Changeset 74b0495 in sasmodels
 Timestamp:
 Oct 10, 2017 1:39:39 PM (5 years ago)
 Branches:
 master, core_shell_microgels, costrafo411, magnetic_model, ticket1257vesicleproduct, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
 Children:
 09141ff
 Parents:
 b76191e
 Files:

 2 deleted
 4 edited
Legend:
 Unmodified
 Added
 Removed

example/oriented_usans.py
r1cd24b4 r74b0495 6 6 7 7 # Spherical particle data, not ellipsoids 8 sans, usans = load_data(' ../../sasview/sasview/test/1d_data/latex_smeared.xml')8 sans, usans = load_data('latex_smeared.xml', index='all') 9 9 usans.qmin, usans.qmax = np.min(usans.x), np.max(usans.x) 10 10 usans.mask = (usans.x < 0.0) 
example/simul_fit.py
r1a4d4c0 r74b0495 1 #!/usr/bin/env python2 # * coding: utf8 *3 4 # To Sasview/documents/scripts5 6 1 from bumps.names import * 7 2 from sasmodels.core import load_model … … 9 4 from sasmodels.data import load_data, plot_data 10 5 6 # latex data, same sample usans and sans 7 # particles radius ~2300, uniform dispersity 8 datasets = load_data('latex_smeared.xml', index='all') 9 #[print(data) for data in datasets] 11 10 12 """ IMPORT THE DATA USED """ 13 datafiles = ['latex_smeared_out_0.txt', 'latex_smeared_out_1.txt'] 14 datasets = [load_data(el) for el in datafiles] 11 # A single sphere model to share between the datasets. We will use 12 # FreeVariables below to set the parameters that are independent between 13 # the datasets. 14 kernel = load_model('sphere') 15 pars = dict(scale=0.01, background=0.0, sld=5.0, sld_solvent=0.0, radius=1500., 16 #radius_pd=0.1, radius_pd_n=35, 17 ) 18 model = Model(kernel, **pars) 15 19 16 for data in datasets: 17 data.qmin = 0.0 18 data.qmax = 10.0 20 # radius and polydispersity (if any) are shared 21 model.radius.range(0, inf) 22 #model.radius_pd.range(0, 1) 19 23 20 #sphere model 21 kernel = load_model('sphere', dtype="single") 22 pars = dict(scale=0.01, background=0.0, sld=1.0, sld_solvent=6.0, radius=1500.) 23 model = Model(kernel, **pars) 24 model.radius.range(0, inf) 25 #model.background.range(inf, inf) 26 #model.scale.range(0, inf) 27 model.sld.range(inf, inf) 28 model.sld_solvent.range(inf, inf) 24 # Contrast and dilution are the same for both measurements, but are not 25 # separable with a single measurement (i.e., I(q) ~ F(q) contrast^2 Vf), 26 # so fit one of scale, sld or solvent sld. With absolute scaling from 27 # data reduction, can use the same parameter for both datasets. 28 model.scale.range(0, inf) 29 #model.sld.range(inf, inf) 30 #model.sld_solvent.range(inf, inf) 29 31 32 # Background is different for sans and usans so set it as a free variable 33 # in the model. 30 34 free = FreeVariables( 31 names=[data. filenamefor data in datasets],35 names=[data.run[0] for data in datasets], 32 36 background=model.background, 33 scale=model.scale,34 37 ) 35 38 free.background.range(inf, inf) 36 free.scale.range(0, inf)37 39 38 M = [Experiment(data=data, model=model) for data in datasets] 40 # Note: can access the parameters for the individual models using 41 # free.background[0] and free.background[1], setting constraints or 42 # ranges as appropriate. 43 44 # For more complex systems where different datasets require independent models, 45 # separate models can be defined, with parameters tied together using 46 # constraint expressions. For example, the following could be used to fit 47 # data set 1 to spheres and data set 2 to cylinders of the same volume: 48 # model1 = Model(load_model('sphere')) 49 # model2 = Model(load_model('cylinder')) 50 # model1.sld = model2.sld 51 # model1.sld_solvent = model2.sld_solvent 52 # model1.scale = model2.scale 53 # # set cylinders and spheres to the same volume 54 # model1.radius = (3/4*model2.radius**2*model2.length)**(1/3) 55 # model1.background.range(0, 2) 56 # model2.background.range(0, 2) 57 58 # Setup the experiments, sharing the same model across all datasets. 59 M = [Experiment(data=data, model=model, name=data.run[0]) for data in datasets] 39 60 40 61 problem = FitProblem(M, freevars=free) 41 42 print(problem._parameters) 
sasmodels/bumps_model.py
r3330bb4 r74b0495 133 133 """ 134 134 _cache = None # type: Dict[str, np.ndarray] 135 def __init__(self, data, model, cutoff=1e5 ):135 def __init__(self, data, model, cutoff=1e5, name=None): 136 136 # type: (Data, Model, float) > None 137 137 # remember inputs so we can inspect from outside 138 self.name = data.filename if name is None else name 138 139 self.model = model 139 140 self.cutoff = cutoff … … 204 205 """ 205 206 data, theory, resid = self._data, self.theory(), self.residuals() 206 plot_theory(data, theory, resid, view, Iq_calc=self.Iq_calc) 207 # TODO: hack to display oriented usans 2D pattern 208 Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None 209 plot_theory(data, theory, resid, view, Iq_calc=Iq_calc) 207 210 208 211 def simulate_data(self, noise=None): 
sasmodels/data.py
r630156b r74b0495 44 44 Data = Union["Data1D", "Data2D", "SesansData"] 45 45 46 def load_data(filename ):46 def load_data(filename, index=0): 47 47 # type: (str) > Data 48 48 """ … … 55 55 filename, indexstr = filename[:1].split('[') 56 56 index = int(indexstr) 57 else:58 index = None59 57 datasets = loader.load(filename) 60 58 if datasets is None: … … 62 60 if not isinstance(datasets, list): 63 61 datasets = [datasets] 64 if index is None and len(datasets) > 1: 65 raise ValueError("Need to specify filename[index] for multipart data") 66 data = datasets[index if index is not None else 0] 67 if hasattr(data, 'x'): 68 data.qmin, data.qmax = data.x.min(), data.x.max() 69 data.mask = (np.isnan(data.y) if data.y is not None 70 else np.zeros_like(data.x, dtype='bool')) 71 elif hasattr(data, 'qx_data'): 72 data.mask = ~data.mask 73 return data 62 for data in datasets: 63 if hasattr(data, 'x'): 64 data.qmin, data.qmax = data.x.min(), data.x.max() 65 data.mask = (np.isnan(data.y) if data.y is not None 66 else np.zeros_like(data.x, dtype='bool')) 67 elif hasattr(data, 'qx_data'): 68 data.mask = ~data.mask 69 return datasets[index] if index != 'all' else datasets 74 70 75 71
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