Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation of quantiles. virtual_indexes : array_like The indexes where the percentile is supposed to be found in the sorted sample. previous_indexes : array_like The floor values of virtual_indexes.
(virtual_indexes, previous_indexes, method)
| 4617 | |
| 4618 | |
| 4619 | def _get_gamma(virtual_indexes, previous_indexes, method): |
| 4620 | """ |
| 4621 | Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation |
| 4622 | of quantiles. |
| 4623 | |
| 4624 | virtual_indexes : array_like |
| 4625 | The indexes where the percentile is supposed to be found in the sorted |
| 4626 | sample. |
| 4627 | previous_indexes : array_like |
| 4628 | The floor values of virtual_indexes. |
| 4629 | interpolation : dict |
| 4630 | The interpolation method chosen, which may have a specific rule |
| 4631 | modifying gamma. |
| 4632 | |
| 4633 | gamma is usually the fractional part of virtual_indexes but can be modified |
| 4634 | by the interpolation method. |
| 4635 | """ |
| 4636 | gamma = np.asanyarray(virtual_indexes - previous_indexes) |
| 4637 | gamma = method["fix_gamma"](gamma, virtual_indexes) |
| 4638 | return np.asanyarray(gamma) |
| 4639 | |
| 4640 | |
| 4641 | def _lerp(a, b, t, out=None): |