[9696b075] | 1 | #!/usr/bin/env python |
---|
| 2 | """ |
---|
| 3 | Provide Line function (y= A + Bx) as a BaseComponent model |
---|
| 4 | """ |
---|
| 5 | |
---|
| 6 | from sans.models.BaseComponent import BaseComponent |
---|
| 7 | import math |
---|
[d4bf55e9] | 8 | import numpy |
---|
[9696b075] | 9 | |
---|
| 10 | |
---|
| 11 | class LineModel(BaseComponent): |
---|
| 12 | """ |
---|
| 13 | Class that evaluates a linear model. |
---|
| 14 | |
---|
| 15 | f(x) = A + Bx |
---|
| 16 | |
---|
| 17 | List of default parameters: |
---|
| 18 | A = 1.0 |
---|
| 19 | B = 1.0 |
---|
| 20 | """ |
---|
| 21 | |
---|
| 22 | def __init__(self): |
---|
| 23 | """ Initialization """ |
---|
| 24 | |
---|
| 25 | # Initialize BaseComponent first, then sphere |
---|
| 26 | BaseComponent.__init__(self) |
---|
| 27 | |
---|
| 28 | ## Name of the model |
---|
| 29 | self.name = "LineModel" |
---|
| 30 | |
---|
| 31 | ## Define parameters |
---|
| 32 | self.params = {} |
---|
| 33 | self.params['A'] = 1.0 |
---|
| 34 | self.params['B'] = 1.0 |
---|
| 35 | self.description='f(x) = A + Bx' |
---|
| 36 | ## Parameter details [units, min, max] |
---|
| 37 | self.details = {} |
---|
| 38 | self.details['A'] = ['', None, None] |
---|
| 39 | self.details['B'] = ['', None, None] |
---|
[988130c6] | 40 | # fixed paramaters |
---|
| 41 | self.fixed=[] |
---|
[9696b075] | 42 | def _line(self, x): |
---|
| 43 | """ |
---|
| 44 | Evaluate the function |
---|
| 45 | @param x: x-value |
---|
| 46 | @return: function value |
---|
| 47 | """ |
---|
| 48 | return self.params['A'] + x *self.params['B'] |
---|
| 49 | |
---|
| 50 | def run(self, x = 0.0): |
---|
| 51 | """ Evaluate the model |
---|
| 52 | @param x: simple value |
---|
| 53 | @return: (Line value) |
---|
| 54 | """ |
---|
| 55 | if x.__class__.__name__ == 'list': |
---|
| 56 | return self._line(x[0]*math.cos(x[1]))*self._line(x[0]*math.sin(x[1])) |
---|
| 57 | elif x.__class__.__name__ == 'tuple': |
---|
| 58 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
---|
| 59 | else: |
---|
| 60 | return self._line(x) |
---|
| 61 | |
---|
| 62 | def runXY(self, x = 0.0): |
---|
| 63 | """ Evaluate the model |
---|
| 64 | @param x: simple value |
---|
| 65 | @return: Line value |
---|
| 66 | """ |
---|
| 67 | if x.__class__.__name__ == 'list': |
---|
| 68 | return self._line(x[0])*self._line(x[1]) |
---|
| 69 | elif x.__class__.__name__ == 'tuple': |
---|
| 70 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
---|
| 71 | else: |
---|
| 72 | return self._line(x) |
---|
[d4bf55e9] | 73 | |
---|
| 74 | def evalDistribution(self, qdist): |
---|
| 75 | """ |
---|
| 76 | Evaluate a distribution of q-values. |
---|
| 77 | |
---|
| 78 | * For 1D, a numpy array is expected as input: |
---|
| 79 | |
---|
| 80 | evalDistribution(q) |
---|
| 81 | |
---|
| 82 | where q is a numpy array. |
---|
| 83 | |
---|
| 84 | |
---|
| 85 | * For 2D, a list of numpy arrays are expected: [qx_prime,qy_prime], |
---|
| 86 | where 1D arrays, |
---|
| 87 | |
---|
| 88 | :param qdist: ndarray of scalar q-values or list [qx,qy] |
---|
| 89 | where qx,qy are 1D ndarrays |
---|
| 90 | |
---|
| 91 | """ |
---|
| 92 | if qdist.__class__.__name__ == 'list': |
---|
| 93 | # Check whether we have a list of ndarrays [qx,qy] |
---|
| 94 | if len(qdist)!=2 or \ |
---|
| 95 | qdist[0].__class__.__name__ != 'ndarray' or \ |
---|
| 96 | qdist[1].__class__.__name__ != 'ndarray': |
---|
| 97 | raise RuntimeError, "evalDistribution expects a list of 2 ndarrays" |
---|
| 98 | |
---|
| 99 | # Extract qx and qy for code clarity |
---|
| 100 | qx = qdist[0] |
---|
| 101 | qy = qdist[1] |
---|
| 102 | #For 2D, Z = A + B * Y, |
---|
| 103 | # so that it keeps its linearity in y-direction. |
---|
| 104 | # calculate q_r component for 2D isotropic |
---|
| 105 | q = qy |
---|
| 106 | # vectorize the model function runXY |
---|
| 107 | v_model = numpy.vectorize(self.runXY,otypes=[float]) |
---|
| 108 | # calculate the scattering |
---|
| 109 | iq_array = v_model(q) |
---|
| 110 | |
---|
| 111 | return iq_array |
---|
| 112 | |
---|
| 113 | elif qdist.__class__.__name__ == 'ndarray': |
---|
| 114 | # We have a simple 1D distribution of q-values |
---|
| 115 | v_model = numpy.vectorize(self.runXY,otypes=[float]) |
---|
| 116 | iq_array = v_model(qdist) |
---|
| 117 | |
---|
| 118 | return iq_array |
---|
| 119 | |
---|
| 120 | else: |
---|
| 121 | mesg = "evalDistribution is expecting an ndarray of scalar q-values" |
---|
| 122 | mesg += " or a list [qx,qy] where qx,qy are 2D ndarrays." |
---|
| 123 | raise RuntimeError, mesg |
---|
| 124 | |
---|
| 125 | |
---|
[9696b075] | 126 | |
---|
| 127 | |
---|
| 128 | if __name__ == "__main__": |
---|
| 129 | l = Line() |
---|
| 130 | print "hello" |
---|
| 131 | |
---|
| 132 | # End of file |
---|