None:
+        if err_x is not None:
             d_x = err_x[idx] / y_counts[idx]
         else:
@@ -791 +792 @@
 
         dq_data = None
-        if data2D.dqx_data != None and data2D.dqy_data != None:
+        if data2D.dqx_data is not None and data2D.dqy_data is not None:
             dq_data = get_dq_data(data2D)
 
@@ -799 +800 @@
         y_err = np.zeros(self.nbins)
         x_err = np.zeros(self.nbins)
-        y_counts = np.zeros(self.nbins)
+        y_counts = np.zeros(self.nbins) # Cycle counts (for the mean)
 
         # Get the min and max into the region: 0 <= phi < 2Pi
@@ -817 +818 @@
             phi_value = math.atan2(qy_data[n], qx_data[n]) + math.pi
 
-            # No need to calculate the frac when all data are within range
+            # No need to calculate: data outside of the radius
             if self.r_min > q_value or q_value > self.r_max:
                 continue
@@ -844 +845 @@
                                   phi_value < phi_max)
 
+            # data oustide of the phi range
             if not is_in:
                 continue
+        
             # Check which type of averaging we need
             if run.lower() == 'phi':
                 temp_x = (self.nbins) * (phi_value - self.phi_min)
                 temp_y = (self.phi_max - self.phi_min)
-                i_bin = int(math.floor(temp_x / temp_y))
             else:
                 temp_x = (self.nbins) * (q_value - self.r_min)
                 temp_y = (self.r_max - self.r_min)
-                i_bin = int(math.floor(temp_x / temp_y))
+            # Bin index calulation
+            i_bin = int(math.floor(temp_x / temp_y))
 
             # Take care of the edge case at phi = 2pi.
@@ -863 +866 @@
             y[i_bin] += data_n
             x[i_bin] += q_value
-            if err_data[n] == None or err_data[n] == 0.0:
+            if err_data[n] is None or err_data[n] == 0.0:
                 if data_n < 0:
                     data_n = -data_n
-                y_err[i_bin] +=  data_n
+                y_err[i_bin] += data_n
             else:
-                y_err[i_bin] += err_data[n] * err_data[n]
-
-            if dq_data != None:
+                y_err[i_bin] += err_data[n]**2
+
+            if dq_data is not None:
                 # To be consistent with dq calculation in 1d reduction,
                 # we need just the averages (not quadratures) because
@@ -900 +903 @@
         y_err[y_err == 0] = np.average(y_err)
         idx = (np.isfinite(y) & np.isfinite(y_err))
-        if x_err != None:
+        if x_err is not None:
             d_x = x_err[idx] / y_counts[idx]
         else: