[af03ddd] | 1 | """ |
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| 2 | This software was developed by the University of Tennessee as part of the |
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| 3 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 4 | project funded by the US National Science Foundation. |
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| 5 | |
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| 6 | If you use DANSE applications to do scientific research that leads to |
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| 7 | publication, we ask that you acknowledge the use of the software with the |
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| 8 | following sentence: |
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| 9 | |
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| 10 | "This work benefited from DANSE software developed under NSF award DMR-0520547." |
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| 11 | |
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| 12 | copyright 2008, University of Tennessee |
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| 13 | """ |
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| 14 | |
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| 15 | """ |
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| 16 | Class definitions for python dispersion model for |
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| 17 | model parameters. These classes are bridges to the C++ |
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| 18 | dispersion object. |
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| 19 | |
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| 20 | The ArrayDispersion class takes in numpy arrays only. |
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| 21 | |
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| 22 | Usage: |
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| 23 | These classes can be used to set the dispersion model of a SANS model |
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| 24 | parameter: |
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| 25 | |
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| 26 | cyl = CylinderModel() |
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| 27 | cyl.set_dispersion('radius', GaussianDispersion()) |
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| 28 | |
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| 29 | |
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| 30 | After the dispersion model is set, you can access it's |
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| 31 | parameter through the dispersion dictionary: |
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| 32 | |
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| 33 | cyl.dispersion['radius']['width'] = 5.0 |
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| 34 | |
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| 35 | TODO: For backward compatibility, the model parameters are still kept in |
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| 36 | a dictionary. The next iteration of refactoring work should involve moving |
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| 37 | away from value-based parameters to object-based parameter. We want to |
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| 38 | store parameters as objects so that we can unify the 'params' and 'dispersion' |
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| 39 | dictionaries into a single dictionary of parameter objects that hold the |
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| 40 | complete information about the parameter (units, limits, dispersion model, etc...). |
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| 41 | |
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| 42 | |
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| 43 | """ |
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| 44 | import sans_extension.c_models as c_models |
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| 45 | |
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[988130c6] | 46 | |
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| 47 | |
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[af03ddd] | 48 | class DispersionModel: |
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| 49 | """ |
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| 50 | Python bridge class for a basic dispersion model |
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| 51 | class with a constant parameter value distribution |
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| 52 | """ |
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| 53 | def __init__(self): |
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| 54 | self.cdisp = c_models.new_dispersion_model() |
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| 55 | |
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| 56 | def set_weights(self, values, weights): |
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| 57 | """ |
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| 58 | Set the weights of an array dispersion |
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| 59 | """ |
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| 60 | message = "set_weights is not available for DispersionModel.\n" |
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| 61 | message += " Solution: Use an ArrayDispersion object" |
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| 62 | raise "RuntimeError", message |
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| 63 | |
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| 64 | class GaussianDispersion(DispersionModel): |
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| 65 | """ |
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| 66 | Python bridge class for a dispersion model based |
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| 67 | on a Gaussian distribution. |
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| 68 | """ |
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| 69 | def __init__(self): |
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| 70 | self.cdisp = c_models.new_gaussian_model() |
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| 71 | |
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| 72 | def set_weights(self, values, weights): |
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| 73 | """ |
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| 74 | Set the weights of an array dispersion |
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| 75 | """ |
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| 76 | message = "set_weights is not available for GaussiantDispersion.\n" |
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| 77 | message += " Solution: Use an ArrayDispersion object" |
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| 78 | raise "RuntimeError", message |
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| 79 | |
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| 80 | class ArrayDispersion(DispersionModel): |
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| 81 | """ |
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| 82 | Python bridge class for a dispersion model based on arrays. |
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| 83 | The user has to set a weight distribution that |
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| 84 | will be used in the averaging the model parameter |
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| 85 | it is applied to. |
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| 86 | """ |
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| 87 | def __init__(self): |
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| 88 | self.cdisp = c_models.new_array_model() |
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| 89 | |
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| 90 | def set_weights(self, values, weights): |
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| 91 | """ |
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| 92 | Set the weights of an array dispersion |
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| 93 | Only accept numpy arrays. |
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| 94 | @param values: numpy array of values |
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| 95 | @param weights: numpy array of weights for each value entry |
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| 96 | """ |
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| 97 | if len(values) != len(weights): |
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| 98 | raise ValueError, "ArrayDispersion.set_weights: given arrays are of different lengths" |
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| 99 | |
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| 100 | c_models.set_dispersion_weights(self.cdisp, values, weights) |
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[988130c6] | 101 | |
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| 102 | models = {GaussianDispersion:"GaussianModel", ArrayDispersion:"MyModel"} |
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[af03ddd] | 103 | |
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