| 1 | """ |
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| 2 | Inferface containing information to store data, model, range of data, etc... |
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| 3 | and retrieve this information. This is an inferface |
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| 4 | for a fitProblem i.e relationship between data and model. |
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| 5 | """ |
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| 6 | ################################################################################ |
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| 7 | #This software was developed by the University of Tennessee as part of the |
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| 8 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 9 | #project funded by the US National Science Foundation. |
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| 10 | # |
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| 11 | #See the license text in license.txt |
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| 12 | # |
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| 13 | #copyright 2009, University of Tennessee |
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| 14 | ################################################################################ |
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| 15 | import copy |
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| 16 | |
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| 17 | from sas.sascalc.fit.qsmearing import smear_selection |
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| 18 | |
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| 19 | class FitProblem(object): |
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| 20 | """ |
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| 21 | Define the relationship between data and model, including range, weights, |
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| 22 | etc. |
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| 23 | """ |
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| 24 | def __init__(self): |
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| 25 | """ |
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| 26 | contains information about data and model to fit |
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| 27 | """ |
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| 28 | ## data used for fitting |
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| 29 | self.fit_data = None |
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| 30 | self.theory_data = None |
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| 31 | self.residuals = None |
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| 32 | # original data: should not be modified |
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| 33 | self.original_data = None |
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| 34 | ## the current model |
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| 35 | self.model = None |
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| 36 | ## if 1 this fit problem will be selected to fit , if 0 |
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| 37 | ## it will not be selected for fit |
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| 38 | self.schedule = 0 |
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| 39 | ##list containing parameter name and value |
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| 40 | self.list_param = [] |
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| 41 | self.list_param2fit = [] |
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| 42 | ## smear object to smear or not data1D |
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| 43 | self.smearer_computed = False |
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| 44 | self.smearer_enable = False |
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| 45 | self.smearer_computer_value = None |
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| 46 | ## fitting range |
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| 47 | self.qmin = None |
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| 48 | self.qmax = None |
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| 49 | # fit weight |
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| 50 | self.weight = None |
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| 51 | self.result = None |
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| 52 | self.fit_tab_caption = None |
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| 53 | self.name_per_page = None |
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| 54 | |
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| 55 | def enable_smearing(self, flag=False): |
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| 56 | """ |
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| 57 | :param flag: bool.When flag is 1 get the computer smear value. When |
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| 58 | flag is 0 ignore smear value. |
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| 59 | """ |
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| 60 | self.smearer_enable = flag |
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| 61 | |
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| 62 | def set_smearer(self, smearer): |
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| 63 | """ |
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| 64 | save reference of smear object on fitdata |
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| 65 | |
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| 66 | :param smear: smear object from DataLoader |
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| 67 | |
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| 68 | """ |
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| 69 | self.smearer_computer_value = smearer |
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| 70 | |
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| 71 | def get_smearer(self): |
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| 72 | """ |
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| 73 | return smear object |
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| 74 | """ |
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| 75 | if not self.smearer_enable: |
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| 76 | return None |
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| 77 | if not self.smearer_computed: |
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| 78 | #smeari_selection should be call only once per fitproblem |
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| 79 | self.smearer_computer_value = smear_selection(self.fit_data, |
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| 80 | self.model) |
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| 81 | self.smearer_computed = True |
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| 82 | return self.smearer_computer_value |
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| 83 | |
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| 84 | def save_model_name(self, name): |
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| 85 | """ |
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| 86 | """ |
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| 87 | self.name_per_page = name |
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| 88 | |
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| 89 | def get_name(self): |
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| 90 | """ |
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| 91 | """ |
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| 92 | return self.name_per_page |
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| 93 | |
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| 94 | def set_model(self, model): |
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| 95 | """ |
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| 96 | associates each model with its new created name |
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| 97 | :param model: model selected |
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| 98 | :param name: name created for model |
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| 99 | """ |
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| 100 | self.model = model |
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| 101 | self.smearer_computer_value = smear_selection(self.fit_data, |
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| 102 | self.model) |
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| 103 | self.smearer_computed = True |
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| 104 | |
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| 105 | def get_model(self): |
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| 106 | """ |
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| 107 | :return: saved model |
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| 108 | """ |
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| 109 | return self.model |
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| 110 | |
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| 111 | def set_residuals(self, residuals): |
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| 112 | """ |
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| 113 | save a copy of residual |
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| 114 | :param data: data selected |
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| 115 | """ |
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| 116 | self.residuals = residuals |
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| 117 | |
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| 118 | def get_residuals(self): |
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| 119 | """ |
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| 120 | :return: residuals |
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| 121 | """ |
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| 122 | return self.residuals |
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| 123 | |
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| 124 | def set_theory_data(self, data): |
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| 125 | """ |
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| 126 | save a copy of the data select to fit |
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| 127 | |
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| 128 | :param data: data selected |
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| 129 | |
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| 130 | """ |
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| 131 | self.theory_data = copy.deepcopy(data) |
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| 132 | |
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| 133 | def get_theory_data(self): |
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| 134 | """ |
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| 135 | :return: theory generated with the current model and data of this class |
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| 136 | """ |
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| 137 | return self.theory_data |
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| 138 | |
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| 139 | def set_fit_data(self, data): |
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| 140 | """ |
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| 141 | Store data associated with this class |
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| 142 | :param data: list of data selected |
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| 143 | """ |
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| 144 | self.original_data = None |
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| 145 | self.fit_data = None |
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| 146 | # original data: should not be modified |
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| 147 | self.original_data = data |
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| 148 | # fit data: used for fit and can be modified for convenience |
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| 149 | self.fit_data = copy.deepcopy(data) |
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| 150 | self.smearer_computer_value = smear_selection(self.fit_data, self.model) |
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| 151 | self.smearer_computed = True |
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| 152 | self.result = None |
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| 153 | |
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| 154 | def get_fit_data(self): |
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| 155 | """ |
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| 156 | :return: data associate with this class |
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| 157 | """ |
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| 158 | return self.fit_data |
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| 159 | |
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| 160 | def get_origin_data(self): |
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| 161 | """ |
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| 162 | """ |
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| 163 | return self.original_data |
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| 164 | |
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| 165 | def set_weight(self, is2d, flag=None): |
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| 166 | """ |
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| 167 | Received flag and compute error on data. |
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| 168 | :param flag: flag to transform error of data. |
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| 169 | :param is2d: flag to distinguish 1D to 2D Data |
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| 170 | """ |
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| 171 | from sas.sasgui.perspectives.fitting.utils import get_weight |
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| 172 | # send original data for weighting |
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| 173 | self.weight = get_weight(data=self.original_data, is2d=is2d, flag=flag) |
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| 174 | if is2d: |
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| 175 | self.fit_data.err_data = self.weight |
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| 176 | else: |
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| 177 | self.fit_data.dy = self.weight |
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| 178 | |
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| 179 | def get_weight(self): |
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| 180 | """ |
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| 181 | returns weight array |
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| 182 | """ |
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| 183 | return self.weight |
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| 184 | |
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| 185 | def set_param2fit(self, list): |
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| 186 | """ |
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| 187 | Store param names to fit (checked) |
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| 188 | :param list: list of the param names |
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| 189 | """ |
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| 190 | self.list_param2fit = list |
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| 191 | |
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| 192 | def get_param2fit(self): |
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| 193 | """ |
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| 194 | return the list param names to fit |
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| 195 | """ |
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| 196 | return self.list_param2fit |
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| 197 | |
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| 198 | def set_model_param(self, name, value=None): |
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| 199 | """ |
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| 200 | Store the name and value of a parameter of this fitproblem's model |
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| 201 | :param name: name of the given parameter |
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| 202 | :param value: value of that parameter |
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| 203 | """ |
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| 204 | self.list_param.append([name, value]) |
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| 205 | |
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| 206 | def get_model_param(self): |
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| 207 | """ |
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| 208 | return list of couple of parameter name and value |
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| 209 | """ |
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| 210 | return self.list_param |
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| 211 | |
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| 212 | def schedule_tofit(self, schedule=0): |
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| 213 | """ |
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| 214 | set schedule to true to decide if this fit must be performed |
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| 215 | """ |
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| 216 | self.schedule = schedule |
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| 217 | |
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| 218 | def get_scheduled(self): |
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| 219 | """ |
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| 220 | return true or false if a problem as being schedule for fitting |
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| 221 | """ |
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| 222 | return self.schedule |
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| 223 | |
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| 224 | def set_range(self, qmin=None, qmax=None): |
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| 225 | """ |
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| 226 | set fitting range |
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| 227 | :param qmin: minimum value to consider for the fit range |
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| 228 | :param qmax: maximum value to consider for the fit range |
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| 229 | """ |
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| 230 | self.qmin = qmin |
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| 231 | self.qmax = qmax |
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| 232 | |
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| 233 | def get_range(self): |
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| 234 | """ |
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| 235 | :return: fitting range |
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| 236 | |
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| 237 | """ |
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| 238 | return self.qmin, self.qmax |
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| 239 | |
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| 240 | def clear_model_param(self): |
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| 241 | """ |
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| 242 | clear constraint info |
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| 243 | """ |
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| 244 | self.list_param = [] |
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| 245 | |
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| 246 | def set_fit_tab_caption(self, caption): |
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| 247 | """ |
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| 248 | """ |
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| 249 | self.fit_tab_caption = str(caption) |
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| 250 | |
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| 251 | def get_fit_tab_caption(self): |
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| 252 | """ |
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| 253 | """ |
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| 254 | return self.fit_tab_caption |
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| 255 | |
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| 256 | def set_graph_id(self, id): |
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| 257 | """ |
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| 258 | Set graph id (from data_group_id at the time the graph produced) |
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| 259 | """ |
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| 260 | self.graph_id = id |
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| 261 | |
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| 262 | def get_graph_id(self): |
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| 263 | """ |
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| 264 | Get graph_id |
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| 265 | """ |
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| 266 | return self.graph_id |
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| 267 | |
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| 268 | def set_result(self, result): |
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| 269 | """ |
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| 270 | """ |
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| 271 | self.result = result |
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| 272 | |
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| 273 | def get_result(self): |
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| 274 | """ |
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| 275 | get result |
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| 276 | """ |
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| 277 | return self.result |
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| 278 | |
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| 279 | |
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| 280 | class FitProblemDictionary(dict): |
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| 281 | """ |
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| 282 | This module implements a dictionary of fitproblem objects |
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| 283 | """ |
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| 284 | def __init__(self): |
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| 285 | dict.__init__(self) |
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| 286 | ## the current model |
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| 287 | self.model = None |
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| 288 | ## if 1 this fit problem will be selected to fit , if 0 |
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| 289 | ## it will not be selected for fit |
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| 290 | self.schedule = 0 |
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| 291 | ##list containing parameter name and value |
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| 292 | self.list_param = [] |
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| 293 | ## fitting range |
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| 294 | self.qmin = None |
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| 295 | self.qmax = None |
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| 296 | self.graph_id = None |
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| 297 | self._smear_on = False |
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| 298 | self.scheduled = 0 |
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| 299 | self.fit_tab_caption = '' |
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| 300 | self.nbr_residuals_computed = 0 |
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| 301 | self.batch_inputs = {} |
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| 302 | self.batch_outputs = {} |
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| 303 | |
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| 304 | def enable_smearing(self, flag=False, fid=None): |
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| 305 | """ |
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| 306 | :param flag: bool.When flag is 1 get the computer smear value. When |
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| 307 | flag is 0 ignore smear value. |
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| 308 | """ |
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| 309 | self._smear_on = flag |
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| 310 | if fid is None: |
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| 311 | for value in self.values(): |
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| 312 | value.enable_smearing(flag) |
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| 313 | elif fid in self: |
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| 314 | self[fid].enable_smearing(flag) |
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| 315 | |
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| 316 | def set_smearer(self, smearer, fid=None): |
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| 317 | """ |
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| 318 | save reference of smear object on fitdata |
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| 319 | :param smear: smear object from DataLoader |
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| 320 | """ |
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| 321 | if fid is None: |
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| 322 | for value in self.values(): |
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| 323 | value.set_smearer(smearer) |
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| 324 | elif fid in self: |
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| 325 | self[fid].set_smearer(smearer) |
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| 326 | |
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| 327 | def get_smearer(self, fid=None): |
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| 328 | """ |
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| 329 | return smear object |
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| 330 | """ |
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| 331 | if fid in self: |
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| 332 | return self[fid].get_smearer() |
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| 333 | |
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| 334 | def save_model_name(self, name, fid=None): |
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| 335 | """ |
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| 336 | """ |
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| 337 | if fid is None: |
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| 338 | for value in self.values(): |
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| 339 | value.save_model_name(name) |
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| 340 | elif fid in self: |
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| 341 | self[fid].save_model_name(name) |
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| 342 | |
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| 343 | def get_name(self, fid=None): |
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| 344 | """ |
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| 345 | """ |
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| 346 | result = [] |
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| 347 | if fid is None: |
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| 348 | for value in self.values(): |
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| 349 | result.append(value.get_name()) |
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| 350 | elif fid in self: |
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| 351 | result.append(self[fid].get_name()) |
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| 352 | return result |
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| 353 | |
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| 354 | def set_model(self, model, fid=None): |
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| 355 | """ |
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| 356 | associates each model with its new created name |
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| 357 | :param model: model selected |
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| 358 | :param name: name created for model |
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| 359 | """ |
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| 360 | self.model = model |
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| 361 | if fid is None: |
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| 362 | for value in self.values(): |
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| 363 | value.set_model(self.model) |
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| 364 | elif fid in self: |
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| 365 | self[fid].set_model(self.model) |
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| 366 | |
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| 367 | def get_model(self, fid): |
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| 368 | """ |
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| 369 | :return: saved model |
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| 370 | """ |
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| 371 | if fid in self: |
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| 372 | return self[fid].get_model() |
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| 373 | |
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| 374 | def set_fit_tab_caption(self, caption): |
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| 375 | """ |
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| 376 | store the caption of the page associated with object |
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| 377 | """ |
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| 378 | self.fit_tab_caption = caption |
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| 379 | |
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| 380 | def get_fit_tab_caption(self): |
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| 381 | """ |
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| 382 | Return the caption of the page associated with object |
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| 383 | """ |
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| 384 | return self.fit_tab_caption |
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| 385 | |
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| 386 | def set_residuals(self, residuals, fid): |
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| 387 | """ |
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| 388 | save a copy of residual |
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| 389 | :param data: data selected |
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| 390 | """ |
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| 391 | if fid in self: |
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| 392 | self[fid].set_residuals(residuals) |
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| 393 | |
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| 394 | def get_residuals(self, fid): |
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| 395 | """ |
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| 396 | :return: residuals |
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| 397 | """ |
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| 398 | if fid in self: |
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| 399 | return self[fid].get_residuals() |
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| 400 | |
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| 401 | def set_theory_data(self, fid, data=None): |
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| 402 | """ |
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| 403 | save a copy of the data select to fit |
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| 404 | :param data: data selected |
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| 405 | """ |
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| 406 | if fid in self: |
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| 407 | self[fid].set_theory_data(data) |
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| 408 | |
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| 409 | def get_theory_data(self, fid): |
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| 410 | """ |
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| 411 | :return: list of data dList |
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| 412 | """ |
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| 413 | if fid in self: |
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| 414 | return self[fid].get_theory_data() |
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| 415 | |
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| 416 | def add_data(self, data): |
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| 417 | """ |
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| 418 | Add data to the current dictionary of fitproblem. if data id does not |
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| 419 | exist create a new fit problem. |
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| 420 | :note: only data changes in the fit problem |
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| 421 | """ |
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| 422 | if data.id not in self: |
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| 423 | self[data.id] = FitProblem() |
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| 424 | self[data.id].set_fit_data(data) |
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| 425 | |
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| 426 | def set_fit_data(self, data): |
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| 427 | """ |
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| 428 | save a copy of the data select to fit |
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| 429 | :param data: data selected |
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| 430 | |
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| 431 | """ |
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| 432 | self.clear() |
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| 433 | if data is None: |
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| 434 | data = [] |
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| 435 | for d in data: |
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| 436 | if d is not None: |
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| 437 | if d.id not in self: |
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| 438 | self[d.id] = FitProblem() |
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| 439 | self[d.id].set_fit_data(d) |
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| 440 | self[d.id].set_model(self.model) |
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| 441 | self[d.id].set_range(self.qmin, self.qmax) |
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| 442 | |
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| 443 | def get_fit_data(self, fid): |
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| 444 | """ |
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| 445 | return data for the given fitproblem id |
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| 446 | :param fid: key representing a fitproblem, usually extract from data id |
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| 447 | """ |
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| 448 | if fid in self: |
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| 449 | return self[fid].get_fit_data() |
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| 450 | |
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| 451 | def set_model_param(self, name, value=None, fid=None): |
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| 452 | """ |
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| 453 | Store the name and value of a parameter of this fitproblem's model |
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| 454 | :param name: name of the given parameter |
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| 455 | :param value: value of that parameter |
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| 456 | """ |
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| 457 | if fid is None: |
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| 458 | for value in self.values(): |
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| 459 | value.set_model_param(name, value) |
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| 460 | elif fid in self: |
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| 461 | self[fid].set_model_param(name, value) |
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| 462 | |
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| 463 | def get_model_param(self, fid): |
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| 464 | """ |
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| 465 | return list of couple of parameter name and value |
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| 466 | """ |
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| 467 | if fid in self: |
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| 468 | return self[fid].get_model_param() |
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| 469 | |
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| 470 | def set_param2fit(self, list): |
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| 471 | """ |
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| 472 | Store param names to fit (checked) |
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| 473 | :param list: list of the param names |
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| 474 | """ |
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| 475 | self.list_param2fit = list |
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| 476 | |
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| 477 | def get_param2fit(self): |
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| 478 | """ |
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| 479 | return the list param names to fit |
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| 480 | """ |
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| 481 | return self.list_param2fit |
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| 482 | |
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| 483 | def schedule_tofit(self, schedule=0): |
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| 484 | """ |
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| 485 | set schedule to true to decide if this fit must be performed |
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| 486 | """ |
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| 487 | self.scheduled = schedule |
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| 488 | for value in self.values(): |
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| 489 | value.schedule_tofit(schedule) |
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| 490 | |
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| 491 | def get_scheduled(self): |
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| 492 | """ |
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| 493 | return true or false if a problem as being schedule for fitting |
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| 494 | """ |
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| 495 | return self.scheduled |
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| 496 | |
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| 497 | def set_range(self, qmin=None, qmax=None, fid=None): |
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| 498 | """ |
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| 499 | set fitting range |
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| 500 | """ |
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| 501 | self.qmin = qmin |
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| 502 | self.qmax = qmax |
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| 503 | if fid is None: |
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| 504 | for value in self.values(): |
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| 505 | value.set_range(self.qmin, self.qmax) |
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| 506 | elif fid in self: |
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| 507 | self[fid].value.set_range(self.qmin, self.qmax) |
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| 508 | |
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| 509 | def get_range(self, fid): |
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| 510 | """ |
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| 511 | :return: fitting range |
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| 512 | """ |
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| 513 | if fid in self: |
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| 514 | return self[fid].get_range() |
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| 515 | |
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| 516 | def set_weight(self, is2d, flag=None, fid=None): |
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| 517 | """ |
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| 518 | fit weight |
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| 519 | """ |
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| 520 | if fid is None: |
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| 521 | for value in self.values(): |
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| 522 | value.set_weight(flag=flag, is2d=is2d) |
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| 523 | elif fid in self: |
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| 524 | self[fid].set_weight(flag=flag, is2d=is2d) |
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| 525 | |
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| 526 | def get_weight(self, fid=None): |
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| 527 | """ |
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| 528 | return fit weight |
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| 529 | """ |
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| 530 | if fid in self: |
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| 531 | return self[fid].get_weight() |
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| 532 | |
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| 533 | def clear_model_param(self, fid=None): |
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| 534 | """ |
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| 535 | clear constraint info |
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| 536 | """ |
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| 537 | if fid is None: |
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| 538 | for value in self.values(): |
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| 539 | value.clear_model_param() |
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| 540 | elif fid in self: |
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| 541 | self[fid].clear_model_param() |
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| 542 | |
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| 543 | def get_fit_problem(self): |
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| 544 | """ |
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| 545 | return fitproblem contained in this dictionary |
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| 546 | """ |
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| 547 | return list(self.values()) |
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| 548 | |
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| 549 | def set_result(self, result, fid): |
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| 550 | """ |
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| 551 | """ |
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| 552 | if fid in self: |
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| 553 | self[fid].set_result(result) |
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| 554 | |
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| 555 | def set_batch_result(self, batch_inputs, batch_outputs): |
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| 556 | """ |
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| 557 | set a list of result |
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| 558 | """ |
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| 559 | self.batch_inputs = batch_inputs |
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| 560 | self.batch_outputs = batch_outputs |
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| 561 | |
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| 562 | def get_result(self, fid): |
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| 563 | """ |
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| 564 | get result |
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| 565 | """ |
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| 566 | if fid in self: |
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| 567 | return self[fid].get_result() |
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| 568 | |
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| 569 | def get_batch_result(self): |
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| 570 | """ |
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| 571 | get result |
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| 572 | """ |
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| 573 | return self.batch_inputs, self.batch_outputs |
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| 574 | |
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| 575 | def set_graph_id(self, id): |
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| 576 | """ |
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| 577 | Set graph id (from data_group_id at the time the graph produced) |
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| 578 | """ |
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| 579 | self.graph_id = id |
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| 580 | |
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| 581 | def get_graph_id(self): |
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| 582 | """ |
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| 583 | Get graph_id |
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| 584 | """ |
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| 585 | return self.graph_id |
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