| 1 | import warnings |
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| 2 | |
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| 3 | import numpy as np |
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| 4 | |
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| 5 | from .core import load_model_definition, load_model, make_kernel |
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| 6 | from .core import call_kernel, call_ER, call_VR |
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| 7 | from . import sesans |
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| 8 | from . import resolution |
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| 9 | from . import resolution2d |
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| 10 | |
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| 11 | class DataMixin(object): |
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| 12 | """ |
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| 13 | DataMixin captures the common aspects of evaluating a SAS model for a |
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| 14 | particular data set, including calculating Iq and evaluating the |
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| 15 | resolution function. It is used in particular by :class:`DirectModel`, |
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| 16 | which evaluates a SAS model parameters as key word arguments to the |
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| 17 | calculator method, and by :class:`bumps_model.Experiment`, which wraps the |
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| 18 | model and data for use with the Bumps fitting engine. It is not |
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| 19 | currently used by :class:`sasview_model.SasviewModel` since this will |
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| 20 | require a number of changes to SasView before we can do it. |
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| 21 | """ |
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| 22 | def _interpret_data(self, data, model): |
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| 23 | self._data = data |
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| 24 | self._model = model |
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| 25 | |
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| 26 | # interpret data |
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| 27 | if hasattr(data, 'lam'): |
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| 28 | self.data_type = 'sesans' |
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| 29 | elif hasattr(data, 'qx_data'): |
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| 30 | self.data_type = 'Iqxy' |
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| 31 | else: |
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| 32 | self.data_type = 'Iq' |
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| 33 | |
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| 34 | partype = model.info['partype'] |
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| 35 | |
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| 36 | if self.data_type == 'sesans': |
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| 37 | q = sesans.make_q(data.sample.zacceptance, data.Rmax) |
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| 38 | self.index = slice(None, None) |
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| 39 | if data.y is not None: |
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| 40 | self.Iq = data.y |
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| 41 | self.dIq = data.dy |
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| 42 | #self._theory = np.zeros_like(q) |
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| 43 | q_vectors = [q] |
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| 44 | elif self.data_type == 'Iqxy': |
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| 45 | if not partype['orientation'] and not partype['magnetic']: |
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| 46 | raise ValueError("not 2D without orientation or magnetic parameters") |
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| 47 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 48 | qmin = getattr(data, 'qmin', 1e-16) |
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| 49 | qmax = getattr(data, 'qmax', np.inf) |
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| 50 | accuracy = getattr(data, 'accuracy', 'Low') |
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| 51 | self.index = ~data.mask & (q >= qmin) & (q <= qmax) |
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| 52 | if data.data is not None: |
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| 53 | self.index &= ~np.isnan(data.data) |
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| 54 | self.Iq = data.data[self.index] |
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| 55 | self.dIq = data.err_data[self.index] |
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| 56 | self.resolution = resolution2d.Pinhole2D(data=data, index=self.index, |
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| 57 | nsigma=3.0, accuracy=accuracy) |
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| 58 | #self._theory = np.zeros_like(self.Iq) |
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| 59 | q_vectors = self.resolution.q_calc |
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| 60 | elif self.data_type == 'Iq': |
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| 61 | self.index = (data.x >= data.qmin) & (data.x <= data.qmax) |
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| 62 | if data.y is not None: |
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| 63 | self.index &= ~np.isnan(data.y) |
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| 64 | self.Iq = data.y[self.index] |
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| 65 | self.dIq = data.dy[self.index] |
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| 66 | if getattr(data, 'dx', None) is not None: |
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| 67 | q, dq = data.x[self.index], data.dx[self.index] |
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| 68 | if (dq>0).any(): |
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| 69 | self.resolution = resolution.Pinhole1D(q, dq) |
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| 70 | else: |
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| 71 | self.resolution = resolution.Perfect1D(q) |
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| 72 | elif (getattr(data, 'dxl', None) is not None and |
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| 73 | getattr(data, 'dxw', None) is not None): |
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| 74 | self.resolution = resolution.Slit1D(data.x[self.index], |
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| 75 | width=data.dxh[self.index], |
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| 76 | height=data.dxw[self.index]) |
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| 77 | else: |
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| 78 | self.resolution = resolution.Perfect1D(data.x[self.index]) |
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| 79 | |
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| 80 | #self._theory = np.zeros_like(self.Iq) |
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| 81 | q_vectors = [self.resolution.q_calc] |
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| 82 | else: |
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| 83 | raise ValueError("Unknown data type") # never gets here |
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| 84 | |
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| 85 | # Remember function inputs so we can delay loading the function and |
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| 86 | # so we can save/restore state |
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| 87 | self._kernel_inputs = [v for v in q_vectors] |
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| 88 | self._kernel = None |
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| 89 | |
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| 90 | def _set_data(self, Iq, noise=None): |
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| 91 | if noise is not None: |
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| 92 | self.dIq = Iq*noise*0.01 |
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| 93 | dy = self.dIq |
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| 94 | y = Iq + np.random.randn(*dy.shape) * dy |
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| 95 | self.Iq = y |
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| 96 | if self.data_type == 'Iq': |
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| 97 | self._data.dy[self.index] = dy |
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| 98 | self._data.y[self.index] = y |
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| 99 | elif self.data_type == 'Iqxy': |
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| 100 | self._data.data[self.index] = y |
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| 101 | elif self.data_type == 'sesans': |
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| 102 | self._data.y[self.index] = y |
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| 103 | else: |
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| 104 | raise ValueError("Unknown model") |
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| 105 | |
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| 106 | def _calc_theory(self, pars, cutoff=0.0): |
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| 107 | if self._kernel is None: |
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| 108 | q_input = self._model.make_input(self._kernel_inputs) |
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| 109 | self._kernel = self._model(q_input) |
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| 110 | |
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| 111 | Iq_calc = call_kernel(self._kernel, pars, cutoff=cutoff) |
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| 112 | if self.data_type == 'sesans': |
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| 113 | result = sesans.hankel(self._data.x, self._data.lam * 1e-9, |
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| 114 | self._data.sample.thickness / 10, |
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| 115 | self._kernel_inputs[0], Iq_calc) |
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| 116 | else: |
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| 117 | result = self.resolution.apply(Iq_calc) |
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| 118 | return result |
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| 119 | |
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| 120 | |
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| 121 | class DirectModel(DataMixin): |
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| 122 | def __init__(self, data, model, cutoff=1e-5): |
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| 123 | self.model = model |
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| 124 | self.cutoff = cutoff |
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| 125 | self._interpret_data(data, model) |
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| 126 | self.kernel = make_kernel(self.model, self._kernel_inputs) |
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| 127 | def __call__(self, **pars): |
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| 128 | return self._calc_theory(pars, cutoff=self.cutoff) |
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| 129 | def ER(self, **pars): |
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| 130 | return call_ER(self.model.info, pars) |
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| 131 | def VR(self, **pars): |
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| 132 | return call_VR(self.model.info, pars) |
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| 133 | def simulate_data(self, noise=None, **pars): |
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| 134 | Iq = self.__call__(**pars) |
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| 135 | self._set_data(Iq, noise=noise) |
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| 136 | |
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| 137 | def demo(): |
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| 138 | import sys |
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| 139 | from .data import empty_data1D, empty_data2D |
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| 140 | |
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| 141 | if len(sys.argv) < 3: |
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| 142 | print "usage: python -m sasmodels.direct_model modelname (q|qx,qy) par=val ..." |
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| 143 | sys.exit(1) |
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| 144 | model_name = sys.argv[1] |
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| 145 | call = sys.argv[2].upper() |
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| 146 | if call not in ("ER","VR"): |
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| 147 | try: |
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| 148 | values = [float(v) for v in call.split(',')] |
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| 149 | except: |
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| 150 | values = [] |
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| 151 | if len(values) == 1: |
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| 152 | q, = values |
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| 153 | data = empty_data1D([q]) |
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| 154 | elif len(values) == 2: |
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| 155 | qx,qy = values |
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| 156 | data = empty_data2D([qx],[qy]) |
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| 157 | else: |
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| 158 | print "use q or qx,qy or ER or VR" |
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| 159 | sys.exit(1) |
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| 160 | else: |
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| 161 | data = empty_data1D([0.001]) # Data not used in ER/VR |
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| 162 | |
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| 163 | model_definition = load_model_definition(model_name) |
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| 164 | model = load_model(model_definition, dtype='single') |
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| 165 | calculator = DirectModel(data, model) |
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| 166 | pars = dict((k,float(v)) |
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| 167 | for pair in sys.argv[3:] |
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| 168 | for k,v in [pair.split('=')]) |
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| 169 | if call == "ER": |
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| 170 | print calculator.ER(**pars) |
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| 171 | elif call == "VR": |
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| 172 | print calculator.VR(**pars) |
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| 173 | else: |
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| 174 | Iq = calculator(**pars) |
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| 175 | print Iq[0] |
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| 176 | |
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| 177 | if __name__ == "__main__": |
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| 178 | demo() |
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