1 | from bumps.names import * |
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2 | |
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3 | from sasmodels import core, bumps_model |
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4 | |
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5 | if True: # fix when data loader exists |
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6 | # from sas.dataloader.readers\ |
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7 | from sas.dataloader.loader import Loader |
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8 | loader = Loader() |
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9 | filename = 'sphere.ses' |
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10 | data = loader.load(filename) |
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11 | if data is None: raise IOError("Could not load file %r"%(filename,)) |
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12 | data.x /= 10 |
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13 | # print data |
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14 | # data = load_sesans('mydatfile.pz') |
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15 | # sans_data = load_sans('mysansfile.xml') |
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16 | |
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17 | else: |
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18 | SElength = np.linspace(0, 2400, 61) # [A] |
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19 | data = np.ones_like(SElength) |
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20 | err_data = np.ones_like(SElength)*0.03 |
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21 | |
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22 | class Sample: |
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23 | zacceptance = 0.1 # [A^-1] |
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24 | thickness = 0.2 # [cm] |
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25 | |
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26 | class SESANSData1D: |
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27 | #q_zmax = 0.23 # [A^-1] |
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28 | lam = 0.2 # [nm] |
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29 | x = SElength |
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30 | y = data |
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31 | dy = err_data |
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32 | sample = Sample() |
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33 | data = SESANSData1D() |
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34 | |
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35 | radius = 1000 |
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36 | data.Rmax = 3*radius # [A] |
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37 | |
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38 | ## Sphere parameters |
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39 | |
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40 | kernel = core.load_model("sphere", dtype='single') |
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41 | phi = Parameter(0.1, name="phi") |
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42 | model = bumps_model.Model(kernel, |
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43 | scale=phi*(1-phi), sld=7.0, solvent_sld=1.0, radius=radius, |
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44 | ) |
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45 | phi.range(0.001,0.5) |
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46 | #model.radius.pmp(40) |
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47 | model.radius.range(1,10000) |
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48 | #model.sld.pm(5) |
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49 | #model.background |
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50 | #model.radius_pd=0 |
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51 | #model.radius_pd_n=0 |
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52 | |
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53 | ### Tri-Axial Ellipsoid |
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54 | # |
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55 | #kernel = core.load_model("triaxial_ellipsoid", dtype='single') |
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56 | #phi = Parameter(0.1, name='phi') |
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57 | #model = bumps_model.Model(kernel, |
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58 | # scale=phi*(1-phi), sld=7.0, solvent_sld=1.0, radius=radius, |
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59 | # ) |
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60 | #phi.range(0.001,0.90) |
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61 | ##model.radius.pmp(40) |
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62 | #model.radius.range(100,10000) |
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63 | ##model.sld.pmp(5) |
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64 | ##model.background |
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65 | ##model.radius_pd = 0 |
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66 | ##model.radius_pd_n = 0 |
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67 | |
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68 | if False: # have sans data |
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69 | M_sesans = bumps_model.Experiment(data=data, model=model) |
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70 | M_sans = bumps_model.Experiment(data=sans_data, model=model) |
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71 | problem = FitProblem([M_sesans, M_sans]) |
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72 | else: |
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73 | M_sesans = bumps_model.Experiment(data=data, model=model) |
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74 | problem = FitProblem(M_sesans) |
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