| 1 | """ |
|---|
| 2 | SAS data representations. |
|---|
| 3 | |
|---|
| 4 | Plotting functions for data sets: |
|---|
| 5 | |
|---|
| 6 | :func:`plot_data` plots the data file. |
|---|
| 7 | |
|---|
| 8 | :func:`plot_theory` plots a calculated result from the model. |
|---|
| 9 | |
|---|
| 10 | Wrappers for the sasview data loader and data manipulations: |
|---|
| 11 | |
|---|
| 12 | :func:`load_data` loads a sasview data file. |
|---|
| 13 | |
|---|
| 14 | :func:`set_beam_stop` masks the beam stop from the data. |
|---|
| 15 | |
|---|
| 16 | :func:`set_half` selects the right or left half of the data, which can |
|---|
| 17 | be useful for shear measurements which have not been properly corrected |
|---|
| 18 | for path length and reflections. |
|---|
| 19 | |
|---|
| 20 | :func:`set_top` cuts the top part off the data. |
|---|
| 21 | |
|---|
| 22 | |
|---|
| 23 | Empty data sets for evaluating models without data: |
|---|
| 24 | |
|---|
| 25 | :func:`empty_data1D` creates an empty dataset, which is useful for plotting |
|---|
| 26 | a theory function before the data is measured. |
|---|
| 27 | |
|---|
| 28 | :func:`empty_data2D` creates an empty 2D dataset. |
|---|
| 29 | |
|---|
| 30 | Note that the empty datasets use a minimal representation of the SasView |
|---|
| 31 | objects so that models can be run without SasView on the path. You could |
|---|
| 32 | also use these for your own data loader. |
|---|
| 33 | |
|---|
| 34 | """ |
|---|
| 35 | import traceback |
|---|
| 36 | |
|---|
| 37 | import numpy as np |
|---|
| 38 | |
|---|
| 39 | def load_data(filename): |
|---|
| 40 | """ |
|---|
| 41 | Load data using a sasview loader. |
|---|
| 42 | """ |
|---|
| 43 | from sas.dataloader.loader import Loader |
|---|
| 44 | loader = Loader() |
|---|
| 45 | data = loader.load(filename) |
|---|
| 46 | if data is None: |
|---|
| 47 | raise IOError("Data %r could not be loaded" % filename) |
|---|
| 48 | return data |
|---|
| 49 | |
|---|
| 50 | |
|---|
| 51 | def set_beam_stop(data, radius, outer=None): |
|---|
| 52 | """ |
|---|
| 53 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
|---|
| 54 | """ |
|---|
| 55 | from sas.dataloader.manipulations import Ringcut |
|---|
| 56 | if hasattr(data, 'qx_data'): |
|---|
| 57 | data.mask = Ringcut(0, radius)(data) |
|---|
| 58 | if outer is not None: |
|---|
| 59 | data.mask += Ringcut(outer, np.inf)(data) |
|---|
| 60 | else: |
|---|
| 61 | data.mask = (data.x < radius) |
|---|
| 62 | if outer is not None: |
|---|
| 63 | data.mask |= (data.x >= outer) |
|---|
| 64 | |
|---|
| 65 | |
|---|
| 66 | def set_half(data, half): |
|---|
| 67 | """ |
|---|
| 68 | Select half of the data, either "right" or "left". |
|---|
| 69 | """ |
|---|
| 70 | from sas.dataloader.manipulations import Boxcut |
|---|
| 71 | if half == 'right': |
|---|
| 72 | data.mask += \ |
|---|
| 73 | Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
|---|
| 74 | if half == 'left': |
|---|
| 75 | data.mask += \ |
|---|
| 76 | Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
|---|
| 77 | |
|---|
| 78 | |
|---|
| 79 | def set_top(data, cutoff): |
|---|
| 80 | """ |
|---|
| 81 | Chop the top off the data, above *cutoff*. |
|---|
| 82 | """ |
|---|
| 83 | from sas.dataloader.manipulations import Boxcut |
|---|
| 84 | data.mask += \ |
|---|
| 85 | Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=cutoff)(data) |
|---|
| 86 | |
|---|
| 87 | |
|---|
| 88 | class Data1D(object): |
|---|
| 89 | def __init__(self, x=None, y=None, dx=None, dy=None): |
|---|
| 90 | self.x, self.y, self.dx, self.dy = x, y, dx, dy |
|---|
| 91 | self.dxl = None |
|---|
| 92 | self.filename = None |
|---|
| 93 | self.qmin = x.min() if x is not None else np.NaN |
|---|
| 94 | self.qmax = x.max() if x is not None else np.NaN |
|---|
| 95 | # TODO: why is 1D mask False and 2D mask True? |
|---|
| 96 | self.mask = (np.isnan(y) if y is not None |
|---|
| 97 | else np.zeros_like(x,'b') if x is not None |
|---|
| 98 | else None) |
|---|
| 99 | self._xaxis, self._xunit = "x", "" |
|---|
| 100 | self._yaxis, self._yunit = "y", "" |
|---|
| 101 | |
|---|
| 102 | def xaxis(self, label, unit): |
|---|
| 103 | """ |
|---|
| 104 | set the x axis label and unit |
|---|
| 105 | """ |
|---|
| 106 | self._xaxis = label |
|---|
| 107 | self._xunit = unit |
|---|
| 108 | |
|---|
| 109 | def yaxis(self, label, unit): |
|---|
| 110 | """ |
|---|
| 111 | set the y axis label and unit |
|---|
| 112 | """ |
|---|
| 113 | self._yaxis = label |
|---|
| 114 | self._yunit = unit |
|---|
| 115 | |
|---|
| 116 | |
|---|
| 117 | |
|---|
| 118 | class Data2D(object): |
|---|
| 119 | def __init__(self, x=None, y=None, z=None, dx=None, dy=None, dz=None): |
|---|
| 120 | self.qx_data, self.dqx_data = x, dx |
|---|
| 121 | self.qy_data, self.dqy_data = y, dy |
|---|
| 122 | self.data, self.err_data = z, dz |
|---|
| 123 | self.mask = (~np.isnan(z) if z is not None |
|---|
| 124 | else np.ones_like(x) if x is not None |
|---|
| 125 | else None) |
|---|
| 126 | self.q_data = np.sqrt(x**2 + y**2) |
|---|
| 127 | self.qmin = 1e-16 |
|---|
| 128 | self.qmax = np.inf |
|---|
| 129 | self.detector = [] |
|---|
| 130 | self.source = Source() |
|---|
| 131 | self.Q_unit = "1/A" |
|---|
| 132 | self.I_unit = "1/cm" |
|---|
| 133 | self.xaxis("Q_x", "A^{-1}") |
|---|
| 134 | self.yaxis("Q_y", "A^{-1}") |
|---|
| 135 | self.zaxis("Intensity", r"\text{cm}^{-1}") |
|---|
| 136 | self._xaxis, self._xunit = "x", "" |
|---|
| 137 | self._yaxis, self._yunit = "y", "" |
|---|
| 138 | self._zaxis, self._zunit = "z", "" |
|---|
| 139 | self.x_bins, self.y_bins = None, None |
|---|
| 140 | |
|---|
| 141 | def xaxis(self, label, unit): |
|---|
| 142 | """ |
|---|
| 143 | set the x axis label and unit |
|---|
| 144 | """ |
|---|
| 145 | self._xaxis = label |
|---|
| 146 | self._xunit = unit |
|---|
| 147 | |
|---|
| 148 | def yaxis(self, label, unit): |
|---|
| 149 | """ |
|---|
| 150 | set the y axis label and unit |
|---|
| 151 | """ |
|---|
| 152 | self._yaxis = label |
|---|
| 153 | self._yunit = unit |
|---|
| 154 | |
|---|
| 155 | def zaxis(self, label, unit): |
|---|
| 156 | """ |
|---|
| 157 | set the y axis label and unit |
|---|
| 158 | """ |
|---|
| 159 | self._zaxis = label |
|---|
| 160 | self._zunit = unit |
|---|
| 161 | |
|---|
| 162 | |
|---|
| 163 | class Vector(object): |
|---|
| 164 | def __init__(self, x=None, y=None, z=None): |
|---|
| 165 | self.x, self.y, self.z = x, y, z |
|---|
| 166 | |
|---|
| 167 | class Detector(object): |
|---|
| 168 | """ |
|---|
| 169 | Detector attributes. |
|---|
| 170 | """ |
|---|
| 171 | def __init__(self, pixel_size=(None, None), distance=None): |
|---|
| 172 | self.pixel_size = Vector(*pixel_size) |
|---|
| 173 | self.distance = distance |
|---|
| 174 | |
|---|
| 175 | class Source(object): |
|---|
| 176 | """ |
|---|
| 177 | Beam attributes. |
|---|
| 178 | """ |
|---|
| 179 | def __init__(self): |
|---|
| 180 | self.wavelength = np.NaN |
|---|
| 181 | self.wavelength_unit = "A" |
|---|
| 182 | |
|---|
| 183 | |
|---|
| 184 | def empty_data1D(q, resolution=0.05): |
|---|
| 185 | """ |
|---|
| 186 | Create empty 1D data using the given *q* as the x value. |
|---|
| 187 | |
|---|
| 188 | *resolution* dq/q defaults to 5%. |
|---|
| 189 | """ |
|---|
| 190 | |
|---|
| 191 | #Iq = 100 * np.ones_like(q) |
|---|
| 192 | #dIq = np.sqrt(Iq) |
|---|
| 193 | Iq, dIq = None, None |
|---|
| 194 | data = Data1D(q, Iq, dx=resolution * q, dy=dIq) |
|---|
| 195 | data.filename = "fake data" |
|---|
| 196 | return data |
|---|
| 197 | |
|---|
| 198 | |
|---|
| 199 | def empty_data2D(qx, qy=None, resolution=0.05): |
|---|
| 200 | """ |
|---|
| 201 | Create empty 2D data using the given mesh. |
|---|
| 202 | |
|---|
| 203 | If *qy* is missing, create a square mesh with *qy=qx*. |
|---|
| 204 | |
|---|
| 205 | *resolution* dq/q defaults to 5%. |
|---|
| 206 | """ |
|---|
| 207 | if qy is None: |
|---|
| 208 | qy = qx |
|---|
| 209 | # 5% dQ/Q resolution |
|---|
| 210 | Qx, Qy = np.meshgrid(qx, qy) |
|---|
| 211 | Qx, Qy = Qx.flatten(), Qy.flatten() |
|---|
| 212 | Iq = 100 * np.ones_like(Qx) |
|---|
| 213 | dIq = np.sqrt(Iq) |
|---|
| 214 | if resolution != 0: |
|---|
| 215 | # https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf |
|---|
| 216 | # Should have an additional constant which depends on distances and |
|---|
| 217 | # radii of the aperture, pixel dimensions and wavelength spread |
|---|
| 218 | # Instead, assume radial dQ/Q is constant, and perpendicular matches |
|---|
| 219 | # radial (which instead it should be inverse). |
|---|
| 220 | Q = np.sqrt(Qx**2 + Qy**2) |
|---|
| 221 | dqx = resolution * Q |
|---|
| 222 | dqy = resolution * Q |
|---|
| 223 | else: |
|---|
| 224 | dqx = dqy = None |
|---|
| 225 | |
|---|
| 226 | data = Data2D(x=Qx, y=Qy, z=Iq, dx=dqx, dy=dqy, dz=dIq) |
|---|
| 227 | data.x_bins = qx |
|---|
| 228 | data.y_bins = qy |
|---|
| 229 | data.filename = "fake data" |
|---|
| 230 | |
|---|
| 231 | # pixel_size in mm, distance in m |
|---|
| 232 | detector = Detector(pixel_size=(5, 5), distance=4) |
|---|
| 233 | data.detector.append(detector) |
|---|
| 234 | data.source.wavelength = 5 # angstroms |
|---|
| 235 | data.source.wavelength_unit = "A" |
|---|
| 236 | return data |
|---|
| 237 | |
|---|
| 238 | |
|---|
| 239 | def plot_data(data, view='log', limits=None): |
|---|
| 240 | """ |
|---|
| 241 | Plot data loaded by the sasview loader. |
|---|
| 242 | """ |
|---|
| 243 | # Note: kind of weird using the plot result functions to plot just the |
|---|
| 244 | # data, but they already handle the masking and graph markup already, so |
|---|
| 245 | # do not repeat. |
|---|
| 246 | if hasattr(data, 'lam'): |
|---|
| 247 | _plot_result_sesans(data, None, None, use_data=True, limits=limits) |
|---|
| 248 | elif hasattr(data, 'qx_data'): |
|---|
| 249 | _plot_result2D(data, None, None, view, use_data=True, limits=limits) |
|---|
| 250 | else: |
|---|
| 251 | _plot_result1D(data, None, None, view, use_data=True, limits=limits) |
|---|
| 252 | |
|---|
| 253 | |
|---|
| 254 | def plot_theory(data, theory, resid=None, view='log', |
|---|
| 255 | use_data=True, limits=None): |
|---|
| 256 | if hasattr(data, 'lam'): |
|---|
| 257 | _plot_result_sesans(data, theory, resid, use_data=True, limits=limits) |
|---|
| 258 | elif hasattr(data, 'qx_data'): |
|---|
| 259 | _plot_result2D(data, theory, resid, view, use_data, limits=limits) |
|---|
| 260 | else: |
|---|
| 261 | _plot_result1D(data, theory, resid, view, use_data, limits=limits) |
|---|
| 262 | |
|---|
| 263 | |
|---|
| 264 | def protect(fn): |
|---|
| 265 | def wrapper(*args, **kw): |
|---|
| 266 | try: |
|---|
| 267 | return fn(*args, **kw) |
|---|
| 268 | except: |
|---|
| 269 | traceback.print_exc() |
|---|
| 270 | |
|---|
| 271 | return wrapper |
|---|
| 272 | |
|---|
| 273 | |
|---|
| 274 | @protect |
|---|
| 275 | def _plot_result1D(data, theory, resid, view, use_data, limits=None): |
|---|
| 276 | """ |
|---|
| 277 | Plot the data and residuals for 1D data. |
|---|
| 278 | """ |
|---|
| 279 | import matplotlib.pyplot as plt |
|---|
| 280 | from numpy.ma import masked_array, masked |
|---|
| 281 | |
|---|
| 282 | use_data = use_data and data.y is not None |
|---|
| 283 | use_theory = theory is not None |
|---|
| 284 | use_resid = resid is not None |
|---|
| 285 | num_plots = (use_data or use_theory) + use_resid |
|---|
| 286 | |
|---|
| 287 | scale = data.x**4 if view == 'q4' else 1.0 |
|---|
| 288 | |
|---|
| 289 | if use_data or use_theory: |
|---|
| 290 | #print(vmin, vmax) |
|---|
| 291 | all_positive = True |
|---|
| 292 | some_present = False |
|---|
| 293 | if use_data: |
|---|
| 294 | mdata = masked_array(data.y, data.mask.copy()) |
|---|
| 295 | mdata[~np.isfinite(mdata)] = masked |
|---|
| 296 | if view is 'log': |
|---|
| 297 | mdata[mdata <= 0] = masked |
|---|
| 298 | plt.errorbar(data.x/10, scale*mdata, yerr=data.dy, fmt='.') |
|---|
| 299 | all_positive = all_positive and (mdata > 0).all() |
|---|
| 300 | some_present = some_present or (mdata.count() > 0) |
|---|
| 301 | |
|---|
| 302 | |
|---|
| 303 | if use_theory: |
|---|
| 304 | mtheory = masked_array(theory, data.mask.copy()) |
|---|
| 305 | mtheory[~np.isfinite(mtheory)] = masked |
|---|
| 306 | if view is 'log': |
|---|
| 307 | mtheory[mtheory <= 0] = masked |
|---|
| 308 | plt.plot(data.x/10, scale*mtheory, '-', hold=True) |
|---|
| 309 | all_positive = all_positive and (mtheory > 0).all() |
|---|
| 310 | some_present = some_present or (mtheory.count() > 0) |
|---|
| 311 | |
|---|
| 312 | if limits is not None: |
|---|
| 313 | plt.ylim(*limits) |
|---|
| 314 | |
|---|
| 315 | if num_plots > 1: |
|---|
| 316 | plt.subplot(1, num_plots, 1) |
|---|
| 317 | plt.xscale('linear' if not some_present else view) |
|---|
| 318 | plt.yscale('linear' |
|---|
| 319 | if view == 'q4' or not some_present or not all_positive |
|---|
| 320 | else view) |
|---|
| 321 | plt.xlabel("$q$/nm$^{-1}$") |
|---|
| 322 | plt.ylabel('$I(q)$') |
|---|
| 323 | |
|---|
| 324 | if use_resid: |
|---|
| 325 | mresid = masked_array(resid, data.mask.copy()) |
|---|
| 326 | mresid[~np.isfinite(mresid)] = masked |
|---|
| 327 | some_present = (mresid.count() > 0) |
|---|
| 328 | |
|---|
| 329 | if num_plots > 1: |
|---|
| 330 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
|---|
| 331 | plt.plot(data.x/10, mresid, '-') |
|---|
| 332 | plt.xlabel("$q$/nm$^{-1}$") |
|---|
| 333 | plt.ylabel('residuals') |
|---|
| 334 | plt.xscale('linear' if not some_present else view) |
|---|
| 335 | |
|---|
| 336 | |
|---|
| 337 | @protect |
|---|
| 338 | def _plot_result_sesans(data, theory, resid, use_data, limits=None): |
|---|
| 339 | import matplotlib.pyplot as plt |
|---|
| 340 | use_data = use_data and data.y is not None |
|---|
| 341 | use_theory = theory is not None |
|---|
| 342 | use_resid = resid is not None |
|---|
| 343 | num_plots = (use_data or use_theory) + use_resid |
|---|
| 344 | |
|---|
| 345 | if use_data or use_theory: |
|---|
| 346 | if num_plots > 1: |
|---|
| 347 | plt.subplot(1, num_plots, 1) |
|---|
| 348 | if use_data: |
|---|
| 349 | plt.errorbar(data.x, data.y, yerr=data.dy) |
|---|
| 350 | if theory is not None: |
|---|
| 351 | plt.plot(data.x, theory, '-', hold=True) |
|---|
| 352 | if limits is not None: |
|---|
| 353 | plt.ylim(*limits) |
|---|
| 354 | plt.xlabel('spin echo length (nm)') |
|---|
| 355 | plt.ylabel('polarization (P/P0)') |
|---|
| 356 | |
|---|
| 357 | if resid is not None: |
|---|
| 358 | if num_plots > 1: |
|---|
| 359 | plt.subplot(1, num_plots, (use_data or use_theory) + 1) |
|---|
| 360 | plt.plot(data.x, resid, 'x') |
|---|
| 361 | plt.xlabel('spin echo length (nm)') |
|---|
| 362 | plt.ylabel('residuals (P/P0)') |
|---|
| 363 | |
|---|
| 364 | |
|---|
| 365 | @protect |
|---|
| 366 | def _plot_result2D(data, theory, resid, view, use_data, limits=None): |
|---|
| 367 | """ |
|---|
| 368 | Plot the data and residuals for 2D data. |
|---|
| 369 | """ |
|---|
| 370 | import matplotlib.pyplot as plt |
|---|
| 371 | use_data = use_data and data.data is not None |
|---|
| 372 | use_theory = theory is not None |
|---|
| 373 | use_resid = resid is not None |
|---|
| 374 | num_plots = use_data + use_theory + use_resid |
|---|
| 375 | |
|---|
| 376 | # Put theory and data on a common colormap scale |
|---|
| 377 | vmin, vmax = np.inf, -np.inf |
|---|
| 378 | if use_data: |
|---|
| 379 | target = data.data[~data.mask] |
|---|
| 380 | datamin = target[target > 0].min() if view == 'log' else target.min() |
|---|
| 381 | datamax = target.max() |
|---|
| 382 | vmin = min(vmin, datamin) |
|---|
| 383 | vmax = max(vmax, datamax) |
|---|
| 384 | if use_theory: |
|---|
| 385 | theorymin = theory[theory > 0].min() if view == 'log' else theory.min() |
|---|
| 386 | theorymax = theory.max() |
|---|
| 387 | vmin = min(vmin, theorymin) |
|---|
| 388 | vmax = max(vmax, theorymax) |
|---|
| 389 | |
|---|
| 390 | # Override data limits from the caller |
|---|
| 391 | if limits is not None: |
|---|
| 392 | vmin, vmax = limits |
|---|
| 393 | |
|---|
| 394 | # Plot data |
|---|
| 395 | if use_data: |
|---|
| 396 | if num_plots > 1: |
|---|
| 397 | plt.subplot(1, num_plots, 1) |
|---|
| 398 | _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) |
|---|
| 399 | plt.title('data') |
|---|
| 400 | h = plt.colorbar() |
|---|
| 401 | h.set_label('$I(q)$') |
|---|
| 402 | |
|---|
| 403 | # plot theory |
|---|
| 404 | if use_theory: |
|---|
| 405 | if num_plots > 1: |
|---|
| 406 | plt.subplot(1, num_plots, use_data+1) |
|---|
| 407 | _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) |
|---|
| 408 | plt.title('theory') |
|---|
| 409 | h = plt.colorbar() |
|---|
| 410 | h.set_label(r'$\log_{10}I(q)$' if view == 'log' |
|---|
| 411 | else r'$q^4 I(q)$' if view == 'q4' |
|---|
| 412 | else '$I(q)$') |
|---|
| 413 | |
|---|
| 414 | # plot resid |
|---|
| 415 | if use_resid: |
|---|
| 416 | if num_plots > 1: |
|---|
| 417 | plt.subplot(1, num_plots, use_data+use_theory+1) |
|---|
| 418 | _plot_2d_signal(data, resid, view='linear') |
|---|
| 419 | plt.title('residuals') |
|---|
| 420 | h = plt.colorbar() |
|---|
| 421 | h.set_label(r'$\Delta I(q)$') |
|---|
| 422 | |
|---|
| 423 | |
|---|
| 424 | @protect |
|---|
| 425 | def _plot_2d_signal(data, signal, vmin=None, vmax=None, view='log'): |
|---|
| 426 | """ |
|---|
| 427 | Plot the target value for the data. This could be the data itself, |
|---|
| 428 | the theory calculation, or the residuals. |
|---|
| 429 | |
|---|
| 430 | *scale* can be 'log' for log scale data, or 'linear'. |
|---|
| 431 | """ |
|---|
| 432 | import matplotlib.pyplot as plt |
|---|
| 433 | from numpy.ma import masked_array |
|---|
| 434 | |
|---|
| 435 | image = np.zeros_like(data.qx_data) |
|---|
| 436 | image[~data.mask] = signal |
|---|
| 437 | valid = np.isfinite(image) |
|---|
| 438 | if view == 'log': |
|---|
| 439 | valid[valid] = (image[valid] > 0) |
|---|
| 440 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 441 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 442 | image[valid] = np.log10(image[valid]) |
|---|
| 443 | elif view == 'q4': |
|---|
| 444 | image[valid] *= (data.qx_data[valid]**2+data.qy_data[valid]**2)**2 |
|---|
| 445 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 446 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 447 | else: |
|---|
| 448 | if vmin is None: vmin = image[valid & ~data.mask].min() |
|---|
| 449 | if vmax is None: vmax = image[valid & ~data.mask].max() |
|---|
| 450 | |
|---|
| 451 | image[~valid | data.mask] = 0 |
|---|
| 452 | #plottable = Iq |
|---|
| 453 | plottable = masked_array(image, ~valid | data.mask) |
|---|
| 454 | xmin, xmax = min(data.qx_data)/10, max(data.qx_data)/10 |
|---|
| 455 | ymin, ymax = min(data.qy_data)/10, max(data.qy_data)/10 |
|---|
| 456 | if view == 'log': |
|---|
| 457 | vmin, vmax = np.log10(vmin), np.log10(vmax) |
|---|
| 458 | plt.imshow(plottable.reshape(len(data.x_bins), len(data.y_bins)), |
|---|
| 459 | interpolation='nearest', aspect=1, origin='upper', |
|---|
| 460 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
|---|
| 461 | plt.xlabel("$q_x$/nm$^{-1}$") |
|---|
| 462 | plt.ylabel("$q_y$/nm$^{-1}$") |
|---|
| 463 | return vmin, vmax |
|---|
| 464 | |
|---|
| 465 | def demo(): |
|---|
| 466 | data = load_data('DEC07086.DAT') |
|---|
| 467 | set_beam_stop(data, 0.004) |
|---|
| 468 | plot_data(data) |
|---|
| 469 | import matplotlib.pyplot as plt; plt.show() |
|---|
| 470 | |
|---|
| 471 | |
|---|
| 472 | if __name__ == "__main__": |
|---|
| 473 | demo() |
|---|
