source: sasview/src/sas/sascalc/data_util/qsmearing.py @ 8938502

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Last change on this file since 8938502 was 8938502, checked in by Adam Washington <adam.washington@…>, 7 years ago

Merge branch 'master' of github.com:SasView/sasview

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File size: 8.6 KB
Line 
1"""
2    Handle Q smearing
3"""
4#####################################################################
5#This software was developed by the University of Tennessee as part of the
6#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
7#project funded by the US National Science Foundation.
8#See the license text in license.txt
9#copyright 2008, University of Tennessee
10######################################################################
11import math
12import logging
13import sys
14
15import numpy as np  # type: ignore
16from numpy import pi, exp # type:ignore
17
18from sasmodels.resolution import Slit1D, Pinhole1D
19from sasmodels.sesans import SesansTransform
20from sasmodels.resolution2d import Pinhole2D
21from .nxsunit import Converter
22
23def smear_selection(data, model = None):
24    """
25    Creates the right type of smearer according
26    to the data.
27    The canSAS format has a rule that either
28    slit smearing data OR resolution smearing data
29    is available.
30
31    For the present purpose, we choose the one that
32    has none-zero data. If both slit and resolution
33    smearing arrays are filled with good data
34    (which should not happen), then we choose the
35    resolution smearing data.
36
37    :param data: Data1D object
38    :param model: sas.model instance
39    """
40    # Sanity check. If we are not dealing with a SAS Data1D
41    # object, just return None
42    # This checks for 2D data (does not throw exception because fail is common)
43    if  data.__class__.__name__ not in ['Data1D', 'Theory1D']:
44        if data is None:
45            return None
46        elif data.dqx_data is None or data.dqy_data is None:
47            return None
48        return PySmear2D(data)
49    # This checks for 1D data with smearing info in the data itself (again, fail is likely; no exceptions)
50    if  not hasattr(data, "dx") and not hasattr(data, "dxl")\
51         and not hasattr(data, "dxw"):
52        return None
53
54    # Look for resolution smearing data
55    # This is the code that checks for SESANS data; it looks for the file loader
56    # TODO: change other sanity checks to check for file loader instead of data structure?
57    _found_sesans = False
58    #if data.dx is not None and data.meta_data['loader']=='SESANS':
59    if data.dx is not None and data.isSesans:
60        #if data.dx[0] > 0.0:
61        if np.size(data.dx[data.dx <= 0]) == 0:
62            _found_sesans = True
63        # if data.dx[0] <= 0.0:
64        if np.size(data.dx[data.dx <= 0]) > 0:
65            raise ValueError('one or more of your dx values are negative, please check the data file!')
66
67    if _found_sesans:
68        # Pre-compute the Hankel matrix (H)
69        SElength = Converter(data._xunit)(data.x, "A")
70
71        theta_max = Converter("radians")(data.sample.zacceptance)[0]
72        q_max = 2 * np.pi / np.max(data.source.wavelength) * np.sin(theta_max)
73        zaccept = Converter("1/A")(q_max, "1/" + data.source.wavelength_unit),
74
75        Rmax = 10000000
76        hankel = SesansTransform(data.x, SElength,
77                                 data.source.wavelength,
78                                 zaccept, Rmax)
79        # Then return the actual transform, as if it were a smearing function
80        return PySmear(hankel, model, offset=0)
81
82    _found_resolution = False
83    if data.dx is not None and len(data.dx) == len(data.x):
84
85        # Check that we have non-zero data
86        if data.dx[0] > 0.0:
87            _found_resolution = True
88            #print "_found_resolution",_found_resolution
89            #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0]
90    # If we found resolution smearing data, return a QSmearer
91    if _found_resolution == True:
92         return pinhole_smear(data, model)
93
94    # Look for slit smearing data
95    _found_slit = False
96    if data.dxl is not None and len(data.dxl) == len(data.x) \
97        and data.dxw is not None and len(data.dxw) == len(data.x):
98
99        # Check that we have non-zero data
100        if data.dxl[0] > 0.0 or data.dxw[0] > 0.0:
101            _found_slit = True
102
103        # Sanity check: all data should be the same as a function of Q
104        for item in data.dxl:
105            if data.dxl[0] != item:
106                _found_resolution = False
107                break
108
109        for item in data.dxw:
110            if data.dxw[0] != item:
111                _found_resolution = False
112                break
113    # If we found slit smearing data, return a slit smearer
114    if _found_slit == True:
115        return slit_smear(data, model)
116    return None
117
118
119class PySmear(object):
120    """
121    Wrapper for pure python sasmodels resolution functions.
122    """
123    def __init__(self, resolution, model, offset=None):
124        self.model = model
125        self.resolution = resolution
126        if offset is None:
127            offset = np.searchsorted(self.resolution.q_calc, self.resolution.q[0])
128        self.offset = offset
129
130    def apply(self, iq_in, first_bin=0, last_bin=None):
131        """
132        Apply the resolution function to the data.
133        Note that this is called with iq_in matching data.x, but with
134        iq_in[first_bin:last_bin] set to theory values for these bins,
135        and the remainder left undefined.  The first_bin, last_bin values
136        should be those returned from get_bin_range.
137        The returned value is of the same length as iq_in, with the range
138        first_bin:last_bin set to the resolution smeared values.
139        """
140        if last_bin is None: last_bin = len(iq_in)
141        start, end = first_bin + self.offset, last_bin + self.offset
142        q_calc = self.resolution.q_calc
143        iq_calc = np.empty_like(q_calc)
144        if start > 0:
145            iq_calc[:start] = self.model.evalDistribution(q_calc[:start])
146        if end+1 < len(q_calc):
147            iq_calc[end+1:] = self.model.evalDistribution(q_calc[end+1:])
148        iq_calc[start:end+1] = iq_in[first_bin:last_bin+1]
149        smeared = self.resolution.apply(iq_calc)
150        return smeared
151    __call__ = apply
152
153    def get_bin_range(self, q_min=None, q_max=None):
154        """
155        For a given q_min, q_max, find the corresponding indices in the data.
156        Returns first, last.
157        Note that these are indexes into q from the data, not the q_calc
158        needed by the resolution function.  Note also that these are the
159        indices, not the range limits.  That is, the complete range will be
160        q[first:last+1].
161        """
162        q = self.resolution.q
163        first = np.searchsorted(q, q_min)
164        last = np.searchsorted(q, q_max)
165        return first, min(last,len(q)-1)
166
167def slit_smear(data, model=None):
168    q = data.x
169    width = data.dxw if data.dxw is not None else 0
170    height = data.dxl if data.dxl is not None else 0
171    # TODO: width and height seem to be reversed
172    return PySmear(Slit1D(q, height, width), model)
173
174def pinhole_smear(data, model=None):
175    q = data.x
176    width = data.dx if data.dx is not None else 0
177    return PySmear(Pinhole1D(q, width), model)
178
179
180class PySmear2D(object):
181    """
182    Q smearing class for SAS 2d pinhole data
183    """
184
185    def __init__(self, data=None, model=None):
186        self.data = data
187        self.model = model
188        self.accuracy = 'Low'
189        self.limit = 3.0
190        self.index = None
191        self.coords = 'polar'
192        self.smearer = True
193
194    def set_accuracy(self, accuracy='Low'):
195        """
196        Set accuracy.
197
198        :param accuracy:  string
199        """
200        self.accuracy = accuracy
201
202    def set_smearer(self, smearer=True):
203        """
204        Set whether or not smearer will be used
205
206        :param smearer: smear object
207
208        """
209        self.smearer = smearer
210
211    def set_data(self, data=None):
212        """
213        Set data.
214
215        :param data: DataLoader.Data_info type
216        """
217        self.data = data
218
219    def set_model(self, model=None):
220        """
221        Set model.
222
223        :param model: sas.models instance
224        """
225        self.model = model
226
227    def set_index(self, index=None):
228        """
229        Set index.
230
231        :param index: 1d arrays
232        """
233        self.index = index
234
235    def get_value(self):
236        """
237        Over sampling of r_nbins times phi_nbins, calculate Gaussian weights,
238        then find smeared intensity
239        """
240        if self.smearer:
241            res = Pinhole2D(data=self.data, index=self.index,
242                            nsigma=3.0, accuracy=self.accuracy,
243                            coords=self.coords)
244            val = self.model.evalDistribution(res.q_calc)
245            return res.apply(val)
246        else:
247            index = self.index if self.index is not None else slice(None)
248            qx_data = self.data.qx_data[index]
249            qy_data = self.data.qy_data[index]
250            q_calc = [qx_data, qy_data]
251            val = self.model.evalDistribution(q_calc)
252            return val
253
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