[ae3ce4e] | 1 | #!/usr/bin/env python |
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[ae60f86] | 2 | """ |
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| 3 | Provide base functionality for all model components |
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[ae3ce4e] | 4 | """ |
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| 5 | |
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| 6 | # imports |
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| 7 | import copy |
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[83a25da] | 8 | import numpy |
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[988130c6] | 9 | #TO DO: that about a way to make the parameter |
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| 10 | #is self return if it is fittable or not |
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[836fe6e] | 11 | |
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[ae3ce4e] | 12 | class BaseComponent: |
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[ae60f86] | 13 | """ |
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| 14 | Basic model component |
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[ae3ce4e] | 15 | |
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[ae60f86] | 16 | Since version 0.5.0, basic operations are no longer supported. |
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[ae3ce4e] | 17 | """ |
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| 18 | |
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| 19 | def __init__(self): |
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| 20 | """ Initialization""" |
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| 21 | |
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| 22 | ## Name of the model |
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| 23 | self.name = "BaseComponent" |
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| 24 | |
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| 25 | ## Parameters to be accessed by client |
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| 26 | self.params = {} |
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[3db3895] | 27 | self.details = {} |
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[d30fdde] | 28 | ## Dictionary used to store the dispersity/averaging |
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| 29 | # parameters of dispersed/averaged parameters. |
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| 30 | self.dispersion = {} |
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[5f89fb8] | 31 | # string containing information about the model such as the equation |
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| 32 | #of the given model, exception or possible use |
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| 33 | self.description='' |
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[c9636f7] | 34 | #list of parameter that can be fitted |
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[988130c6] | 35 | self.fixed= [] |
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[25a608f5] | 36 | ## parameters with orientation |
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| 37 | self.orientation_params =[] |
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[c3e4a7fa] | 38 | ## store dispersity reference |
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| 39 | self._persistency_dict = {} |
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[ae3ce4e] | 40 | |
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| 41 | def __str__(self): |
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[ae60f86] | 42 | """ |
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[ae3ce4e] | 43 | @return: string representation |
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| 44 | """ |
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| 45 | return self.name |
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| 46 | |
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[988130c6] | 47 | def is_fittable(self, par_name): |
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[c9636f7] | 48 | """ |
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| 49 | Check if a given parameter is fittable or not |
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| 50 | @param par_name: the parameter name to check |
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| 51 | """ |
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| 52 | return par_name.lower() in self.fixed |
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[988130c6] | 53 | #For the future |
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[836fe6e] | 54 | #return self.params[str(par_name)].is_fittable() |
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[988130c6] | 55 | |
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[ae60f86] | 56 | def run(self, x): return NotImplemented |
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| 57 | def runXY(self, x): return NotImplemented |
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[f9bf661] | 58 | def calculate_ER(self): return NotImplemented |
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[83a25da] | 59 | def evalDistribution(self, qdist): |
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| 60 | """ |
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| 61 | Evaluate a distribution of q-values. |
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| 62 | |
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[ecc58e72] | 63 | * For 1D, a numpy array is expected as input: |
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[83a25da] | 64 | |
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[ecc58e72] | 65 | evalDistribution(q) |
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| 66 | |
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| 67 | where q is a numpy array. |
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| 68 | |
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| 69 | |
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[3080527] | 70 | * For 2D, a list of numpy arrays are expected: [qx_prime,qy_prime], |
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| 71 | where 1D arrays, |
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[ecc58e72] | 72 | |
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[3080527] | 73 | qx_prime = [ qx[0], qx[1], qx[2], ....] |
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| 74 | and |
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| 75 | qy_prime = [ qy[0], qy[1], qy[2], ....] |
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[ecc58e72] | 76 | |
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| 77 | The method is then called the following way: |
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| 78 | |
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| 79 | evalDistribution([qx_prime, qy_prime]) |
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| 80 | |
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[3080527] | 81 | @param qdist: ndarray of scalar q-values or list [qx,qy] where qx,qy are 1D ndarrays |
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[83a25da] | 82 | """ |
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[ecc58e72] | 83 | if qdist.__class__.__name__ == 'list': |
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| 84 | # Check whether we have a list of ndarrays [qx,qy] |
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| 85 | if len(qdist)!=2 or \ |
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| 86 | qdist[0].__class__.__name__ != 'ndarray' or \ |
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| 87 | qdist[1].__class__.__name__ != 'ndarray': |
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| 88 | raise RuntimeError, "evalDistribution expects a list of 2 ndarrays" |
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| 89 | |
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| 90 | # Extract qx and qy for code clarity |
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| 91 | qx = qdist[0] |
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| 92 | qy = qdist[1] |
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| 93 | |
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| 94 | # Create output array |
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[3080527] | 95 | iq_array = numpy.zeros((len(qx))) |
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| 96 | |
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| 97 | for i in range(len(qx)): |
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| 98 | iq_array[i] = self.runXY([qx[i],qy[i]]) |
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[ecc58e72] | 99 | return iq_array |
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| 100 | |
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| 101 | elif qdist.__class__.__name__ == 'ndarray': |
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| 102 | # We have a simple 1D distribution of q-values |
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| 103 | iq_array = numpy.zeros(len(qdist)) |
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| 104 | for i in range(len(qdist)): |
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| 105 | iq_array[i] = self.runXY(qdist[i]) |
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| 106 | return iq_array |
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[83a25da] | 107 | |
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[ecc58e72] | 108 | else: |
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| 109 | mesg = "evalDistribution is expecting an ndarray of scalar q-values" |
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| 110 | mesg += " or a list [qx,qy] where qx,qy are 2D ndarrays." |
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| 111 | raise RuntimeError, mesg |
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[83a25da] | 112 | |
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[ae3ce4e] | 113 | def clone(self): |
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| 114 | """ Returns a new object identical to the current object """ |
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| 115 | obj = copy.deepcopy(self) |
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[8809e48] | 116 | return self._clone(obj) |
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| 117 | |
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| 118 | def _clone(self, obj): |
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| 119 | """ |
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| 120 | Internal utility function to copy the internal |
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| 121 | data members to a fresh copy. |
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| 122 | """ |
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[ae60f86] | 123 | obj.params = copy.deepcopy(self.params) |
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| 124 | obj.details = copy.deepcopy(self.details) |
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| 125 | obj.dispersion = copy.deepcopy(self.dispersion) |
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[138c139] | 126 | obj._persistency_dict = copy.deepcopy( self._persistency_dict) |
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[ae3ce4e] | 127 | return obj |
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| 128 | |
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| 129 | def setParam(self, name, value): |
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[ae60f86] | 130 | """ |
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| 131 | Set the value of a model parameter |
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[ae3ce4e] | 132 | |
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| 133 | @param name: name of the parameter |
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| 134 | @param value: value of the parameter |
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| 135 | """ |
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[ae60f86] | 136 | # Look for dispersion parameters |
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| 137 | toks = name.split('.') |
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| 138 | if len(toks)==2: |
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| 139 | for item in self.dispersion.keys(): |
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| 140 | if item.lower()==toks[0].lower(): |
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| 141 | for par in self.dispersion[item]: |
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| 142 | if par.lower() == toks[1].lower(): |
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| 143 | self.dispersion[item][par] = value |
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| 144 | return |
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| 145 | else: |
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| 146 | # Look for standard parameter |
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| 147 | for item in self.params.keys(): |
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| 148 | if item.lower()==name.lower(): |
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| 149 | self.params[item] = value |
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| 150 | return |
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| 151 | |
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| 152 | raise ValueError, "Model does not contain parameter %s" % name |
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[ae3ce4e] | 153 | |
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[ae60f86] | 154 | def getParam(self, name): |
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| 155 | """ |
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| 156 | Set the value of a model parameter |
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| 157 | |
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[ae3ce4e] | 158 | @param name: name of the parameter |
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| 159 | """ |
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[ae60f86] | 160 | # Look for dispersion parameters |
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[ae3ce4e] | 161 | toks = name.split('.') |
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[ae60f86] | 162 | if len(toks)==2: |
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| 163 | for item in self.dispersion.keys(): |
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| 164 | if item.lower()==toks[0].lower(): |
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| 165 | for par in self.dispersion[item]: |
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| 166 | if par.lower() == toks[1].lower(): |
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| 167 | return self.dispersion[item][par] |
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| 168 | else: |
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| 169 | # Look for standard parameter |
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| 170 | for item in self.params.keys(): |
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| 171 | if item.lower()==name.lower(): |
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| 172 | return self.params[item] |
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| 173 | |
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| 174 | raise ValueError, "Model does not contain parameter %s" % name |
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| 175 | |
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[ae3ce4e] | 176 | def getParamList(self): |
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| 177 | """ |
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[ae60f86] | 178 | Return a list of all available parameters for the model |
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| 179 | """ |
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| 180 | list = self.params.keys() |
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| 181 | # WARNING: Extending the list with the dispersion parameters |
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| 182 | list.extend(self.getDispParamList()) |
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| 183 | return list |
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| 184 | |
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| 185 | def getDispParamList(self): |
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| 186 | """ |
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| 187 | Return a list of all available parameters for the model |
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| 188 | """ |
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| 189 | list = [] |
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| 190 | |
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| 191 | for item in self.dispersion.keys(): |
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| 192 | for p in self.dispersion[item].keys(): |
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| 193 | if p not in ['type']: |
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| 194 | list.append('%s.%s' % (item.lower(), p.lower())) |
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| 195 | |
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| 196 | return list |
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| 197 | |
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| 198 | # Old-style methods that are no longer used |
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| 199 | def setParamWithToken(self, name, value, token, member): return NotImplemented |
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| 200 | def getParamWithToken(self, name, token, member): return NotImplemented |
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| 201 | def getParamListWithToken(self, token, member): return NotImplemented |
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| 202 | def __add__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 203 | def __sub__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 204 | def __mul__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 205 | def __div__(self, other): raise ValueError, "Model operation are no longer supported" |
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| 206 | |
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