Compute the anomalies (deviations from the arithmetic mean) along the given axis. Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis. Parameters ---------- axis : in
(self, axis=None, dtype=None)
| 5430 | return result |
| 5431 | |
| 5432 | def anom(self, axis=None, dtype=None): |
| 5433 | """ |
| 5434 | Compute the anomalies (deviations from the arithmetic mean) |
| 5435 | along the given axis. |
| 5436 | |
| 5437 | Returns an array of anomalies, with the same shape as the input and |
| 5438 | where the arithmetic mean is computed along the given axis. |
| 5439 | |
| 5440 | Parameters |
| 5441 | ---------- |
| 5442 | axis : int, optional |
| 5443 | Axis over which the anomalies are taken. |
| 5444 | The default is to use the mean of the flattened array as reference. |
| 5445 | dtype : dtype, optional |
| 5446 | Type to use in computing the variance. For arrays of integer type |
| 5447 | the default is float32; for arrays of float types it is the same as |
| 5448 | the array type. |
| 5449 | |
| 5450 | See Also |
| 5451 | -------- |
| 5452 | mean : Compute the mean of the array. |
| 5453 | |
| 5454 | Examples |
| 5455 | -------- |
| 5456 | >>> import numpy as np |
| 5457 | >>> a = np.ma.array([1,2,3]) |
| 5458 | >>> a.anom() |
| 5459 | masked_array(data=[-1., 0., 1.], |
| 5460 | mask=False, |
| 5461 | fill_value=1e+20) |
| 5462 | |
| 5463 | """ |
| 5464 | m = self.mean(axis, dtype) |
| 5465 | if not axis: |
| 5466 | return self - m |
| 5467 | else: |
| 5468 | return self - expand_dims(m, axis) |
| 5469 | |
| 5470 | def var(self, axis=None, dtype=None, out=None, ddof=0, |
| 5471 | keepdims=np._NoValue, mean=np._NoValue): |