Changeset 9e7837a in sasmodels
 Timestamp:
 Jul 27, 2018 7:33:49 PM (3 weeks ago)
 Branches:
 master, beta_approx, beta_approx_lazy_results, beta_approx_new_R_eff, doc_update, ticket1104resolution
 Children:
 0168844
 Parents:
 581661f
 File:

 1 edited
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sasmodels/resolution.py
r0b9c6df r9e7837a 20 20 MINIMUM_RESOLUTION = 1e8 21 21 MINIMUM_ABSOLUTE_Q = 0.02 # relative to the minimum q in the data 22 PINHOLE_N_SIGMA = 2.5 # From: Barker & Pedersen 1995 JAC 22 # According to (Barker & Pedersen 1995 JAC), 2.5 sigma is a good limit. 23 # According to simulations with github.com:scattering/sansresolution.git 24 # it is better to use asymmetric bounds (2.5, 3.0) 25 PINHOLE_N_SIGMA = (2.5, 3.0) 23 26 24 27 class Resolution(object): … … 90 93 # from the geometry, they may appear since we are using a truncated 91 94 # gaussian to represent resolution rather than a skew distribution. 92 cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q)93 self.q_calc = self.q_calc[self.q_calc >= cutoff]95 #cutoff = MINIMUM_ABSOLUTE_Q*np.min(self.q) 96 #self.q_calc = self.q_calc[self.q_calc >= cutoff] 94 97 95 98 # Build weight matrix from calculated q values … … 188 191 cdf = erf((edges[:, None]  q[None, :]) / (sqrt(2.0)*q_width)[None, :]) 189 192 weights = cdf[1:]  cdf[:1] 190 # Limit q range to +/ 2.5 sigma 191 qhigh = q + nsigma*q_width 192 #qlow = q  nsigma*q_width # linear limits 193 qlow = q*q/qhigh # log limits 193 # Limit q range to (2.5,+3) sigma 194 try: 195 nsigma_low, nsigma_high = nsigma 196 except TypeError: 197 nsigma_low = nsigma_high = nsigma 198 qhigh = q + nsigma_high*q_width 199 qlow = q  nsigma_low*q_width # linear limits 200 ##qlow = q*q/qhigh # log limits 194 201 weights[q_calc[:, None] < qlow[None, :]] = 0. 195 202 weights[q_calc[:, None] > qhigh[None, :]] = 0. … … 365 372 366 373 367 def pinhole_extend_q(q, q_width, nsigma= 3):374 def pinhole_extend_q(q, q_width, nsigma=PINHOLE_N_SIGMA): 368 375 """ 369 376 Given *q* and *q_width*, find a set of sampling points *q_calc* so … … 371 378 function. 372 379 """ 373 q_min, q_max = np.min(q  nsigma*q_width), np.max(q + nsigma*q_width) 380 try: 381 nsigma_low, nsigma_high = nsigma 382 except TypeError: 383 nsigma_low = nsigma_high = nsigma 384 q_min, q_max = np.min(q  nsigma_low*q_width), np.max(q + nsigma_high*q_width) 374 385 return linear_extrapolation(q, q_min, q_max) 375 386
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