Returns the mean of all values along a particular axis (dimension). Given an array of m tuples and n components: * Default is to return the mean of all values in an array. * axis=0: Return the mean values of all components and return a one tuple, n-component array. * axis=1: Re
(array, axis=None, controller=None, size=None)
| 303 | |
| 304 | @dsa._override_numpy(numpy.mean) |
| 305 | def mean(array, axis=None, controller=None, size=None): |
| 306 | """Returns the mean of all values along a particular axis (dimension). |
| 307 | Given an array of m tuples and n components: |
| 308 | * Default is to return the mean of all values in an array. |
| 309 | * axis=0: Return the mean values of all components and return a one |
| 310 | tuple, n-component array. |
| 311 | * axis=1: Return the mean values of all components of each tuple and |
| 312 | return an m-tuple, 1-component array. |
| 313 | |
| 314 | When called in parallel, this function will compute the mean across |
| 315 | processes when a controller argument is passed or the global controller |
| 316 | is defined. To disable parallel summing when running in parallel, pass a |
| 317 | dummy controller as follows: |
| 318 | |
| 319 | mean(array, controller=vtkmodules.vtkParallelCore.vtkDummyController()). |
| 320 | """ |
| 321 | from .algorithms import array_count |
| 322 | if axis is None or axis == 0: |
| 323 | if size is None: |
| 324 | size = array_count(array, axis, controller) |
| 325 | return sum(array, axis, controller) / size |
| 326 | else: |
| 327 | if type(array) == dsa.VTKCompositeDataArray: |
| 328 | return apply_ufunc(algs.mean, array, (axis,)) |
| 329 | else: |
| 330 | return algs.mean(array, axis) |
| 331 | |
| 332 | @dsa._override_numpy(numpy.var) |
| 333 | def var(array, axis=None, controller=None): |
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