Changes in / [502c7b8:01c8d9e] in sasmodels
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doc/guide/pd/polydispersity.rst
rd712a0f r29afc50 20 20 P(q) = \text{scale} \langle F^* F \rangle / V + \text{background} 21 21 22 where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an 23 average over the size distribution $f(x; \bar x, \sigma)$, giving 24 25 .. math:: 26 27 P(q) = \frac{\text{scale}}{V} \int_\mathbb{R} 28 f(x; \bar x, \sigma) F^2(q, x)\, dx + \text{background} 22 where $F$ is the scattering amplitude and $\langle\cdot\rangle$ denotes an 23 average over the size distribution. 29 24 30 25 Each distribution is characterized by a center value $\bar x$ or … … 46 41 with larger values of $N_\sigma$ required for heavier tailed distributions. 47 42 The scattering in general falls rapidly with $qr$ so the usual assumption 48 that $ f(r - 3\sigma_r)$ is tiny and therefore $f(r - 3\sigma_r)f(r - 3\sigma_r)$43 that $G(r - 3\sigma_r)$ is tiny and therefore $f(r - 3\sigma_r)G(r - 3\sigma_r)$ 49 44 will not contribute much to the average may not hold when particles are large. 50 45 This, too, will require increasing $N_\sigma$. … … 68 63 69 64 Additional distributions are under consideration. 70 71 .. note:: In 2009 IUPAC decided to introduce the new term 'dispersity' to replace72 the term 'polydispersity' (see `Pure Appl. Chem., (2009), 81(2),73 351-353 <http://media.iupac.org/publications/pac/2009/pdf/8102x0351.pdf>`_74 in order to make the terminology describing distributions of properties75 unambiguous. Throughout the SasView documentation we continue to use the76 term polydispersity because one of the consequences of the IUPAC change is77 that orientational polydispersity would not meet their new criteria (which78 requires dispersity to be dimensionless).79 65 80 66 Suggested Applications -
explore/beta/sasfit_compare.py
r01c8d9e r01c8d9e 1 1 from __future__ import division, print_function 2 2 # Make sasmodels available on the path 3 import sys, 3 import sys,os 4 4 BETA_DIR = os.path.dirname(os.path.realpath(__file__)) 5 5 #SASMODELS_DIR = os.path.dirname(os.path.dirname(BETA_DIR)) 6 6 SASMODELS_DIR = r"C:\Source\sasmodels" 7 7 sys.path.insert(0, SASMODELS_DIR) 8 8 import os 9 9 from collections import namedtuple 10 10 … … 223 223 if radius_effective is None: 224 224 radius_effective = radius_eff/total_weight 225 print("this is the effective radius for pure python",radius_effective) 225 226 if norm == 'sasfit': 226 227 IQD = F2 … … 231 232 # = F2/total_weight / total_volume/total_weight 232 233 # = F2/total_volume 234 233 235 IQD = F2/average_volume*1e-4*volfraction 234 236 F1 *= 1e-2 # Yun is using sld in 1/A^2, not 1e-6/A^2 … … 243 245 IQSD = IQD*SQ 244 246 IQBD = IQD*SQ_EFF 247 print("\n\n\n\n\n this is F1" + str(F1)) 248 249 print("\n\n\n\n\n this is F2" + str(F2)) 250 print("\n\n\n\n\n this is SQ" + str(SQ)) 245 251 return Theory(Q=q, F1=F1, F2=F2, P=IQD, S=SQ, I=IQSD, Seff=SQ_EFF, Ibeta=IQBD) 246 252 … … 334 340 Ipars.setdefault(k.replace("_pd_type", "_pd_nsigma"), nsigmas) 335 341 342 336 343 #Ppars['scale'] = Spars.get('volfraction', 1) 337 344 P = build_model(Pname, q) … … 410 417 target = sasmodels_theory(q, model, beta_mode=1, **pars) 411 418 actual = ellipsoid_pe(q, norm='sasview', **pars) 419 print(actual) 412 420 title = " ".join(("sasmodels", model, pd_type)) 413 421 compare(title, target, actual) … … 452 460 Q = data[0] 453 461 F1 = data[1] 454 F2 = data[2]455 462 P = data[3] 456 463 S = data[5] … … 464 471 Q = data[0] 465 472 F1 = data[1] 466 F2 = data[2]467 473 P = data[3] 468 474 S = data[5] -
sasmodels/models/core_shell_sphere.py
rdc76240 r2d81cfe 21 21 .. math:: 22 22 23 F (q) = \frac{3}{V_s}\left[23 F^2(q) = \frac{3}{V_s}\left[ 24 24 V_c(\rho_c-\rho_s)\frac{\sin(qr_c)-qr_c\cos(qr_c)}{(qr_c)^3} + 25 25 V_s(\rho_s-\rho_\text{solv})\frac{\sin(qr_s)-qr_s\cos(qr_s)}{(qr_s)^3}
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