| 1 | |
|---|
| 2 | ##################################################################### |
|---|
| 3 | #This software was developed by the University of Tennessee as part of the |
|---|
| 4 | #Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
|---|
| 5 | #project funded by the US National Science Foundation. |
|---|
| 6 | #See the license text in license.txt |
|---|
| 7 | #copyright 2008, University of Tennessee |
|---|
| 8 | ###################################################################### |
|---|
| 9 | |
|---|
| 10 | import DataLoader.extensions.smearer as smearer |
|---|
| 11 | import numpy |
|---|
| 12 | import math |
|---|
| 13 | import logging, sys |
|---|
| 14 | from DataLoader.smearing_2d import Smearer2D |
|---|
| 15 | |
|---|
| 16 | def smear_selection(data1D): |
|---|
| 17 | """ |
|---|
| 18 | Creates the right type of smearer according |
|---|
| 19 | to the data. |
|---|
| 20 | |
|---|
| 21 | The canSAS format has a rule that either |
|---|
| 22 | slit smearing data OR resolution smearing data |
|---|
| 23 | is available. |
|---|
| 24 | |
|---|
| 25 | For the present purpose, we choose the one that |
|---|
| 26 | has none-zero data. If both slit and resolution |
|---|
| 27 | smearing arrays are filled with good data |
|---|
| 28 | (which should not happen), then we choose the |
|---|
| 29 | resolution smearing data. |
|---|
| 30 | |
|---|
| 31 | :param data1D: Data1D object |
|---|
| 32 | """ |
|---|
| 33 | # Sanity check. If we are not dealing with a SANS Data1D |
|---|
| 34 | # object, just return None |
|---|
| 35 | if data1D.__class__.__name__ not in ['Data1D', 'Theory1D']: |
|---|
| 36 | if data1D == None: |
|---|
| 37 | return None |
|---|
| 38 | elif data1D.dqx_data == None or data1D.dqy_data == None: |
|---|
| 39 | return None |
|---|
| 40 | return Smearer2D(data1D) |
|---|
| 41 | |
|---|
| 42 | if not hasattr(data1D, "dx") and not hasattr(data1D, "dxl") and not hasattr(data1D, "dxw"): |
|---|
| 43 | return None |
|---|
| 44 | |
|---|
| 45 | # Look for resolution smearing data |
|---|
| 46 | _found_resolution = False |
|---|
| 47 | if data1D.dx is not None and len(data1D.dx)==len(data1D.x): |
|---|
| 48 | |
|---|
| 49 | # Check that we have non-zero data |
|---|
| 50 | if data1D.dx[0]>0.0: |
|---|
| 51 | _found_resolution = True |
|---|
| 52 | #print "_found_resolution",_found_resolution |
|---|
| 53 | #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0] |
|---|
| 54 | # If we found resolution smearing data, return a QSmearer |
|---|
| 55 | if _found_resolution == True: |
|---|
| 56 | return QSmearer(data1D) |
|---|
| 57 | |
|---|
| 58 | # Look for slit smearing data |
|---|
| 59 | _found_slit = False |
|---|
| 60 | if data1D.dxl is not None and len(data1D.dxl)==len(data1D.x) \ |
|---|
| 61 | and data1D.dxw is not None and len(data1D.dxw)==len(data1D.x): |
|---|
| 62 | |
|---|
| 63 | # Check that we have non-zero data |
|---|
| 64 | if data1D.dxl[0]>0.0 or data1D.dxw[0]>0.0: |
|---|
| 65 | _found_slit = True |
|---|
| 66 | |
|---|
| 67 | # Sanity check: all data should be the same as a function of Q |
|---|
| 68 | for item in data1D.dxl: |
|---|
| 69 | if data1D.dxl[0] != item: |
|---|
| 70 | _found_resolution = False |
|---|
| 71 | break |
|---|
| 72 | |
|---|
| 73 | for item in data1D.dxw: |
|---|
| 74 | if data1D.dxw[0] != item: |
|---|
| 75 | _found_resolution = False |
|---|
| 76 | break |
|---|
| 77 | # If we found slit smearing data, return a slit smearer |
|---|
| 78 | if _found_slit == True: |
|---|
| 79 | return SlitSmearer(data1D) |
|---|
| 80 | |
|---|
| 81 | return None |
|---|
| 82 | |
|---|
| 83 | |
|---|
| 84 | class _BaseSmearer(object): |
|---|
| 85 | |
|---|
| 86 | def __init__(self): |
|---|
| 87 | self.nbins = 0 |
|---|
| 88 | self._weights = None |
|---|
| 89 | ## Internal flag to keep track of C++ smearer initialization |
|---|
| 90 | self._init_complete = False |
|---|
| 91 | self._smearer = None |
|---|
| 92 | |
|---|
| 93 | def __deepcopy__(self, memo={}): |
|---|
| 94 | """ |
|---|
| 95 | Return a valid copy of self. |
|---|
| 96 | Avoid copying the _smearer C object and force a matrix recompute |
|---|
| 97 | when the copy is used. |
|---|
| 98 | """ |
|---|
| 99 | result = _BaseSmearer() |
|---|
| 100 | result.nbins = self.nbins |
|---|
| 101 | return result |
|---|
| 102 | |
|---|
| 103 | |
|---|
| 104 | def _compute_matrix(self): return NotImplemented |
|---|
| 105 | |
|---|
| 106 | def get_bin_range(self, q_min=None, q_max=None): |
|---|
| 107 | """ |
|---|
| 108 | |
|---|
| 109 | :param q_min: minimum q-value to smear |
|---|
| 110 | :param q_max: maximum q-value to smear |
|---|
| 111 | |
|---|
| 112 | """ |
|---|
| 113 | # If this is the first time we call for smearing, |
|---|
| 114 | # initialize the C++ smearer object first |
|---|
| 115 | if not self._init_complete: |
|---|
| 116 | self._initialize_smearer() |
|---|
| 117 | |
|---|
| 118 | if q_min == None: |
|---|
| 119 | q_min = self.min |
|---|
| 120 | |
|---|
| 121 | if q_max == None: |
|---|
| 122 | q_max = self.max |
|---|
| 123 | |
|---|
| 124 | _qmin_unsmeared, _qmax_unsmeared = self.get_unsmeared_range(q_min, q_max) |
|---|
| 125 | |
|---|
| 126 | _first_bin = None |
|---|
| 127 | _last_bin = None |
|---|
| 128 | |
|---|
| 129 | step = (self.max-self.min)/(self.nbins-1.0) |
|---|
| 130 | try: |
|---|
| 131 | for i in range(self.nbins): |
|---|
| 132 | q_i = smearer.get_q(self._smearer, i) |
|---|
| 133 | if (q_i >= _qmin_unsmeared) and (q_i <= _qmax_unsmeared): |
|---|
| 134 | # Identify first and last bin |
|---|
| 135 | if _first_bin is None: |
|---|
| 136 | _first_bin = i |
|---|
| 137 | else: |
|---|
| 138 | _last_bin = i |
|---|
| 139 | except: |
|---|
| 140 | raise RuntimeError, "_BaseSmearer.get_bin_range: error getting range\n %s" % sys.exc_value |
|---|
| 141 | |
|---|
| 142 | return _first_bin, _last_bin |
|---|
| 143 | |
|---|
| 144 | def __call__(self, iq_in, first_bin=0, last_bin=None): |
|---|
| 145 | """ |
|---|
| 146 | Perform smearing |
|---|
| 147 | """ |
|---|
| 148 | # If this is the first time we call for smearing, |
|---|
| 149 | # initialize the C++ smearer object first |
|---|
| 150 | if not self._init_complete: |
|---|
| 151 | self._initialize_smearer() |
|---|
| 152 | |
|---|
| 153 | # Get the max value for the last bin |
|---|
| 154 | if last_bin is None or last_bin>=len(iq_in): |
|---|
| 155 | last_bin = len(iq_in)-1 |
|---|
| 156 | # Check that the first bin is positive |
|---|
| 157 | if first_bin<0: |
|---|
| 158 | first_bin = 0 |
|---|
| 159 | |
|---|
| 160 | # Sanity check |
|---|
| 161 | if len(iq_in) != self.nbins: |
|---|
| 162 | raise RuntimeError, "Invalid I(q) vector: inconsistent array length %d != %s" % (len(iq_in), str(self.nbins)) |
|---|
| 163 | |
|---|
| 164 | # Storage for smeared I(q) |
|---|
| 165 | iq_out = numpy.zeros(self.nbins) |
|---|
| 166 | smear_output = smearer.smear(self._smearer, iq_in, iq_out, first_bin, last_bin) |
|---|
| 167 | if smear_output < 0: |
|---|
| 168 | raise RuntimeError, "_BaseSmearer: could not smear, code = %g" % smear_output |
|---|
| 169 | return iq_out |
|---|
| 170 | |
|---|
| 171 | class _SlitSmearer(_BaseSmearer): |
|---|
| 172 | """ |
|---|
| 173 | Slit smearing for I(q) array |
|---|
| 174 | """ |
|---|
| 175 | |
|---|
| 176 | def __init__(self, nbins=None, width=None, height=None, min=None, max=None): |
|---|
| 177 | """ |
|---|
| 178 | Initialization |
|---|
| 179 | |
|---|
| 180 | :param iq: I(q) array [cm-1] |
|---|
| 181 | :param width: slit width [A-1] |
|---|
| 182 | :param height: slit height [A-1] |
|---|
| 183 | :param min: Q_min [A-1] |
|---|
| 184 | :param max: Q_max [A-1] |
|---|
| 185 | |
|---|
| 186 | """ |
|---|
| 187 | _BaseSmearer.__init__(self) |
|---|
| 188 | ## Slit width in Q units |
|---|
| 189 | self.width = width |
|---|
| 190 | ## Slit height in Q units |
|---|
| 191 | self.height = height |
|---|
| 192 | ## Q_min (Min Q-value for I(q)) |
|---|
| 193 | self.min = min |
|---|
| 194 | ## Q_max (Max Q_value for I(q)) |
|---|
| 195 | self.max = max |
|---|
| 196 | ## Number of Q bins |
|---|
| 197 | self.nbins = nbins |
|---|
| 198 | ## Number of points used in the smearing computation |
|---|
| 199 | self.npts = 1000 |
|---|
| 200 | ## Smearing matrix |
|---|
| 201 | self._weights = None |
|---|
| 202 | self.qvalues = None |
|---|
| 203 | |
|---|
| 204 | def _initialize_smearer(self): |
|---|
| 205 | """ |
|---|
| 206 | Initialize the C++ smearer object. |
|---|
| 207 | This method HAS to be called before smearing |
|---|
| 208 | """ |
|---|
| 209 | #self._smearer = smearer.new_slit_smearer(self.width, self.height, self.min, self.max, self.nbins) |
|---|
| 210 | self._smearer = smearer.new_slit_smearer_with_q(self.width, self.height, self.qvalues) |
|---|
| 211 | self._init_complete = True |
|---|
| 212 | |
|---|
| 213 | def get_unsmeared_range(self, q_min, q_max): |
|---|
| 214 | """ |
|---|
| 215 | Determine the range needed in unsmeared-Q to cover |
|---|
| 216 | the smeared Q range |
|---|
| 217 | """ |
|---|
| 218 | # Range used for input to smearing |
|---|
| 219 | _qmin_unsmeared = q_min |
|---|
| 220 | _qmax_unsmeared = q_max |
|---|
| 221 | try: |
|---|
| 222 | _qmin_unsmeared = self.min |
|---|
| 223 | _qmax_unsmeared = self.max |
|---|
| 224 | except: |
|---|
| 225 | logging.error("_SlitSmearer.get_bin_range: %s" % sys.exc_value) |
|---|
| 226 | return _qmin_unsmeared, _qmax_unsmeared |
|---|
| 227 | |
|---|
| 228 | class SlitSmearer(_SlitSmearer): |
|---|
| 229 | """ |
|---|
| 230 | Adaptor for slit smearing class and SANS data |
|---|
| 231 | """ |
|---|
| 232 | def __init__(self, data1D): |
|---|
| 233 | """ |
|---|
| 234 | Assumption: equally spaced bins of increasing q-values. |
|---|
| 235 | |
|---|
| 236 | :param data1D: data used to set the smearing parameters |
|---|
| 237 | """ |
|---|
| 238 | # Initialization from parent class |
|---|
| 239 | super(SlitSmearer, self).__init__() |
|---|
| 240 | |
|---|
| 241 | ## Slit width |
|---|
| 242 | self.width = 0 |
|---|
| 243 | if data1D.dxw is not None and len(data1D.dxw)==len(data1D.x): |
|---|
| 244 | self.width = data1D.dxw[0] |
|---|
| 245 | # Sanity check |
|---|
| 246 | for value in data1D.dxw: |
|---|
| 247 | if value != self.width: |
|---|
| 248 | raise RuntimeError, "Slit smearing parameters must be the same for all data" |
|---|
| 249 | |
|---|
| 250 | ## Slit height |
|---|
| 251 | self.height = 0 |
|---|
| 252 | if data1D.dxl is not None and len(data1D.dxl)==len(data1D.x): |
|---|
| 253 | self.height = data1D.dxl[0] |
|---|
| 254 | # Sanity check |
|---|
| 255 | for value in data1D.dxl: |
|---|
| 256 | if value != self.height: |
|---|
| 257 | raise RuntimeError, "Slit smearing parameters must be the same for all data" |
|---|
| 258 | |
|---|
| 259 | ## Number of Q bins |
|---|
| 260 | self.nbins = len(data1D.x) |
|---|
| 261 | ## Minimum Q |
|---|
| 262 | self.min = min(data1D.x) |
|---|
| 263 | ## Maximum |
|---|
| 264 | self.max = max(data1D.x) |
|---|
| 265 | ## Q-values |
|---|
| 266 | self.qvalues = data1D.x |
|---|
| 267 | |
|---|
| 268 | |
|---|
| 269 | class _QSmearer(_BaseSmearer): |
|---|
| 270 | """ |
|---|
| 271 | Perform Gaussian Q smearing |
|---|
| 272 | """ |
|---|
| 273 | |
|---|
| 274 | def __init__(self, nbins=None, width=None, min=None, max=None): |
|---|
| 275 | """ |
|---|
| 276 | Initialization |
|---|
| 277 | |
|---|
| 278 | :param nbins: number of Q bins |
|---|
| 279 | :param width: array standard deviation in Q [A-1] |
|---|
| 280 | :param min: Q_min [A-1] |
|---|
| 281 | :param max: Q_max [A-1] |
|---|
| 282 | """ |
|---|
| 283 | _BaseSmearer.__init__(self) |
|---|
| 284 | ## Standard deviation in Q [A-1] |
|---|
| 285 | self.width = width |
|---|
| 286 | ## Q_min (Min Q-value for I(q)) |
|---|
| 287 | self.min = min |
|---|
| 288 | ## Q_max (Max Q_value for I(q)) |
|---|
| 289 | self.max = max |
|---|
| 290 | ## Number of Q bins |
|---|
| 291 | self.nbins = nbins |
|---|
| 292 | ## Smearing matrix |
|---|
| 293 | self._weights = None |
|---|
| 294 | self.qvalues = None |
|---|
| 295 | |
|---|
| 296 | def _initialize_smearer(self): |
|---|
| 297 | """ |
|---|
| 298 | Initialize the C++ smearer object. |
|---|
| 299 | This method HAS to be called before smearing |
|---|
| 300 | """ |
|---|
| 301 | #self._smearer = smearer.new_q_smearer(numpy.asarray(self.width), self.min, self.max, self.nbins) |
|---|
| 302 | self._smearer = smearer.new_q_smearer_with_q(numpy.asarray(self.width), self.qvalues) |
|---|
| 303 | self._init_complete = True |
|---|
| 304 | |
|---|
| 305 | def get_unsmeared_range(self, q_min, q_max): |
|---|
| 306 | """ |
|---|
| 307 | Determine the range needed in unsmeared-Q to cover |
|---|
| 308 | the smeared Q range |
|---|
| 309 | Take 3 sigmas as the offset between smeared and unsmeared space |
|---|
| 310 | """ |
|---|
| 311 | # Range used for input to smearing |
|---|
| 312 | _qmin_unsmeared = q_min |
|---|
| 313 | _qmax_unsmeared = q_max |
|---|
| 314 | try: |
|---|
| 315 | offset = 3.0*max(self.width) |
|---|
| 316 | _qmin_unsmeared = max([self.min, q_min-offset]) |
|---|
| 317 | _qmax_unsmeared = min([self.max, q_max+offset]) |
|---|
| 318 | except: |
|---|
| 319 | logging.error("_QSmearer.get_bin_range: %s" % sys.exc_value) |
|---|
| 320 | return _qmin_unsmeared, _qmax_unsmeared |
|---|
| 321 | |
|---|
| 322 | |
|---|
| 323 | class QSmearer(_QSmearer): |
|---|
| 324 | """ |
|---|
| 325 | Adaptor for Gaussian Q smearing class and SANS data |
|---|
| 326 | """ |
|---|
| 327 | def __init__(self, data1D): |
|---|
| 328 | """ |
|---|
| 329 | Assumption: equally spaced bins of increasing q-values. |
|---|
| 330 | |
|---|
| 331 | :param data1D: data used to set the smearing parameters |
|---|
| 332 | """ |
|---|
| 333 | # Initialization from parent class |
|---|
| 334 | super(QSmearer, self).__init__() |
|---|
| 335 | |
|---|
| 336 | ## Resolution |
|---|
| 337 | self.width = numpy.zeros(len(data1D.x)) |
|---|
| 338 | if data1D.dx is not None and len(data1D.dx)==len(data1D.x): |
|---|
| 339 | self.width = data1D.dx |
|---|
| 340 | |
|---|
| 341 | ## Number of Q bins |
|---|
| 342 | self.nbins = len(data1D.x) |
|---|
| 343 | ## Minimum Q |
|---|
| 344 | self.min = min(data1D.x) |
|---|
| 345 | ## Maximum |
|---|
| 346 | self.max = max(data1D.x) |
|---|
| 347 | ## Q-values |
|---|
| 348 | self.qvalues = data1D.x |
|---|
| 349 | |
|---|
| 350 | |
|---|
| 351 | if __name__ == '__main__': |
|---|
| 352 | x = 0.001*numpy.arange(1,11) |
|---|
| 353 | y = 12.0-numpy.arange(1,11) |
|---|
| 354 | print x |
|---|
| 355 | #for i in range(10): print i, 0.001 + i*0.008/9.0 |
|---|
| 356 | #for i in range(100): print i, int(math.floor( (i/ (100/9.0)) )) |
|---|
| 357 | |
|---|
| 358 | |
|---|
| 359 | s = _SlitSmearer(nbins=10, width=0.0, height=0.005, min=0.001, max=0.010) |
|---|
| 360 | #s = _QSmearer(nbins=10, width=0.001, min=0.001, max=0.010) |
|---|
| 361 | s._compute_matrix() |
|---|
| 362 | |
|---|
| 363 | sy = s(y) |
|---|
| 364 | print sy |
|---|
| 365 | |
|---|
| 366 | if True: |
|---|
| 367 | for i in range(10): |
|---|
| 368 | print x[i],y[i], sy[i] |
|---|
| 369 | #print q, ' : ', s.weight(q), s._compute_iq(q) |
|---|
| 370 | #print q, ' : ', s(q), s._compute_iq(q) |
|---|
| 371 | #s._compute_iq(q) |
|---|
| 372 | |
|---|
| 373 | |
|---|
| 374 | |
|---|
| 375 | |
|---|
