Views provided by UsageCounts
This is the beta-release of Parcels v2. Compared to the last v1.1.1 release, there are three important changes 1) The order of arguments for Field interpolation has changed. This is now field[time, depth, lat, lon], which is consistent with the dimension order in which data is stored in the field.data numpy array (#503 and #276). 2) The dt argument has been dropped from Kernel definitions, so that the only arguments allowed in a Kernel are def kernelfunc(fieldset, particle, time) (#503) 3) Interpolation for C-grids is now done in a fluxes framework, instead of a velocity framework. The details of this will be presented in a manuscript, to be submitted soon (#499 and #494) Note that 1) and 2) above mean that Kernels written for Parcels v1 will break in this Parcels v2. If you're updating to this v2.0.0beta, therefore please update your custom Kernels. Other updates since v1.1.1 are: New FieldSet.from_xarray_dataset() method to directly read xarray.DataSet objects (#476) An optional argument in Field.show() to control which depth level to plot (#478) ParticleSet.from_field() now also implemented for Curvilinear Fields (#496) And numerous small bug fixes
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 7 |

Views provided by UsageCounts