[803f835] | 1 | """ |
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| 2 | Class interface to the model calculator. |
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| 3 | |
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| 4 | Calling a model is somewhat non-trivial since the functions called depend |
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| 5 | on the data type. For 1D data the *Iq* kernel needs to be called, for |
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| 6 | 2D data the *Iqxy* kernel needs to be called, and for SESANS data the |
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| 7 | *Iq* kernel needs to be called followed by a Hankel transform. Before |
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| 8 | the kernel is called an appropriate *q* calculation vector needs to be |
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| 9 | constructed. This is not the simple *q* vector where you have measured |
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| 10 | the data since the resolution calculation will require values beyond the |
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| 11 | range of the measured data. After the calculation the resolution calculator |
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| 12 | must be called to return the predicted value for each measured data point. |
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| 13 | |
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| 14 | :class:`DirectModel` is a callable object that takes *parameter=value* |
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| 15 | keyword arguments and returns the appropriate theory values for the data. |
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| 16 | |
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| 17 | :class:`DataMixin` does the real work of interpreting the data and calling |
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| 18 | the model calculator. This is used by :class:`DirectModel`, which uses |
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| 19 | direct parameter values and by :class:`bumps_model.Experiment` which wraps |
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| 20 | the parameter values in boxes so that the user can set fitting ranges, etc. |
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| 21 | on the individual parameters and send the model to the Bumps optimizers. |
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| 22 | """ |
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| 23 | from __future__ import print_function |
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[ae7b97b] | 24 | |
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| 25 | import numpy as np |
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| 26 | |
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[aa4946b] | 27 | from .core import load_model_definition, load_model, make_kernel |
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| 28 | from .core import call_kernel, call_ER, call_VR |
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[7cf2cfd] | 29 | from . import sesans |
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| 30 | from . import resolution |
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| 31 | from . import resolution2d |
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[ae7b97b] | 32 | |
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[7cf2cfd] | 33 | class DataMixin(object): |
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| 34 | """ |
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| 35 | DataMixin captures the common aspects of evaluating a SAS model for a |
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| 36 | particular data set, including calculating Iq and evaluating the |
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| 37 | resolution function. It is used in particular by :class:`DirectModel`, |
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| 38 | which evaluates a SAS model parameters as key word arguments to the |
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| 39 | calculator method, and by :class:`bumps_model.Experiment`, which wraps the |
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| 40 | model and data for use with the Bumps fitting engine. It is not |
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| 41 | currently used by :class:`sasview_model.SasviewModel` since this will |
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| 42 | require a number of changes to SasView before we can do it. |
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[803f835] | 43 | |
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| 44 | :meth:`_interpret_data` initializes the data structures necessary |
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| 45 | to manage the calculations. This sets attributes in the child class |
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| 46 | such as *data_type* and *resolution*. |
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| 47 | |
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| 48 | :meth:`_calc_theory` evaluates the model at the given control values. |
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| 49 | |
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| 50 | :meth:`_set_data` sets the intensity data in the data object, |
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| 51 | possibly with random noise added. This is useful for simulating a |
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| 52 | dataset with the results from :meth:`_calc_theory`. |
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[7cf2cfd] | 53 | """ |
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| 54 | def _interpret_data(self, data, model): |
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[803f835] | 55 | # pylint: disable=attribute-defined-outside-init |
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| 56 | |
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[7cf2cfd] | 57 | self._data = data |
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| 58 | self._model = model |
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| 59 | |
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| 60 | # interpret data |
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| 61 | if hasattr(data, 'lam'): |
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| 62 | self.data_type = 'sesans' |
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| 63 | elif hasattr(data, 'qx_data'): |
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| 64 | self.data_type = 'Iqxy' |
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| 65 | else: |
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| 66 | self.data_type = 'Iq' |
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| 67 | |
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| 68 | partype = model.info['partype'] |
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| 69 | |
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| 70 | if self.data_type == 'sesans': |
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| 71 | q = sesans.make_q(data.sample.zacceptance, data.Rmax) |
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[803f835] | 72 | index = slice(None, None) |
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| 73 | res = None |
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[7cf2cfd] | 74 | if data.y is not None: |
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[803f835] | 75 | Iq, dIq = data.y, data.dy |
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| 76 | else: |
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| 77 | Iq, dIq = None, None |
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[7cf2cfd] | 78 | #self._theory = np.zeros_like(q) |
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| 79 | q_vectors = [q] |
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| 80 | elif self.data_type == 'Iqxy': |
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| 81 | if not partype['orientation'] and not partype['magnetic']: |
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| 82 | raise ValueError("not 2D without orientation or magnetic parameters") |
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| 83 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 84 | qmin = getattr(data, 'qmin', 1e-16) |
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| 85 | qmax = getattr(data, 'qmax', np.inf) |
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| 86 | accuracy = getattr(data, 'accuracy', 'Low') |
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[803f835] | 87 | index = ~data.mask & (q >= qmin) & (q <= qmax) |
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[7cf2cfd] | 88 | if data.data is not None: |
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[803f835] | 89 | index &= ~np.isnan(data.data) |
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| 90 | Iq = data.data[index] |
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| 91 | dIq = data.err_data[index] |
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| 92 | else: |
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| 93 | Iq, dIq = None, None |
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| 94 | res = resolution2d.Pinhole2D(data=data, index=index, |
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| 95 | nsigma=3.0, accuracy=accuracy) |
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[7cf2cfd] | 96 | #self._theory = np.zeros_like(self.Iq) |
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[803f835] | 97 | q_vectors = res.q_calc |
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[7cf2cfd] | 98 | elif self.data_type == 'Iq': |
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[803f835] | 99 | index = (data.x >= data.qmin) & (data.x <= data.qmax) |
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[7cf2cfd] | 100 | if data.y is not None: |
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[803f835] | 101 | index &= ~np.isnan(data.y) |
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| 102 | Iq = data.y[index] |
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| 103 | dIq = data.dy[index] |
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| 104 | else: |
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| 105 | Iq, dIq = None, None |
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[7cf2cfd] | 106 | if getattr(data, 'dx', None) is not None: |
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[803f835] | 107 | q, dq = data.x[index], data.dx[index] |
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| 108 | if (dq > 0).any(): |
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| 109 | res = resolution.Pinhole1D(q, dq) |
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[7cf2cfd] | 110 | else: |
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[803f835] | 111 | res = resolution.Perfect1D(q) |
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| 112 | elif (getattr(data, 'dxl', None) is not None |
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| 113 | and getattr(data, 'dxw', None) is not None): |
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| 114 | res = resolution.Slit1D(data.x[index], |
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| 115 | width=data.dxh[index], |
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| 116 | height=data.dxw[index]) |
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[7cf2cfd] | 117 | else: |
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[803f835] | 118 | res = resolution.Perfect1D(data.x[index]) |
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[7cf2cfd] | 119 | |
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| 120 | #self._theory = np.zeros_like(self.Iq) |
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[803f835] | 121 | q_vectors = [res.q_calc] |
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[7cf2cfd] | 122 | else: |
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| 123 | raise ValueError("Unknown data type") # never gets here |
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| 124 | |
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| 125 | # Remember function inputs so we can delay loading the function and |
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| 126 | # so we can save/restore state |
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| 127 | self._kernel_inputs = [v for v in q_vectors] |
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| 128 | self._kernel = None |
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[803f835] | 129 | self.Iq, self.dIq, self.index = Iq, dIq, index |
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| 130 | self.resolution = res |
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[7cf2cfd] | 131 | |
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| 132 | def _set_data(self, Iq, noise=None): |
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[803f835] | 133 | # pylint: disable=attribute-defined-outside-init |
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[7cf2cfd] | 134 | if noise is not None: |
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| 135 | self.dIq = Iq*noise*0.01 |
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| 136 | dy = self.dIq |
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| 137 | y = Iq + np.random.randn(*dy.shape) * dy |
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| 138 | self.Iq = y |
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| 139 | if self.data_type == 'Iq': |
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| 140 | self._data.dy[self.index] = dy |
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| 141 | self._data.y[self.index] = y |
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| 142 | elif self.data_type == 'Iqxy': |
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| 143 | self._data.data[self.index] = y |
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| 144 | elif self.data_type == 'sesans': |
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| 145 | self._data.y[self.index] = y |
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| 146 | else: |
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| 147 | raise ValueError("Unknown model") |
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| 148 | |
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| 149 | def _calc_theory(self, pars, cutoff=0.0): |
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| 150 | if self._kernel is None: |
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[eafc9fa] | 151 | self._kernel = make_kernel(self._model, self._kernel_inputs) # pylint: disable=attribute-defined-outside-init |
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[7cf2cfd] | 152 | |
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| 153 | Iq_calc = call_kernel(self._kernel, pars, cutoff=cutoff) |
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| 154 | if self.data_type == 'sesans': |
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| 155 | result = sesans.hankel(self._data.x, self._data.lam * 1e-9, |
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| 156 | self._data.sample.thickness / 10, |
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| 157 | self._kernel_inputs[0], Iq_calc) |
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| 158 | else: |
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| 159 | result = self.resolution.apply(Iq_calc) |
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| 160 | return result |
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| 161 | |
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| 162 | |
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| 163 | class DirectModel(DataMixin): |
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[803f835] | 164 | """ |
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| 165 | Create a calculator object for a model. |
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| 166 | |
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| 167 | *data* is 1D SAS, 2D SAS or SESANS data |
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| 168 | |
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| 169 | *model* is a model calculator return from :func:`generate.load_model` |
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| 170 | |
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| 171 | *cutoff* is the polydispersity weight cutoff. |
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| 172 | """ |
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[7cf2cfd] | 173 | def __init__(self, data, model, cutoff=1e-5): |
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| 174 | self.model = model |
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| 175 | self.cutoff = cutoff |
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[803f835] | 176 | # Note: _interpret_data defines the model attributes |
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[7cf2cfd] | 177 | self._interpret_data(data, model) |
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[803f835] | 178 | |
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[16bc3fc] | 179 | def __call__(self, **pars): |
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[7cf2cfd] | 180 | return self._calc_theory(pars, cutoff=self.cutoff) |
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[803f835] | 181 | |
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[aa4946b] | 182 | def ER(self, **pars): |
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[803f835] | 183 | """ |
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| 184 | Compute the equivalent radius for the model. |
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| 185 | |
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| 186 | Return 0. if not defined. |
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| 187 | """ |
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[aa4946b] | 188 | return call_ER(self.model.info, pars) |
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[803f835] | 189 | |
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[aa4946b] | 190 | def VR(self, **pars): |
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[803f835] | 191 | """ |
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| 192 | Compute the equivalent volume for the model, including polydispersity |
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| 193 | effects. |
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| 194 | |
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| 195 | Return 1. if not defined. |
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| 196 | """ |
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[aa4946b] | 197 | return call_VR(self.model.info, pars) |
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[803f835] | 198 | |
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[7cf2cfd] | 199 | def simulate_data(self, noise=None, **pars): |
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[803f835] | 200 | """ |
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| 201 | Generate simulated data for the model. |
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| 202 | """ |
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[7cf2cfd] | 203 | Iq = self.__call__(**pars) |
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| 204 | self._set_data(Iq, noise=noise) |
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[ae7b97b] | 205 | |
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[803f835] | 206 | def main(): |
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| 207 | """ |
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| 208 | Program to evaluate a particular model at a set of q values. |
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| 209 | """ |
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[ae7b97b] | 210 | import sys |
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[7cf2cfd] | 211 | from .data import empty_data1D, empty_data2D |
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| 212 | |
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[ae7b97b] | 213 | if len(sys.argv) < 3: |
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[9404dd3] | 214 | print("usage: python -m sasmodels.direct_model modelname (q|qx,qy) par=val ...") |
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[ae7b97b] | 215 | sys.exit(1) |
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| 216 | model_name = sys.argv[1] |
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[aa4946b] | 217 | call = sys.argv[2].upper() |
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[803f835] | 218 | if call not in ("ER", "VR"): |
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[7cf2cfd] | 219 | try: |
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| 220 | values = [float(v) for v in call.split(',')] |
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| 221 | except: |
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| 222 | values = [] |
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[aa4946b] | 223 | if len(values) == 1: |
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[7cf2cfd] | 224 | q, = values |
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| 225 | data = empty_data1D([q]) |
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[aa4946b] | 226 | elif len(values) == 2: |
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[803f835] | 227 | qx, qy = values |
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| 228 | data = empty_data2D([qx], [qy]) |
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[aa4946b] | 229 | else: |
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[9404dd3] | 230 | print("use q or qx,qy or ER or VR") |
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[aa4946b] | 231 | sys.exit(1) |
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[7cf2cfd] | 232 | else: |
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| 233 | data = empty_data1D([0.001]) # Data not used in ER/VR |
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| 234 | |
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| 235 | model_definition = load_model_definition(model_name) |
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[d18582e] | 236 | model = load_model(model_definition) |
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[7cf2cfd] | 237 | calculator = DirectModel(data, model) |
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[803f835] | 238 | pars = dict((k, float(v)) |
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[ae7b97b] | 239 | for pair in sys.argv[3:] |
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[803f835] | 240 | for k, v in [pair.split('=')]) |
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[aa4946b] | 241 | if call == "ER": |
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[9404dd3] | 242 | print(calculator.ER(**pars)) |
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[aa4946b] | 243 | elif call == "VR": |
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[9404dd3] | 244 | print(calculator.VR(**pars)) |
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[aa4946b] | 245 | else: |
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[7cf2cfd] | 246 | Iq = calculator(**pars) |
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[9404dd3] | 247 | print(Iq[0]) |
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[ae7b97b] | 248 | |
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| 249 | if __name__ == "__main__": |
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[803f835] | 250 | main() |
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