Changes in / [d38f244:4050e6a] in sasmodels

File:

• sasmodels/resolution.py (modified) (7 diffs)

sasmodels/resolution.py

@@ -17 +17 @@
 
 MINIMUM_RESOLUTION = 1e-8
-MINIMUM_ABSOLUTE_Q = 0.02  # relative to the minimum q in the data
+
+
+# When extrapolating to -q, what is the minimum positive q relative to q_min
+# that we wish to calculate?
+MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION = 0.01
 
 class Resolution(object):
@@ -78 +82 @@
         self.q_calc = (pinhole_extend_q(q, q_width, nsigma=nsigma)
                        if q_calc is None else np.sort(q_calc))
-
-        # Protect against models which are not defined for very low q.  Limit
-        # the smallest q value evaluated (in absolute) to 0.02*min
-        cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
-        self.q_calc = self.q_calc[abs(self.q_calc) >= cutoff]
-
-        # Build weight matrix from calculated q values
         self.weight_matrix = pinhole_resolution(self.q_calc, self.q,
                                 np.maximum(q_width, MINIMUM_RESOLUTION))
-        self.q_calc = abs(self.q_calc)
 
     def apply(self, theory):
@@ -127 +123 @@
         self.q_calc = slit_extend_q(q, qx_width, qy_width) \
             if q_calc is None else np.sort(q_calc)
-
-        # Protect against models which are not defined for very low q.  Limit
-        # the smallest q value evaluated (in absolute) to 0.02*min
-        cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)
-        self.q_calc = self.q_calc[abs(self.q_calc) >= cutoff]
-
-        # Build weight matrix from calculated q values
         self.weight_matrix = \
             slit_resolution(self.q_calc, self.q, qx_width, qy_width)
-        self.q_calc = abs(self.q_calc)
 
     def apply(self, theory):
@@ -165 +153 @@
     # neither trapezoid nor Simpson's rule improved the accuracy.
     edges = bin_edges(q_calc)
-    #edges[edges < 0.0] = 0.0 # clip edges below zero
+    edges[edges < 0.0] = 0.0 # clip edges below zero
     cdf = erf((edges[:, None] - q[None, :]) / (sqrt(2.0)*q_width)[None, :])
     weights = cdf[1:] - cdf[:-1]
@@ -298 +286 @@
     # The current algorithm is a midpoint rectangle rule.
     q_edges = bin_edges(q_calc) # Note: requires q > 0
-    #q_edges[q_edges < 0.0] = 0.0 # clip edges below zero
+    q_edges[q_edges < 0.0] = 0.0 # clip edges below zero
     weights = np.zeros((len(q), len(q_calc)), 'd')
 
@@ -404 +392 @@
     interval.
 
-    Note that extrapolated values may be negative.
+    if *q_min* is zero or less then *q[0]/10* is used instead.
     """
     q = np.sort(q)
     if q_min + 2*MINIMUM_RESOLUTION < q[0]:
+        if q_min <= 0: q_min = q_min*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION
         n_low = np.ceil((q[0]-q_min) / (q[1]-q[0])) if q[1] > q[0] else 15
         q_low = np.linspace(q_min, q[0], n_low+1)[:-1]
@@ -459 +448 @@
         log_delta_q = log(10.) / points_per_decade
     if q_min < q[0]:
-        if q_min < 0: q_min = q[0]*MINIMUM_ABSOLUTE_Q
+        if q_min < 0: q_min = q[0]*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION
         n_low = log_delta_q * (log(q[0])-log(q_min))
         q_low = np.logspace(log10(q_min), log10(q[0]), np.ceil(n_low)+1)[:-1]