[ae3ce4e] | 1 | #!/usr/bin/env python |
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[79ac6f8] | 2 | |
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[ae60f86] | 3 | """ |
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[79ac6f8] | 4 | Provide base functionality for all model components |
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[ae3ce4e] | 5 | """ |
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| 6 | |
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| 7 | # imports |
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| 8 | import copy |
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[83a25da] | 9 | import numpy |
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[988130c6] | 10 | #TO DO: that about a way to make the parameter |
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| 11 | #is self return if it is fittable or not |
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[836fe6e] | 12 | |
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[ae3ce4e] | 13 | class BaseComponent: |
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[ae60f86] | 14 | """ |
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[79ac6f8] | 15 | Basic model component |
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| 16 | |
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| 17 | Since version 0.5.0, basic operations are no longer supported. |
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[ae3ce4e] | 18 | """ |
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| 19 | |
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| 20 | def __init__(self): |
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| 21 | """ Initialization""" |
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| 22 | |
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| 23 | ## Name of the model |
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[79ac6f8] | 24 | self.name = "BaseComponent" |
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[ae3ce4e] | 25 | |
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| 26 | ## Parameters to be accessed by client |
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| 27 | self.params = {} |
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[3db3895] | 28 | self.details = {} |
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[d30fdde] | 29 | ## Dictionary used to store the dispersity/averaging |
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| 30 | # parameters of dispersed/averaged parameters. |
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| 31 | self.dispersion = {} |
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[5f89fb8] | 32 | # string containing information about the model such as the equation |
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| 33 | #of the given model, exception or possible use |
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| 34 | self.description='' |
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[c9636f7] | 35 | #list of parameter that can be fitted |
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[35aface] | 36 | self.fixed = [] |
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| 37 | #list of non-fittable parameter |
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| 38 | self.non_fittable = [] |
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[25a608f5] | 39 | ## parameters with orientation |
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[35aface] | 40 | self.orientation_params = [] |
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[c3e4a7fa] | 41 | ## store dispersity reference |
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| 42 | self._persistency_dict = {} |
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[0145a25] | 43 | ## independent parameter name and unit [string] |
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| 44 | self.input_name = "Q" |
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| 45 | self.input_unit = "A^{-1}" |
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| 46 | ## output name and unit [string] |
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| 47 | self.output_name = "Intensity" |
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| 48 | self.output_unit = "cm^{-1}" |
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| 49 | |
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[ae3ce4e] | 50 | def __str__(self): |
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[ae60f86] | 51 | """ |
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[79ac6f8] | 52 | :return: string representatio |
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| 53 | |
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[ae3ce4e] | 54 | """ |
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| 55 | return self.name |
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| 56 | |
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[988130c6] | 57 | def is_fittable(self, par_name): |
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[c9636f7] | 58 | """ |
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[79ac6f8] | 59 | Check if a given parameter is fittable or not |
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| 60 | |
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| 61 | :param par_name: the parameter name to check |
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| 62 | |
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[c9636f7] | 63 | """ |
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| 64 | return par_name.lower() in self.fixed |
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[988130c6] | 65 | #For the future |
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[836fe6e] | 66 | #return self.params[str(par_name)].is_fittable() |
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[988130c6] | 67 | |
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[ae60f86] | 68 | def run(self, x): return NotImplemented |
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| 69 | def runXY(self, x): return NotImplemented |
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[f9bf661] | 70 | def calculate_ER(self): return NotImplemented |
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[e08bd5b] | 71 | def calculate_VR(self): return NotImplemented |
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[83a25da] | 72 | def evalDistribution(self, qdist): |
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| 73 | """ |
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[79ac6f8] | 74 | Evaluate a distribution of q-values. |
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| 75 | |
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| 76 | * For 1D, a numpy array is expected as input: |
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| 77 | |
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[2f1a0dc] | 78 | evalDistribution(q) |
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[ecc58e72] | 79 | |
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[79ac6f8] | 80 | where q is a numpy array. |
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| 81 | |
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| 82 | |
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| 83 | * For 2D, a list of numpy arrays are expected: [qx_prime,qy_prime], |
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| 84 | where 1D arrays, |
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| 85 | |
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| 86 | qx_prime = [ qx[0], qx[1], qx[2], ....] |
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| 87 | and |
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| 88 | qy_prime = [ qy[0], qy[1], qy[2], ....] |
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| 89 | |
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| 90 | Then get |
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| 91 | q = numpy.sqrt(qx_prime^2+qy_prime^2) |
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| 92 | |
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| 93 | that is a qr in 1D array; |
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| 94 | q = [q[0], q[1], q[2], ....] |
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| 95 | |
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| 96 | :Note: Due to 2D speed issue, no anisotropic scattering |
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| 97 | is supported for python models, thus C-models should have |
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| 98 | their own evalDistribution methods. |
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| 99 | |
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| 100 | The method is then called the following way: |
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| 101 | |
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| 102 | evalDistribution(q) |
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| 103 | where q is a numpy array. |
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| 104 | |
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| 105 | :param qdist: ndarray of scalar q-values or list [qx,qy] |
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| 106 | where qx,qy are 1D ndarrays |
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| 107 | |
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[83a25da] | 108 | """ |
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[ecc58e72] | 109 | if qdist.__class__.__name__ == 'list': |
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| 110 | # Check whether we have a list of ndarrays [qx,qy] |
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| 111 | if len(qdist)!=2 or \ |
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| 112 | qdist[0].__class__.__name__ != 'ndarray' or \ |
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| 113 | qdist[1].__class__.__name__ != 'ndarray': |
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| 114 | raise RuntimeError, "evalDistribution expects a list of 2 ndarrays" |
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| 115 | |
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| 116 | # Extract qx and qy for code clarity |
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| 117 | qx = qdist[0] |
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| 118 | qy = qdist[1] |
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| 119 | |
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[2f1a0dc] | 120 | # calculate q_r component for 2D isotropic |
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| 121 | q = numpy.sqrt(qx**2+qy**2) |
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| 122 | # vectorize the model function runXY |
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| 123 | v_model = numpy.vectorize(self.runXY,otypes=[float]) |
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| 124 | # calculate the scattering |
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| 125 | iq_array = v_model(q) |
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| 126 | |
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[ecc58e72] | 127 | return iq_array |
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| 128 | |
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| 129 | elif qdist.__class__.__name__ == 'ndarray': |
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| 130 | # We have a simple 1D distribution of q-values |
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[2f1a0dc] | 131 | v_model = numpy.vectorize(self.runXY,otypes=[float]) |
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| 132 | iq_array = v_model(qdist) |
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| 133 | |
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[ecc58e72] | 134 | return iq_array |
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[83a25da] | 135 | |
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[ecc58e72] | 136 | else: |
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| 137 | mesg = "evalDistribution is expecting an ndarray of scalar q-values" |
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| 138 | mesg += " or a list [qx,qy] where qx,qy are 2D ndarrays." |
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| 139 | raise RuntimeError, mesg |
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[2f1a0dc] | 140 | |
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| 141 | |
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[83a25da] | 142 | |
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[ae3ce4e] | 143 | def clone(self): |
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| 144 | """ Returns a new object identical to the current object """ |
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| 145 | obj = copy.deepcopy(self) |
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[8809e48] | 146 | return self._clone(obj) |
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| 147 | |
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| 148 | def _clone(self, obj): |
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| 149 | """ |
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[79ac6f8] | 150 | Internal utility function to copy the internal |
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| 151 | data members to a fresh copy. |
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[8809e48] | 152 | """ |
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[ae60f86] | 153 | obj.params = copy.deepcopy(self.params) |
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| 154 | obj.details = copy.deepcopy(self.details) |
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| 155 | obj.dispersion = copy.deepcopy(self.dispersion) |
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[138c139] | 156 | obj._persistency_dict = copy.deepcopy( self._persistency_dict) |
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[ae3ce4e] | 157 | return obj |
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[045be84d] | 158 | |
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[4ce74b44] | 159 | def set_dispersion(self, parameter, dispersion): |
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[045be84d] | 160 | """ |
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| 161 | model dispersions |
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| 162 | """ |
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[4ce74b44] | 163 | ##Not Implemented |
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| 164 | return None |
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[045be84d] | 165 | |
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| 166 | def getProfile(self): |
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| 167 | """ |
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| 168 | Get SLD profile |
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| 169 | |
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| 170 | : return: (z, beta) where z is a list of depth of the transition points |
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| 171 | beta is a list of the corresponding SLD values |
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| 172 | """ |
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| 173 | #Not Implemented |
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| 174 | return None, None |
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| 175 | |
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[ae3ce4e] | 176 | def setParam(self, name, value): |
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[ae60f86] | 177 | """ |
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[79ac6f8] | 178 | Set the value of a model parameter |
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| 179 | |
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| 180 | :param name: name of the parameter |
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| 181 | :param value: value of the parameter |
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[ae3ce4e] | 182 | |
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| 183 | """ |
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[ae60f86] | 184 | # Look for dispersion parameters |
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| 185 | toks = name.split('.') |
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| 186 | if len(toks)==2: |
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| 187 | for item in self.dispersion.keys(): |
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| 188 | if item.lower()==toks[0].lower(): |
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| 189 | for par in self.dispersion[item]: |
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| 190 | if par.lower() == toks[1].lower(): |
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| 191 | self.dispersion[item][par] = value |
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| 192 | return |
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| 193 | else: |
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| 194 | # Look for standard parameter |
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| 195 | for item in self.params.keys(): |
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| 196 | if item.lower()==name.lower(): |
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| 197 | self.params[item] = value |
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| 198 | return |
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| 199 | |
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| 200 | raise ValueError, "Model does not contain parameter %s" % name |
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[ae3ce4e] | 201 | |
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[ae60f86] | 202 | def getParam(self, name): |
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| 203 | """ |
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[79ac6f8] | 204 | Set the value of a model parameter |
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[ae60f86] | 205 | |
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[79ac6f8] | 206 | :param name: name of the parameter |
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| 207 | |
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[ae3ce4e] | 208 | """ |
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[ae60f86] | 209 | # Look for dispersion parameters |
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[ae3ce4e] | 210 | toks = name.split('.') |
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[ae60f86] | 211 | if len(toks)==2: |
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| 212 | for item in self.dispersion.keys(): |
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| 213 | if item.lower()==toks[0].lower(): |
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| 214 | for par in self.dispersion[item]: |
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| 215 | if par.lower() == toks[1].lower(): |
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| 216 | return self.dispersion[item][par] |
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| 217 | else: |
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| 218 | # Look for standard parameter |
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| 219 | for item in self.params.keys(): |
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| 220 | if item.lower()==name.lower(): |
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| 221 | return self.params[item] |
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| 222 | |
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| 223 | raise ValueError, "Model does not contain parameter %s" % name |
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| 224 | |
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[ae3ce4e] | 225 | def getParamList(self): |
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| 226 | """ |
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[79ac6f8] | 227 | Return a list of all available parameters for the model |
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[ae60f86] | 228 | """ |
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| 229 | list = self.params.keys() |
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| 230 | # WARNING: Extending the list with the dispersion parameters |
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| 231 | list.extend(self.getDispParamList()) |
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| 232 | return list |
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| 233 | |
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| 234 | def getDispParamList(self): |
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| 235 | """ |
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[79ac6f8] | 236 | Return a list of all available parameters for the model |
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[ae60f86] | 237 | """ |
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| 238 | list = [] |
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| 239 | |
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| 240 | for item in self.dispersion.keys(): |
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| 241 | for p in self.dispersion[item].keys(): |
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| 242 | if p not in ['type']: |
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| 243 | list.append('%s.%s' % (item.lower(), p.lower())) |
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| 244 | |
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| 245 | return list |
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| 246 | |
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| 247 | # Old-style methods that are no longer used |
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| 248 | def setParamWithToken(self, name, value, token, member): return NotImplemented |
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| 249 | def getParamWithToken(self, name, token, member): return NotImplemented |
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| 250 | def getParamListWithToken(self, token, member): return NotImplemented |
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| 251 | def __add__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 252 | def __sub__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 253 | def __mul__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 254 | def __div__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 255 | |
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