1 | import warnings |
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2 | |
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3 | import numpy as np |
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4 | |
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5 | from . import models |
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6 | from . import weights |
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7 | |
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8 | try: |
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9 | from .kernelcl import load_model |
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10 | except ImportError,exc: |
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11 | warnings.warn(str(exc)) |
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12 | warnings.warn("using ctypes instead") |
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13 | from .kerneldll import load_model |
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14 | |
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15 | def load_model_definition(model_name): |
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16 | __import__('sasmodels.models.'+model_name) |
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17 | model_definition = getattr(models, model_name, None) |
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18 | return model_definition |
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19 | |
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20 | # load_model is imported above. It looks like the following |
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21 | #def load_model(model_definition, dtype='single): |
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22 | # if kerneldll: |
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23 | # if source is newer than compiled: compile |
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24 | # load dll |
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25 | # return kernel |
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26 | # elif kernelcl: |
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27 | # compile source on context |
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28 | # return kernel |
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29 | |
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30 | |
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31 | def make_kernel(model, q_vectors): |
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32 | """ |
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33 | Return a computation kernel from the model definition and the q input. |
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34 | """ |
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35 | input = model.make_input(q_vectors) |
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36 | return model(input) |
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37 | |
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38 | def get_weights(kernel, pars, name): |
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39 | """ |
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40 | Generate the distribution for parameter *name* given the parameter values |
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41 | in *pars*. |
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42 | |
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43 | Searches for "name", "name_pd", "name_pd_type", "name_pd_n", "name_pd_sigma" |
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44 | """ |
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45 | relative = name in kernel.info['partype']['pd-rel'] |
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46 | limits = kernel.info['limits'] |
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47 | disperser = pars.get(name+'_pd_type', 'gaussian') |
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48 | value = pars.get(name) |
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49 | npts = pars.get(name+'_pd_n', 0) |
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50 | width = pars.get(name+'_pd', 0.0) |
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51 | nsigma = pars.get(name+'_pd_nsigma', 3.0) |
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52 | v,w = weights.get_weights( |
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53 | disperser, npts, width, nsigma, |
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54 | value, limits[name], relative) |
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55 | return v,w/np.sum(w) |
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56 | |
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57 | def call_kernel(kernel, pars): |
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58 | fixed_pars = [pars.get(name, kernel.info['defaults'][name]) |
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59 | for name in kernel.fixed_pars] |
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60 | pd_pars = [get_weights(kernel, pars, name) for name in kernel.pd_pars] |
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61 | return kernel(fixed_pars, pd_pars) |
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62 | |
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63 | class DirectModel: |
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64 | def __init__(self, name, q_vectors, dtype='single'): |
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65 | self.model_definition = load_model_definition(name) |
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66 | self.model = load_model(self.model_definition, dtype=dtype) |
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67 | q_vectors = [np.ascontiguousarray(q,dtype=dtype) for q in q_vectors] |
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68 | self.kernel = make_kernel(self.model, q_vectors) |
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69 | def __call__(self, pars): |
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70 | return call_kernel(self.kernel, pars) |
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71 | |
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72 | def demo(): |
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73 | import sys |
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74 | if len(sys.argv) < 3: |
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75 | print "usage: python -m sasmodels.direct_model modelname (q|qx,qy) par=val ..." |
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76 | sys.exit(1) |
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77 | model_name = sys.argv[1] |
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78 | values = [float(v) for v in sys.argv[2].split(',')] |
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79 | if len(values) == 1: |
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80 | q = values[0] |
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81 | q_vectors = [[q]] |
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82 | elif len(values) == 2: |
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83 | qx,qy = values |
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84 | q_vectors = [[qx],[qy]] |
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85 | else: |
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86 | print "use q or qx,qy" |
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87 | sys.exit(1) |
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88 | model = DirectModel(model_name, q_vectors) |
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89 | pars = dict((k,float(v)) |
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90 | for pair in sys.argv[3:] |
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91 | for k,v in [pair.split('=')]) |
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92 | Iq = model(pars) |
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93 | print Iq[0] |
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94 | |
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95 | if __name__ == "__main__": |
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96 | demo() |
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