Returns the standard deviation of all values along a particular axis (dimension). Given an array of m tuples and n components: * Default is to return the standard deviation of all values in an array. * axis=0: Return the standard deviation values of all components and return a
(array, axis=None, controller=None)
| 359 | |
| 360 | @dsa._override_numpy(numpy.std) |
| 361 | def std(array, axis=None, controller=None): |
| 362 | """Returns the standard deviation of all values along a particular |
| 363 | axis (dimension). |
| 364 | Given an array of m tuples and n components: |
| 365 | * Default is to return the standard deviation of all values in an array. |
| 366 | * axis=0: Return the standard deviation values of all components and |
| 367 | return a one tuple, n-component array. |
| 368 | * axis=1: Return the standard deviation values of all components of |
| 369 | each tuple and return an m-tuple, 1-component array. |
| 370 | |
| 371 | When called in parallel, this function will compute the standard deviation |
| 372 | across processes when a controller argument is passed or the global controller |
| 373 | is defined. To disable parallel summing when running in parallel, pass a dummy |
| 374 | controller as follows: |
| 375 | |
| 376 | std(array, controller=vtkmodules.vtkParallelCore.vtkDummyController()). |
| 377 | """ |
| 378 | from .algorithms import sqrt |
| 379 | return sqrt(var(array, axis, controller)) |
| 380 | |
| 381 | @dsa._override_numpy(numpy.shape) |
| 382 | def shape(array): |