1 | __all__ = ["list_models", "load_model_cl", "load_model_dll", |
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2 | "load_model_definition", ] |
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3 | |
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4 | from os.path import basename, dirname, join as joinpath |
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5 | from glob import glob |
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6 | |
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7 | import numpy as np |
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8 | |
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9 | from . import models |
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10 | from . import weights |
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11 | |
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12 | try: |
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13 | from .kernelcl import load_model as load_model_cl |
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14 | except Exception,exc: |
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15 | load_model_cl = None |
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16 | from .kerneldll import load_model as load_model_dll |
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17 | |
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18 | def list_models(): |
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19 | root = dirname(__file__) |
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20 | files = sorted(glob(joinpath(root, 'models', "[a-zA-Z]*.py"))) |
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21 | available_models = [basename(f)[:-3] for f in files] |
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22 | return available_models |
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23 | |
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24 | def load_model_definition(model_name): |
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25 | __import__('sasmodels.models.'+model_name) |
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26 | model_definition = getattr(models, model_name, None) |
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27 | return model_definition |
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28 | |
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29 | def make_kernel(model, q_vectors): |
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30 | """ |
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31 | Return a computation kernel from the model definition and the q input. |
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32 | """ |
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33 | input = model.make_input(q_vectors) |
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34 | return model(input) |
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35 | |
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36 | def get_weights(kernel, pars, name): |
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37 | """ |
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38 | Generate the distribution for parameter *name* given the parameter values |
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39 | in *pars*. |
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40 | |
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41 | Searches for "name", "name_pd", "name_pd_type", "name_pd_n", "name_pd_sigma" |
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42 | """ |
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43 | relative = name in kernel.info['partype']['pd-rel'] |
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44 | limits = kernel.info['limits'] |
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45 | disperser = pars.get(name+'_pd_type', 'gaussian') |
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46 | value = pars.get(name, kernel.info['defaults'][name]) |
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47 | npts = pars.get(name+'_pd_n', 0) |
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48 | width = pars.get(name+'_pd', 0.0) |
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49 | nsigma = pars.get(name+'_pd_nsigma', 3.0) |
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50 | v,w = weights.get_weights( |
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51 | disperser, npts, width, nsigma, |
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52 | value, limits[name], relative) |
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53 | return v,w/np.sum(w) |
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54 | |
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55 | def dispersion_mesh(pars): |
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56 | """ |
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57 | Create a mesh grid of dispersion parameters and weights. |
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58 | |
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59 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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60 | and w is a vector containing the products for weights for each |
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61 | parameter set in the vector. |
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62 | """ |
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63 | values, weights = zip(*pars) |
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64 | if len(values) > 1: |
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65 | values = [v.flatten() for v in np.meshgrid(*values)] |
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66 | weights = np.vstack([v.flatten() for v in np.meshgrid(*weights)]) |
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67 | weights = np.prod(weights, axis=0) |
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68 | return values, weights |
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69 | |
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70 | def call_kernel(kernel, pars, cutoff=1e-5): |
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71 | fixed_pars = [pars.get(name, kernel.info['defaults'][name]) |
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72 | for name in kernel.fixed_pars] |
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73 | pd_pars = [get_weights(kernel, pars, name) for name in kernel.pd_pars] |
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74 | return kernel(fixed_pars, pd_pars, cutoff=cutoff) |
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75 | |
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76 | def call_ER(kernel, pars): |
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77 | ER = kernel.info.get('ER', None) |
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78 | if ER is None: |
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79 | return 1.0 |
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80 | else: |
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81 | vol_pars = [get_weights(kernel, pars, name) |
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82 | for name in kernel.info['partype']['volume']] |
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83 | values, weights = dispersion_mesh(vol_pars) |
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84 | fv = ER(*values) |
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85 | #print values[0].shape, weights.shape, fv.shape |
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86 | return np.sum(weights*fv) / np.sum(weights) |
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87 | |
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88 | def call_VR(kernel, pars): |
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89 | VR = kernel.info.get('VR', None) |
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90 | if VR is None: |
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91 | return 1.0 |
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92 | else: |
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93 | vol_pars = [get_weights(kernel, pars, name) |
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94 | for name in kernel.info['partype']['volume']] |
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95 | values, weights = dispersion_mesh(vol_pars) |
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96 | whole,part = VR(*values) |
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97 | return np.sum(weights*part)/np.sum(weights*whole) |
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98 | |
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