Releases: OceanParcels/Parcels
Parcels v2.1.3: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v2.1.3 is an update mostly focussing on improved memory behaviour when doing large simulations. In particular
- It fixes some serious issues with memory usage for field chunking (see #668, #703 and #711), which have now been addressed in #719
- Fixing xarray compatibility (#716)
- Fixing a bug in cartopy.quiver (#714)
- New kernels to compute density (#688)
And various other minor bug fixes
Parcels v2.1.2: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v2.1.2 is a quick release build on previous versions v2.1.1. In particular:
- It fixes a serious bug on some systems with floating point accuracy, that could lead to incorrect output files (see #670 and solution at #672). For this reason, all users are encouraged to use this new version v2.1.2 instead of v2.1.1.
- It also fixes a smaller bug (#676) with the
timestamps
argument inField.from_netcdf()
Parcels v2.1.1: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v2.1.1 builds on previous versions v2.0.0. The major changes of v2.1.1 are:
[Note that v2.1.1 comes after a botched release of v2.1.0]
- Parcels has a parallel MPI version! While working on multiple processors, the particles are spread over the processors for an efficient integration. (#625). See https://oceanparcels.org#parallel_install for instructions on how to install.
- For an efficient loading of the
Fieldset
, theField
objects are now loaded by chunks, controlled by the parameterfield_chunksize
(#632). This results in lower memory usage and faster simulation. It is also a fundamental part of the parallel implementation, since for low number of particles per processor, the computation time is dominated by the loading of the data. A more efficient parallel version will be dynamically balancing the particles between the processors such to minimise the number of chunks loaded per processor. See this document for further background on the implementation. - An efficient writing of the particleset. For a quicker export of the data, particles are now dumped into npy files during simulation. The pickles are gathered into one single file at the end of the simulation. (#614)
- A proper management of
particle.dt
modified by the kernel. If the kernel modifiesparticle.dt
, the kernel will automatically be restarted with the updateddt
. If you want to simply updates thedt
for next kernel call, useparticle.update_next_dt(new_dt)
. (#657) - New particles can now be added to the
ParticleSet
only via a temporaryParticleSet
object. This enables a proper control of theparticle.id
in parallel (#629) Field.gradient()
function is not available anymore. This functionality was providing spurious results on curvilinear grids and was conflicting with the use of chunked fields. Users can still obtain easily an accurate field gradient (see example proposed in #633)- Using the
time_periodic
flag inFieldSet
creation now requires the length of the period (#659) - Numerous bug fixes
Note that Parcels v2.1.1 is the last version to officially support Python 2.7. While all functionalities currently work with both Python 2 and 3, new development and code dependencies will progressively lead to incompatibility with Python 2. We strongly advice the users to switch to Python 3.
Parcels v2.1.0: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v2.1.0 builds on previous versions v2.0.0. The major changes of v2.1.0 are:
- Parcels has a parallel MPI version! While working on multiple processors, the particles are spread over the processors for an efficient integration. (#625). See https://oceanparcels.org#parallel_install for instructions on how to install.
- For an efficient loading of the
Fieldset
, theField
objects are now loaded by chunks, controlled by the parameterfield_chunksize
(#632). This results in lower memory usage and faster simulation. It is also a fundamental part of the parallel implementation, since for low number of particles per processor, the computation time is dominated by the loading of the data. A more efficient parallel version will be dynamically balancing the particles between the processors such to minimise the number of chunks loaded per processor. See this document for further background on the implementation. - An efficient writing of the particleset. For a quicker export of the data, particles are now dumped into npy files during simulation. The pickles are gathered into one single file at the end of the simulation. (#614)
- A proper management of
particle.dt
modified by the kernel. If the kernel modifiesparticle.dt
, the kernel will automatically be restarted with the updateddt
. If you want to simply updates thedt
for next kernel call, useparticle.update_next_dt(new_dt)
. (#657) - New particles can now be added to the
ParticleSet
only via a temporaryParticleSet
object. This enables a proper control of theparticle.id
in parallel (#629) Field.gradient()
function is not available anymore. This functionality was providing spurious results on curvilinear grids and was conflicting with the use of chunked fields. Users can still obtain easily an accurate field gradient (see example proposed in #633)- Using the
time_periodic
flag inFieldSet
creation now requires the length of the period (#659) - Numerous bug fixes
Note that Parcels v2.1.0 is the last version to officially support Python 2.7. While all functionalities currently work with both Python 2 and 3, new development and code dependencies will progressively lead to incompatibility with Python 2. We strongly advice the users to switch to Python 3.
Parcels v2.0.0: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v2.0.0 builds on previous versions v2.0.0.beta and v2.0.0.beta2. It's the release which is fully described in the paper The Parcels v2.0 Lagrangian framework: new field interpolation schemes, by Delandmeter and van Sebille, 2019, GMD.
The major changes of v2.0.0 compared to v1.1.1 are
-
The order of arguments for
Field
interpolation has changed. This is nowfield[time, depth, lat, lon]
, which is consistent with the dimension order in which data is stored in thefield.data
numpy array (#503 and #276). -
The
dt
argument has been dropped from Kernel definitions, so that the only arguments allowed in a Kernel aredef kernelfunc(fieldset, particle, time)
(#503) -
Interpolation for C-grids is now done in a fluxes framework, instead of a velocity framework (#499 and #494).
-
Interpolation for B-grids (#573)
-
Support for
np.float64
accuracy of particle locations. This can be set using thelonlatdepth_dtype
argument in ParticleSet construction. Default isnp.float64
for C-grids, andnp.float32
for all other grids (#552 and #557)
Note also a number of other minor development:
- See v2.0.0.beta
- See v2.0.0.beta2
- Unpinning netcdf4 1.4.1 (#597)
- Numerous bug fixes
Parcels v2.0.0-beta2: a Lagrangian Ocean Analysis tool for the petascale age
This is the second beta version of Parcels v2.0.0. It builds upon v2.0.0beta, and compared to that version has a few important fixes and improvements:
- Support for
np.float64
accuracy of particle locations. This can be set using thelonlatdepth_dtype
argument inParticleSet
construction. Default isnp.float64
for C-grids, andnp.float32
for all other grids (#552 and #557) - Renaming of the
full_load
argument inFieldSet.from_netcdf()
construction todeferred_load
, wheredeferred_load = not full_load
, for consistency with how we normally call this mode. Default for netcdf files with more than three snapshots isdeferred_load=True
(#550) - Support for Netcdf files without a
time
dimension, by using thetimestamps
argument inFieldSet
construction (#540) - New tutorials on how to work with 3-dimensional C-grid data such as NEMO (#531) and the
UnitConverter
classes (#516) - Check if the keys in the
dimensions
dictionary are onlylon
,lat
,depth
andtime
(#545) - Change in how variable name should be set in
Field.from_netcdf()
. Now use tuplevariable=(Field_name, variable_name_in_NetCDF_file)
(#545) - Numerous bugfixes
Note also that there is a problem with the latest release of the netCDF4 library, v1.4.2 (Issue #513). For the time being, we recommend downgrading to v1.4.1, using conda install netcdf4=1.4.1
Parcels v2.0.0-beta: a Lagrangian Ocean Analysis tool for the petascale age
This is the beta-release of Parcels v2. Compared to the last v1.1.1 release, there are three important changes
-
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 thefield.data
numpy array (#503 and #276). -
The
dt
argument has been dropped from Kernel definitions, so that the only arguments allowed in a Kernel aredef kernelfunc(fieldset, particle, time)
(#503) -
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:
Parcels v1.1.1: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v1.1.1 builds on the previous v1.1.0 release. Major changes since then:
-
In
Fieldset.from_netcdf()
, the format offilenames
argument has been enriched (#463).
As before,filenames
can be:- list of files
- dictionaries of list of files, where the dictionary keys are field names
On top of that,
filenames
can now be:- dictionaries of lists of files, where the dictionary keys are dimension names:
filenames[dimension_name] = [files]
- dictionaries of dictionaries of lists of files:
filenames[field_name][dimension_name] = [files]
This particularly affects the function
Fieldset.from_nemo()
:New
filenames
looks like:filenames = {'U': {'lon': data_path + 'mesh_mask.nc4', 'lat': data_path + 'mesh_mask.nc4', 'data': data_path + 'U_purely_zonal-ORCA025_grid_U.nc4'}, 'V': {'lon': data_path + 'mesh_mask.nc4', 'lat': data_path + 'mesh_mask.nc4', 'data': data_path + 'V_purely_zonal-ORCA025_grid_V.nc4'}}
instead of:
filenames = {'U': data_path + 'U_purely_zonal-ORCA025_grid_U.nc4', 'V': data_path + 'V_purely_zonal-ORCA025_grid_V.nc4', 'mesh_mask': data_path + 'mesh_mask.nc4'}
This modification enables to easily read 3d curvilinear NEMO fields, when the depth dimension is in the data-files.
-
A new
TimeConverter
class converts between dates and the number of seconds that Parcels uses under the hood. This enables to read netcdf files with less common time formats, such as NOLEAP. (#456) -
New
netcdf_engine
as argument forFieldset.from_netcdf()
, enables to open netcdf files with a different engine when the default netcdf4 engine does not work. (#460) -
Improved error messages for C-kernel generator (#457)
-
Minor bug fixes
Note that it is now recommended to use a recent version of xarray (>=0.10.8) together with Parcels, since it better parses some netcdf files used by common OGCMs.
Parcels v1.1.0: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v1.1 builds on the previous v1.0.5 release. Major changes since then
- A renaming of the
FieldList
class toSummedFields
, so that Fields can be summed byfieldAB = fieldA + fieldB
(#435) - Implement way to do computations on defer-loaded FieldSets (#430). Useful for on-the-fly computing of e.g. relative vorticity.
- Adding of metadata, including by default the Parcels version, to the ParticleFile (#438)
- Support for
ParticleSet.from_field()
for Curvilinear Grids (#429) - A few bug fixes, including for rotated grids (#440), for
indices
across 2D and 3D Fields (#437) and for Fields without time dimension (#431)
As always, please let us know if anything isn't working as expected.
Parcels v1.0.5: a Lagrangian Ocean Analysis tool for the petascale age
Parcels v1.0.5 is a quick update of v1.0.4, mostly because of problems to create a conda-forge
package from v1.0.4.
Nevertheless, there are also a few minor updates in this v1.0.5
- Support for the
**
operator to take power in JIT mode (#424) - Fixed a bug where
runtime
was sometimes not correctly set when deferred-loading fields (#422)
Note that installation instructions have changed. After creating the conda
environment, users should now run python setup.py install