| 1 | """ |
|---|
| 2 | This software was developed by the University of Tennessee as part of the |
|---|
| 3 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
|---|
| 4 | project funded by the US National Science Foundation. |
|---|
| 5 | |
|---|
| 6 | See the license text in license.txt |
|---|
| 7 | |
|---|
| 8 | copyright 2010, University of Tennessee |
|---|
| 9 | """ |
|---|
| 10 | import unittest |
|---|
| 11 | import numpy, math |
|---|
| 12 | from DataLoader.loader import Loader |
|---|
| 13 | from DataLoader.data_info import Data1D |
|---|
| 14 | from sans.invariant import invariant |
|---|
| 15 | from DataLoader.qsmearing import smear_selection |
|---|
| 16 | |
|---|
| 17 | class TestLinearFit(unittest.TestCase): |
|---|
| 18 | """ |
|---|
| 19 | Test Line fit |
|---|
| 20 | """ |
|---|
| 21 | def setUp(self): |
|---|
| 22 | x = numpy.asarray([1.,2.,3.,4.,5.,6.,7.,8.,9.]) |
|---|
| 23 | y = numpy.asarray([1.,2.,3.,4.,5.,6.,7.,8.,9.]) |
|---|
| 24 | dy = y/10.0 |
|---|
| 25 | |
|---|
| 26 | self.data = Data1D(x=x,y=y,dy=dy) |
|---|
| 27 | |
|---|
| 28 | def test_fit_linear_data(self): |
|---|
| 29 | """ |
|---|
| 30 | Simple linear fit |
|---|
| 31 | """ |
|---|
| 32 | |
|---|
| 33 | # Create invariant object. Background and scale left as defaults. |
|---|
| 34 | fit = invariant.Extrapolator(data=self.data) |
|---|
| 35 | a,b = fit.fit() |
|---|
| 36 | |
|---|
| 37 | # Test results |
|---|
| 38 | self.assertAlmostEquals(a, 1.0, 5) |
|---|
| 39 | self.assertAlmostEquals(b, 0.0, 5) |
|---|
| 40 | |
|---|
| 41 | def test_fit_linear_data_with_noise(self): |
|---|
| 42 | """ |
|---|
| 43 | Simple linear fit with noise |
|---|
| 44 | """ |
|---|
| 45 | import random, math |
|---|
| 46 | |
|---|
| 47 | for i in range(len(self.data.y)): |
|---|
| 48 | self.data.y[i] = self.data.y[i]+.1*random.random() |
|---|
| 49 | |
|---|
| 50 | # Create invariant object. Background and scale left as defaults. |
|---|
| 51 | fit = invariant.Extrapolator(data=self.data) |
|---|
| 52 | a,b = fit.fit() |
|---|
| 53 | |
|---|
| 54 | # Test results |
|---|
| 55 | self.assertTrue(math.fabs(a-1.0)<0.05) |
|---|
| 56 | self.assertTrue(math.fabs(b)<0.1) |
|---|
| 57 | |
|---|
| 58 | |
|---|
| 59 | class TestInvariantCalculator(unittest.TestCase): |
|---|
| 60 | """ |
|---|
| 61 | Test Line fit |
|---|
| 62 | """ |
|---|
| 63 | def setUp(self): |
|---|
| 64 | self.data = Loader().load("latex_smeared.xml")[0] |
|---|
| 65 | |
|---|
| 66 | def test_initial_data_processing(self): |
|---|
| 67 | """ |
|---|
| 68 | Test whether the background and scale are handled properly |
|---|
| 69 | when creating an InvariantCalculator object |
|---|
| 70 | """ |
|---|
| 71 | length = len(self.data.x) |
|---|
| 72 | self.assertEqual(length, len(self.data.y)) |
|---|
| 73 | inv = invariant.InvariantCalculator(self.data) |
|---|
| 74 | |
|---|
| 75 | self.assertEqual(length, len(inv._data.x)) |
|---|
| 76 | self.assertEqual(inv._data.x[0], self.data.x[0]) |
|---|
| 77 | |
|---|
| 78 | # Now the same thing with a background value |
|---|
| 79 | bck = 0.1 |
|---|
| 80 | inv = invariant.InvariantCalculator(self.data, background=bck) |
|---|
| 81 | self.assertEqual(inv._background, bck) |
|---|
| 82 | |
|---|
| 83 | self.assertEqual(length, len(inv._data.x)) |
|---|
| 84 | self.assertEqual(inv._data.y[0]+bck, self.data.y[0]) |
|---|
| 85 | |
|---|
| 86 | # Now the same thing with a scale value |
|---|
| 87 | scale = 0.1 |
|---|
| 88 | inv = invariant.InvariantCalculator(self.data, scale=scale) |
|---|
| 89 | self.assertEqual(inv._scale, scale) |
|---|
| 90 | |
|---|
| 91 | self.assertEqual(length, len(inv._data.x)) |
|---|
| 92 | self.assertAlmostEqual(inv._data.y[0]/scale, self.data.y[0],7) |
|---|
| 93 | |
|---|
| 94 | |
|---|
| 95 | def test_incompatible_data_class(self): |
|---|
| 96 | """ |
|---|
| 97 | Check that only classes that inherit from Data1D are allowed as data. |
|---|
| 98 | """ |
|---|
| 99 | class Incompatible(): |
|---|
| 100 | pass |
|---|
| 101 | self.assertRaises(ValueError, invariant.InvariantCalculator, Incompatible()) |
|---|
| 102 | |
|---|
| 103 | |
|---|
| 104 | class TestGuinierExtrapolation(unittest.TestCase): |
|---|
| 105 | """ |
|---|
| 106 | Generate a Guinier distribution and verify that the extrapolation |
|---|
| 107 | produce the correct ditribution. |
|---|
| 108 | """ |
|---|
| 109 | |
|---|
| 110 | def setUp(self): |
|---|
| 111 | """ |
|---|
| 112 | Generate a Guinier distribution. After extrapolating, we will |
|---|
| 113 | verify that we obtain the scale and rg parameters |
|---|
| 114 | """ |
|---|
| 115 | self.scale = 1.5 |
|---|
| 116 | self.rg = 30.0 |
|---|
| 117 | x = numpy.arange(0.0001, 0.1, 0.0001) |
|---|
| 118 | y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x]) |
|---|
| 119 | dy = y*.1 |
|---|
| 120 | self.data = Data1D(x=x, y=y, dy=dy) |
|---|
| 121 | |
|---|
| 122 | def test_low_q(self): |
|---|
| 123 | """ |
|---|
| 124 | Invariant with low-Q extrapolation |
|---|
| 125 | """ |
|---|
| 126 | # Create invariant object. Background and scale left as defaults. |
|---|
| 127 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 128 | # Set the extrapolation parameters for the low-Q range |
|---|
| 129 | inv.set_extrapolation(range='low', npts=20, function='guinier') |
|---|
| 130 | |
|---|
| 131 | self.assertEqual(inv._low_extrapolation_npts, 20) |
|---|
| 132 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier) |
|---|
| 133 | |
|---|
| 134 | # Data boundaries for fiiting |
|---|
| 135 | qmin = inv._data.x[0] |
|---|
| 136 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 137 | |
|---|
| 138 | # Extrapolate the low-Q data |
|---|
| 139 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 140 | qmin=qmin, |
|---|
| 141 | qmax=qmax, |
|---|
| 142 | power=inv._low_extrapolation_power) |
|---|
| 143 | self.assertAlmostEqual(self.scale, a, 6) |
|---|
| 144 | self.assertAlmostEqual(self.rg, b, 6) |
|---|
| 145 | |
|---|
| 146 | |
|---|
| 147 | class TestPowerLawExtrapolation(unittest.TestCase): |
|---|
| 148 | """ |
|---|
| 149 | Generate a power law distribution and verify that the extrapolation |
|---|
| 150 | produce the correct ditribution. |
|---|
| 151 | """ |
|---|
| 152 | |
|---|
| 153 | def setUp(self): |
|---|
| 154 | """ |
|---|
| 155 | Generate a power law distribution. After extrapolating, we will |
|---|
| 156 | verify that we obtain the scale and m parameters |
|---|
| 157 | """ |
|---|
| 158 | self.scale = 1.5 |
|---|
| 159 | self.m = 3.0 |
|---|
| 160 | x = numpy.arange(0.0001, 0.1, 0.0001) |
|---|
| 161 | y = numpy.asarray([self.scale * math.pow(q ,-1.0*self.m) for q in x]) |
|---|
| 162 | dy = y*.1 |
|---|
| 163 | self.data = Data1D(x=x, y=y, dy=dy) |
|---|
| 164 | |
|---|
| 165 | def test_low_q(self): |
|---|
| 166 | """ |
|---|
| 167 | Invariant with low-Q extrapolation |
|---|
| 168 | """ |
|---|
| 169 | # Create invariant object. Background and scale left as defaults. |
|---|
| 170 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 171 | # Set the extrapolation parameters for the low-Q range |
|---|
| 172 | inv.set_extrapolation(range='low', npts=20, function='power_law') |
|---|
| 173 | |
|---|
| 174 | self.assertEqual(inv._low_extrapolation_npts, 20) |
|---|
| 175 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.PowerLaw) |
|---|
| 176 | |
|---|
| 177 | # Data boundaries for fitting |
|---|
| 178 | qmin = inv._data.x[0] |
|---|
| 179 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 180 | |
|---|
| 181 | # Extrapolate the low-Q data |
|---|
| 182 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 183 | qmin=qmin, |
|---|
| 184 | qmax=qmax, |
|---|
| 185 | power=inv._low_extrapolation_power) |
|---|
| 186 | |
|---|
| 187 | self.assertAlmostEqual(self.scale, a, 6) |
|---|
| 188 | self.assertAlmostEqual(self.m, b, 6) |
|---|
| 189 | |
|---|
| 190 | class TestLinearization(unittest.TestCase): |
|---|
| 191 | |
|---|
| 192 | def test_guinier_incompatible_length(self): |
|---|
| 193 | g = invariant.Guinier() |
|---|
| 194 | data_in = Data1D(x=[1], y=[1,2], dy=None) |
|---|
| 195 | self.assertRaises(AssertionError, g.linearize_data, data_in) |
|---|
| 196 | data_in = Data1D(x=[1,1], y=[1,2], dy=[1]) |
|---|
| 197 | self.assertRaises(AssertionError, g.linearize_data, data_in) |
|---|
| 198 | |
|---|
| 199 | def test_linearization(self): |
|---|
| 200 | """ |
|---|
| 201 | Check that the linearization process filters out points |
|---|
| 202 | that can't be transformed |
|---|
| 203 | """ |
|---|
| 204 | x = numpy.asarray(numpy.asarray([0,1,2,3])) |
|---|
| 205 | y = numpy.asarray(numpy.asarray([1,1,1,1])) |
|---|
| 206 | g = invariant.Guinier() |
|---|
| 207 | data_in = Data1D(x=x, y=y) |
|---|
| 208 | data_out = g.linearize_data(data_in) |
|---|
| 209 | x_out, y_out, dy_out = data_out.x, data_out.y, data_out.dy |
|---|
| 210 | self.assertEqual(len(x_out), 3) |
|---|
| 211 | self.assertEqual(len(y_out), 3) |
|---|
| 212 | self.assertEqual(len(dy_out), 3) |
|---|
| 213 | |
|---|
| 214 | |
|---|
| 215 | class TestDataExtraLow(unittest.TestCase): |
|---|
| 216 | """ |
|---|
| 217 | Generate a Guinier distribution and verify that the extrapolation |
|---|
| 218 | produce the correct ditribution. Tested if the data generated by the |
|---|
| 219 | invariant calculator is correct |
|---|
| 220 | """ |
|---|
| 221 | |
|---|
| 222 | def setUp(self): |
|---|
| 223 | """ |
|---|
| 224 | Generate a Guinier distribution. After extrapolating, we will |
|---|
| 225 | verify that we obtain the scale and rg parameters |
|---|
| 226 | """ |
|---|
| 227 | self.scale = 1.5 |
|---|
| 228 | self.rg = 30.0 |
|---|
| 229 | x = numpy.arange(0.0001, 0.1, 0.0001) |
|---|
| 230 | y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x]) |
|---|
| 231 | dy = y*.1 |
|---|
| 232 | self.data = Data1D(x=x, y=y, dy=dy) |
|---|
| 233 | |
|---|
| 234 | def test_low_q(self): |
|---|
| 235 | """ |
|---|
| 236 | Invariant with low-Q extrapolation with no slit smear |
|---|
| 237 | """ |
|---|
| 238 | # Create invariant object. Background and scale left as defaults. |
|---|
| 239 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 240 | # Set the extrapolation parameters for the low-Q range |
|---|
| 241 | inv.set_extrapolation(range='low', npts=20, function='guinier') |
|---|
| 242 | |
|---|
| 243 | self.assertEqual(inv._low_extrapolation_npts, 20) |
|---|
| 244 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier) |
|---|
| 245 | |
|---|
| 246 | # Data boundaries for fiiting |
|---|
| 247 | qmin = inv._data.x[0] |
|---|
| 248 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 249 | |
|---|
| 250 | # Extrapolate the low-Q data |
|---|
| 251 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 252 | qmin=qmin, |
|---|
| 253 | qmax=qmax, |
|---|
| 254 | power=inv._low_extrapolation_power) |
|---|
| 255 | self.assertAlmostEqual(self.scale, a, 6) |
|---|
| 256 | self.assertAlmostEqual(self.rg, b, 6) |
|---|
| 257 | |
|---|
| 258 | qstar = inv.get_qstar(extrapolation='low') |
|---|
| 259 | reel_y = self.data.y |
|---|
| 260 | test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x) |
|---|
| 261 | for i in range(len(self.data.x)): |
|---|
| 262 | value = math.fabs(test_y[i]-reel_y[i])/reel_y[i] |
|---|
| 263 | self.assert_(value < 0.001) |
|---|
| 264 | |
|---|
| 265 | class TestDataExtraLowSlit(unittest.TestCase): |
|---|
| 266 | """ |
|---|
| 267 | for a smear data, test that the fitting go through |
|---|
| 268 | reel data for the 2 first points |
|---|
| 269 | """ |
|---|
| 270 | def setUp(self): |
|---|
| 271 | """ |
|---|
| 272 | Reel data containing slit smear information |
|---|
| 273 | .Use 2 points of data to fit with power_law when exptrapolating |
|---|
| 274 | """ |
|---|
| 275 | list = Loader().load("latex_smeared.xml") |
|---|
| 276 | self.data = list[0] |
|---|
| 277 | self.data.dxl = list[0].dxl |
|---|
| 278 | self.data.dxw = list[0].dxw |
|---|
| 279 | self.npts = 2 |
|---|
| 280 | |
|---|
| 281 | def test_low_q(self): |
|---|
| 282 | """ |
|---|
| 283 | Invariant with low-Q extrapolation with slit smear |
|---|
| 284 | """ |
|---|
| 285 | # Create invariant object. Background and scale left as defaults. |
|---|
| 286 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 287 | # Set the extrapolation parameters for the low-Q range |
|---|
| 288 | inv.set_extrapolation(range='low', npts=self.npts, function='power_law') |
|---|
| 289 | |
|---|
| 290 | self.assertEqual(inv._low_extrapolation_npts, self.npts) |
|---|
| 291 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.PowerLaw) |
|---|
| 292 | |
|---|
| 293 | # Data boundaries for fiiting |
|---|
| 294 | qmin = inv._data.x[0] |
|---|
| 295 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 296 | |
|---|
| 297 | # Extrapolate the low-Q data |
|---|
| 298 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 299 | qmin=qmin, |
|---|
| 300 | qmax=qmax, |
|---|
| 301 | power=inv._low_extrapolation_power) |
|---|
| 302 | |
|---|
| 303 | qstar = inv.get_qstar(extrapolation='low') |
|---|
| 304 | reel_y = self.data.y |
|---|
| 305 | #Compution the y 's coming out of the invariant when computing extrapolated |
|---|
| 306 | #low data . expect the fit engine to have been already called and the guinier |
|---|
| 307 | # to have the radius and the scale fitted |
|---|
| 308 | test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x[:inv._low_extrapolation_npts]) |
|---|
| 309 | #Check any points generated from the reel data and the extrapolation have |
|---|
| 310 | #very close value |
|---|
| 311 | self.assert_(len(test_y))== len(reel_y[:inv._low_extrapolation_npts]) |
|---|
| 312 | for i in range(inv._low_extrapolation_npts): |
|---|
| 313 | value = math.fabs(test_y[i]-reel_y[i])/reel_y[i] |
|---|
| 314 | self.assert_(value < 0.001) |
|---|
| 315 | data_out_range, data_in_range= inv.get_extra_data_low(npts_in=None) |
|---|
| 316 | |
|---|
| 317 | class TestDataExtraLowSlitGuinier(unittest.TestCase): |
|---|
| 318 | """ |
|---|
| 319 | for a smear data, test that the fitting go through |
|---|
| 320 | reel data for atleast the 2 first points |
|---|
| 321 | """ |
|---|
| 322 | |
|---|
| 323 | def setUp(self): |
|---|
| 324 | """ |
|---|
| 325 | Generate a Guinier distribution. After extrapolating, we will |
|---|
| 326 | verify that we obtain the scale and rg parameters |
|---|
| 327 | """ |
|---|
| 328 | self.scale = 1.5 |
|---|
| 329 | self.rg = 30.0 |
|---|
| 330 | x = numpy.arange(0.0001, 0.1, 0.0001) |
|---|
| 331 | y = numpy.asarray([self.scale * math.exp( -(q*self.rg)**2 / 3.0 ) for q in x]) |
|---|
| 332 | dy = y*.1 |
|---|
| 333 | dxl = 0.117 * numpy.ones(len(x)) |
|---|
| 334 | self.data = Data1D(x=x, y=y, dy=dy) |
|---|
| 335 | self.data.dxl = dxl |
|---|
| 336 | self.npts = len(x)-10 |
|---|
| 337 | |
|---|
| 338 | def test_low_q(self): |
|---|
| 339 | """ |
|---|
| 340 | Invariant with low-Q extrapolation with slit smear |
|---|
| 341 | """ |
|---|
| 342 | # Create invariant object. Background and scale left as defaults. |
|---|
| 343 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 344 | # Set the extrapolation parameters for the low-Q range |
|---|
| 345 | inv.set_extrapolation(range='low', npts=self.npts, function='guinier') |
|---|
| 346 | |
|---|
| 347 | self.assertEqual(inv._low_extrapolation_npts, self.npts) |
|---|
| 348 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier) |
|---|
| 349 | |
|---|
| 350 | # Data boundaries for fiiting |
|---|
| 351 | qmin = inv._data.x[0] |
|---|
| 352 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 353 | |
|---|
| 354 | # Extrapolate the low-Q data |
|---|
| 355 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 356 | qmin=qmin, |
|---|
| 357 | qmax=qmax, |
|---|
| 358 | power=inv._low_extrapolation_power) |
|---|
| 359 | |
|---|
| 360 | |
|---|
| 361 | qstar = inv.get_qstar(extrapolation='low') |
|---|
| 362 | reel_y = self.data.y |
|---|
| 363 | #Compution the y 's coming out of the invariant when computing extrapolated |
|---|
| 364 | #low data . expect the fit engine to have been already called and the guinier |
|---|
| 365 | # to have the radius and the scale fitted |
|---|
| 366 | test_y = inv._low_extrapolation_function.evaluate_model(x=self.data.x[:inv._low_extrapolation_npts]) |
|---|
| 367 | self.assert_(len(test_y))== len(reel_y[:inv._low_extrapolation_npts]) |
|---|
| 368 | |
|---|
| 369 | for i in range(inv._low_extrapolation_npts): |
|---|
| 370 | value = math.fabs(test_y[i]-reel_y[i])/reel_y[i] |
|---|
| 371 | self.assert_(value < 0.001) |
|---|
| 372 | |
|---|
| 373 | def test_low_data(self): |
|---|
| 374 | """ |
|---|
| 375 | Invariant with low-Q extrapolation with slit smear |
|---|
| 376 | """ |
|---|
| 377 | # Create invariant object. Background and scale left as defaults. |
|---|
| 378 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 379 | # Set the extrapolation parameters for the low-Q range |
|---|
| 380 | inv.set_extrapolation(range='low', npts=self.npts, function='guinier') |
|---|
| 381 | |
|---|
| 382 | self.assertEqual(inv._low_extrapolation_npts, self.npts) |
|---|
| 383 | self.assertEqual(inv._low_extrapolation_function.__class__, invariant.Guinier) |
|---|
| 384 | |
|---|
| 385 | # Data boundaries for fiiting |
|---|
| 386 | qmin = inv._data.x[0] |
|---|
| 387 | qmax = inv._data.x[inv._low_extrapolation_npts - 1] |
|---|
| 388 | |
|---|
| 389 | # Extrapolate the low-Q data |
|---|
| 390 | a, b = inv._fit(model=inv._low_extrapolation_function, |
|---|
| 391 | qmin=qmin, |
|---|
| 392 | qmax=qmax, |
|---|
| 393 | power=inv._low_extrapolation_power) |
|---|
| 394 | |
|---|
| 395 | |
|---|
| 396 | qstar = inv.get_qstar(extrapolation='low') |
|---|
| 397 | reel_y = self.data.y |
|---|
| 398 | #Compution the y 's coming out of the invariant when computing extrapolated |
|---|
| 399 | #low data . expect the fit engine to have been already called and the guinier |
|---|
| 400 | # to have the radius and the scale fitted |
|---|
| 401 | data_out_range, data_in_range= inv.get_extra_data_low() |
|---|
| 402 | test_y = data_in_range.y |
|---|
| 403 | self.assert_(len(test_y))== len(reel_y[:inv._low_extrapolation_npts]) |
|---|
| 404 | for i in range(inv._low_extrapolation_npts): |
|---|
| 405 | value = math.fabs(test_y[i]-reel_y[i])/reel_y[i] |
|---|
| 406 | self.assert_(value < 0.001) |
|---|
| 407 | |
|---|
| 408 | data_out_range, data_in_range= inv.get_extra_data_low(npts_in= 2, nsteps=10, |
|---|
| 409 | q_start= 1e-4) |
|---|
| 410 | test_y = data_in_range.y |
|---|
| 411 | self.assert_(len(test_y))== len(reel_y[:2]) |
|---|
| 412 | for i in range(2): |
|---|
| 413 | value = math.fabs(test_y[i]-reel_y[i])/reel_y[i] |
|---|
| 414 | self.assert_(value < 0.001) |
|---|
| 415 | #test the data out of range |
|---|
| 416 | test_out_y = data_out_range.y |
|---|
| 417 | self.assertEqual(len(test_out_y), 10) |
|---|
| 418 | |
|---|
| 419 | class TestDataExtraHighSlitPowerLaw(unittest.TestCase): |
|---|
| 420 | """ |
|---|
| 421 | for a smear data, test that the fitting go through |
|---|
| 422 | reel data for atleast the 2 first points |
|---|
| 423 | """ |
|---|
| 424 | |
|---|
| 425 | def setUp(self): |
|---|
| 426 | """ |
|---|
| 427 | Generate a Guinier distribution. After extrapolating, we will |
|---|
| 428 | verify that we obtain the scale and rg parameters |
|---|
| 429 | """ |
|---|
| 430 | self.scale = 1.5 |
|---|
| 431 | self.m = 3.0 |
|---|
| 432 | x = numpy.arange(0.0001, 0.1, 0.0001) |
|---|
| 433 | y = numpy.asarray([self.scale * math.pow(q ,-1.0*self.m) for q in x]) |
|---|
| 434 | dy = y*.1 |
|---|
| 435 | self.data = Data1D(x=x, y=y, dy=dy) |
|---|
| 436 | dxl = 0.117 * numpy.ones(len(x)) |
|---|
| 437 | self.data.dxl = dxl |
|---|
| 438 | self.npts = 20 |
|---|
| 439 | |
|---|
| 440 | def test_high_q(self): |
|---|
| 441 | """ |
|---|
| 442 | Invariant with high-Q extrapolation with slit smear |
|---|
| 443 | """ |
|---|
| 444 | # Create invariant object. Background and scale left as defaults. |
|---|
| 445 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 446 | # Set the extrapolation parameters for the low-Q range |
|---|
| 447 | inv.set_extrapolation(range='high', npts=self.npts, function='power_law') |
|---|
| 448 | |
|---|
| 449 | self.assertEqual(inv._high_extrapolation_npts, self.npts) |
|---|
| 450 | self.assertEqual(inv._high_extrapolation_function.__class__, invariant.PowerLaw) |
|---|
| 451 | |
|---|
| 452 | # Data boundaries for fiiting |
|---|
| 453 | xlen = len(self.data.x) |
|---|
| 454 | start = xlen - inv._high_extrapolation_npts |
|---|
| 455 | qmin = inv._data.x[start] |
|---|
| 456 | qmax = inv._data.x[xlen-1] |
|---|
| 457 | |
|---|
| 458 | # Extrapolate the high-Q data |
|---|
| 459 | a, b = inv._fit(model=inv._high_extrapolation_function, |
|---|
| 460 | qmin=qmin, |
|---|
| 461 | qmax=qmax, |
|---|
| 462 | power=inv._high_extrapolation_power) |
|---|
| 463 | |
|---|
| 464 | |
|---|
| 465 | qstar = inv.get_qstar(extrapolation='high') |
|---|
| 466 | reel_y = self.data.y |
|---|
| 467 | #Compution the y 's coming out of the invariant when computing extrapolated |
|---|
| 468 | #low data . expect the fit engine to have been already called and the power law |
|---|
| 469 | # to have the radius and the scale fitted |
|---|
| 470 | |
|---|
| 471 | |
|---|
| 472 | test_y = inv._high_extrapolation_function.evaluate_model(x=self.data.x[start: ]) |
|---|
| 473 | self.assert_(len(test_y))== len(reel_y[start:]) |
|---|
| 474 | |
|---|
| 475 | for i in range(len(self.data.x[start:])): |
|---|
| 476 | value = math.fabs(test_y[i]-reel_y[start+i])/reel_y[start+i] |
|---|
| 477 | self.assert_(value < 0.001) |
|---|
| 478 | |
|---|
| 479 | def test_high_data(self): |
|---|
| 480 | """ |
|---|
| 481 | Invariant with low-Q extrapolation with slit smear |
|---|
| 482 | """ |
|---|
| 483 | # Create invariant object. Background and scale left as defaults. |
|---|
| 484 | inv = invariant.InvariantCalculator(data=self.data) |
|---|
| 485 | # Set the extrapolation parameters for the low-Q range |
|---|
| 486 | inv.set_extrapolation(range='high', npts=self.npts, function='power_law') |
|---|
| 487 | |
|---|
| 488 | self.assertEqual(inv._high_extrapolation_npts, self.npts) |
|---|
| 489 | self.assertEqual(inv._high_extrapolation_function.__class__, invariant.PowerLaw) |
|---|
| 490 | |
|---|
| 491 | # Data boundaries for fiiting |
|---|
| 492 | xlen = len(self.data.x) |
|---|
| 493 | start = xlen - inv._high_extrapolation_npts |
|---|
| 494 | qmin = inv._data.x[start] |
|---|
| 495 | qmax = inv._data.x[xlen-1] |
|---|
| 496 | |
|---|
| 497 | # Extrapolate the high-Q data |
|---|
| 498 | a, b = inv._fit(model=inv._high_extrapolation_function, |
|---|
| 499 | qmin=qmin, |
|---|
| 500 | qmax=qmax, |
|---|
| 501 | power=inv._high_extrapolation_power) |
|---|
| 502 | |
|---|
| 503 | |
|---|
| 504 | qstar = inv.get_qstar(extrapolation='high') |
|---|
| 505 | reel_y = self.data.y |
|---|
| 506 | #Compution the y 's coming out of the invariant when computing extrapolated |
|---|
| 507 | #low data . expect the fit engine to have been already called and the power law |
|---|
| 508 | # to have the radius and the scale fitted |
|---|
| 509 | |
|---|
| 510 | data_out_range, data_in_range= inv.get_extra_data_high() |
|---|
| 511 | test_y = data_in_range.y |
|---|
| 512 | self.assert_(len(test_y))== len(reel_y[start:]) |
|---|
| 513 | temp = reel_y[start:] |
|---|
| 514 | |
|---|
| 515 | for i in range(len(self.data.x[start:])): |
|---|
| 516 | value = math.fabs(test_y[i]- temp[i])/temp[i] |
|---|
| 517 | self.assert_(value < 0.001) |
|---|
| 518 | |
|---|
| 519 | data_out_range, data_in_range= inv.get_extra_data_high(npts_in=5, nsteps=10, |
|---|
| 520 | q_end= 2) |
|---|
| 521 | test_y = data_in_range.y |
|---|
| 522 | self.assert_(len(test_y)==5) |
|---|
| 523 | temp = reel_y[start:start+5] |
|---|
| 524 | |
|---|
| 525 | for i in range(len(self.data.x[start:start+5])): |
|---|
| 526 | |
|---|
| 527 | value = math.fabs(test_y[i]- temp[i])/temp[i] |
|---|
| 528 | self.assert_(value < 0.06) |
|---|
| 529 | #test the data out of range |
|---|
| 530 | test_out_y = data_out_range.y |
|---|
| 531 | self.assertEqual(len(test_out_y), 10) |
|---|
| 532 | |
|---|
