Forward nD multiresolution analysis. It is a projection onto the wavelet subspaces. Parameters ---------- data: array_like Input data wavelet : Wavelet object or name string, or tuple of wavelets Wavelet to use. This can also be a tuple containing a wavelet to
(data, wavelet, level=None, axes=None, transform='swtn',
mode='periodization')
| 288 | |
| 289 | |
| 290 | def mran(data, wavelet, level=None, axes=None, transform='swtn', |
| 291 | mode='periodization'): |
| 292 | """Forward nD multiresolution analysis. |
| 293 | |
| 294 | It is a projection onto the wavelet subspaces. |
| 295 | |
| 296 | Parameters |
| 297 | ---------- |
| 298 | data: array_like |
| 299 | Input data |
| 300 | wavelet : Wavelet object or name string, or tuple of wavelets |
| 301 | Wavelet to use. This can also be a tuple containing a wavelet to |
| 302 | apply along each axis in `axes`. |
| 303 | level : int, optional |
| 304 | Decomposition level (must be >= 0). If level is None (default) then it |
| 305 | will be calculated using the `dwt_max_level` function. |
| 306 | axes : tuple of ints, optional |
| 307 | Axes over which to compute the DWT. Repeated elements are not allowed. |
| 308 | transform : {'dwtn', 'swtn'} |
| 309 | Whether to use the DWT or SWT for the transforms. |
| 310 | mode : str or tuple of str, optional |
| 311 | Signal extension mode, see `Modes` (default: 'symmetric'). This option |
| 312 | is only used when transform='dwtn'. |
| 313 | |
| 314 | Returns |
| 315 | ------- |
| 316 | coeffs : list |
| 317 | For more information, see the detailed description in `wavedecn`. |
| 318 | |
| 319 | See Also |
| 320 | -------- |
| 321 | imran, swtn |
| 322 | |
| 323 | Notes |
| 324 | ----- |
| 325 | This is sometimes referred to as an additive decomposition because the |
| 326 | inverse transform (``imran``) is just the sum of the coefficient arrays |
| 327 | [1]_. The decomposition using ``transform='dwt'`` corresponds to section |
| 328 | 2.2 while that using an undecimated transform (``transform='swt'``) is |
| 329 | described in section 3.2 and appendix A. |
| 330 | |
| 331 | This transform does not share the variance partition property of ``swtn`` |
| 332 | with `norm=True`. It does however, result in coefficients that are |
| 333 | temporally aligned regardless of the symmetry of the wavelet used. |
| 334 | |
| 335 | The redundancy of this transform is ``(2**n - 1) * level + 1`` where ``n`` |
| 336 | corresponds to the number of axes transformed. |
| 337 | |
| 338 | References |
| 339 | ---------- |
| 340 | .. [1] Donald B. Percival and Harold O. Mofjeld. Analysis of Subtidal |
| 341 | Coastal Sea Level Fluctuations Using Wavelets. Journal of the American |
| 342 | Statistical Association Vol. 92, No. 439 (Sep., 1997), pp. 868-880. |
| 343 | https://doi.org/10.2307/2965551 |
| 344 | """ |
| 345 | axes, axes_shapes, ndim_transform = _prep_axes_wavedecn(data.shape, axes) |
| 346 | wavelets = _wavelets_per_axis(wavelet, axes) |
| 347 |
nothing calls this directly
no test coverage detected