Changes in / [e9b17b18:2dcd6e7] in sasmodels

File:

• sasmodels/resolution.py (modified) (5 diffs)

sasmodels/resolution.py

@@ -20 +20 @@
 MINIMUM_RESOLUTION = 1e-8
 MINIMUM_ABSOLUTE_Q = 0.02  # relative to the minimum q in the data
-# According to (Barker & Pedersen 1995 JAC), 2.5 sigma is a good limit.
-# According to simulations with github.com:scattering/sansresolution.git
-# it is better to use asymmetric bounds (2.5, 3.0)
-PINHOLE_N_SIGMA = (2.5, 3.0)
+PINHOLE_N_SIGMA = 2.5 # From: Barker & Pedersen 1995 JAC
 
 class Resolution(object):
@@ -93 +90 @@
         # from the geometry, they may appear since we are using a truncated
         # gaussian to represent resolution rather than a skew distribution.
-        #cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
-        #self.q_calc = self.q_calc[self.q_calc >= cutoff]
+        cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
+        self.q_calc = self.q_calc[self.q_calc >= cutoff]
 
         # Build weight matrix from calculated q values
@@ -191 +188 @@
     cdf = erf((edges[:, None] - q[None, :]) / (sqrt(2.0)*q_width)[None, :])
     weights = cdf[1:] - cdf[:-1]
-    # Limit q range to (-2.5,+3) sigma
-    try:
-        nsigma_low, nsigma_high = nsigma
-    except TypeError:
-        nsigma_low = nsigma_high = nsigma
-    qhigh = q + nsigma_high*q_width
-    qlow = q - nsigma_low*q_width  # linear limits
-    ##qlow = q*q/qhigh  # log limits
+    # Limit q range to +/- 2.5 sigma
+    qhigh = q + nsigma*q_width
+    #qlow = q - nsigma*q_width  # linear limits
+    qlow = q*q/qhigh  # log limits
     weights[q_calc[:, None] < qlow[None, :]] = 0.
     weights[q_calc[:, None] > qhigh[None, :]] = 0.
@@ -372 +365 @@
 
 
-def pinhole_extend_q(q, q_width, nsigma=PINHOLE_N_SIGMA):
+def pinhole_extend_q(q, q_width, nsigma=3):
     """
     Given *q* and *q_width*, find a set of sampling points *q_calc* so
@@ -378 +371 @@
     function.
     """
-    try:
-        nsigma_low, nsigma_high = nsigma
-    except TypeError:
-        nsigma_low = nsigma_high = nsigma
-    q_min, q_max = np.min(q - nsigma_low*q_width), np.max(q + nsigma_high*q_width)
+    q_min, q_max = np.min(q - nsigma*q_width), np.max(q + nsigma*q_width)
     return linear_extrapolation(q, q_min, q_max)