Parcels documentation

Welcome to the documentation of parcels. This page provides detailed documentation for each method, class and function. The documentation corresponds to the latest anaconda release, for newer documentation see the docstrings in the code.

See http://www.oceanparcels.org for general information on the Parcels project, including how to install and use.

parcels.particleset module

class parcels.particleset.ParticleSet(fieldset, pclass=<class 'parcels.particle.JITParticle'>, lon=None, lat=None, depth=None, time=None, repeatdt=None, lonlatdepth_dtype=None, pid_orig=None, **kwargs)[source]

Bases: object

Container class for storing particle and executing kernel over them.

Please note that this currently only supports fixed size particle sets.

Parameters
  • fieldsetparcels.fieldset.FieldSet object from which to sample velocity

  • pclass – Optional parcels.particle.JITParticle or parcels.particle.ScipyParticle object that defines custom particle

  • lon – List of initial longitude values for particles

  • lat – List of initial latitude values for particles

  • depth – Optional list of initial depth values for particles. Default is 0m

  • time – Optional list of initial time values for particles. Default is fieldset.U.grid.time[0]

  • repeatdt – Optional interval (in seconds) on which to repeat the release of the ParticleSet

  • lonlatdepth_dtype – Floating precision for lon, lat, depth particle coordinates. It is either np.float32 or np.float64. Default is np.float32 if fieldset.U.interp_method is ‘linear’ and np.float64 if the interpolation method is ‘cgrid_velocity’

  • partitions – List of cores on which to distribute the particles for MPI runs. Default: None, in which case particles are distributed automatically on the processors

Other Variables can be initialised using further arguments (e.g. v=… for a Variable named ‘v’)

Kernel(pyfunc, c_include='', delete_cfiles=True)[source]

Wrapper method to convert a pyfunc into a parcels.kernel.Kernel object based on fieldset and ptype of the ParticleSet

Parameters

delete_cfiles – Boolean whether to delete the C-files after compilation in JIT mode (default is True)

ParticleFile(*args, **kwargs)[source]

Wrapper method to initialise a parcels.particlefile.ParticleFile object from the ParticleSet

add(particles)[source]

Method to add particles to the ParticleSet

density(field_name=None, particle_val=None, relative=False, area_scale=False)[source]

Method to calculate the density of particles in a ParticleSet from their locations, through a 2D histogram.

Parameters
  • field – Optional parcels.field.Field object to calculate the histogram on. Default is fieldset.U

  • particle_val – Optional numpy-array of values to weigh each particle with, or string name of particle variable to use weigh particles with. Default is None, resulting in a value of 1 for each particle

  • relative – Boolean to control whether the density is scaled by the total weight of all particles. Default is False

  • area_scale – Boolean to control whether the density is scaled by the area (in m^2) of each grid cell. Default is False

execute(pyfunc=<function AdvectionRK4>, endtime=None, runtime=None, dt=1.0, moviedt=None, recovery=None, output_file=None, movie_background_field=None, verbose_progress=None, postIterationCallbacks=None, callbackdt=None)[source]

Execute a given kernel function over the particle set for multiple timesteps. Optionally also provide sub-timestepping for particle output.

Parameters
  • pyfunc – Kernel function to execute. This can be the name of a defined Python function or a parcels.kernel.Kernel object. Kernels can be concatenated using the + operator

  • endtime – End time for the timestepping loop. It is either a datetime object or a positive double.

  • runtime – Length of the timestepping loop. Use instead of endtime. It is either a timedelta object or a positive double.

  • dt – Timestep interval to be passed to the kernel. It is either a timedelta object or a double. Use a negative value for a backward-in-time simulation.

  • moviedt – Interval for inner sub-timestepping (leap), which dictates the update frequency of animation. It is either a timedelta object or a positive double. None value means no animation.

  • output_fileparcels.particlefile.ParticleFile object for particle output

  • recovery – Dictionary with additional :mod:parcels.tools.error recovery kernels to allow custom recovery behaviour in case of kernel errors.

  • movie_background_field – field plotted as background in the movie if moviedt is set. ‘vector’ shows the velocity as a vector field.

  • verbose_progress – Boolean for providing a progress bar for the kernel execution loop.

  • postIterationCallbacks – (Optional) Array of functions that are to be called after each iteration (post-process, non-Kernel)

  • callbackdt – (Optional, in conjecture with ‘postIterationCallbacks) timestep inverval to (latestly) interrupt the running kernel and invoke post-iteration callbacks from ‘postIterationCallbacks’

classmethod from_field(fieldset, pclass, start_field, size, mode='monte_carlo', depth=None, time=None, repeatdt=None, lonlatdepth_dtype=None)[source]

Initialise the ParticleSet randomly drawn according to distribution from a field

Parameters
  • fieldsetparcels.fieldset.FieldSet object from which to sample velocity

  • pclass – mod:parcels.particle.JITParticle or parcels.particle.ScipyParticle object that defines custom particle

  • start_field – Field for initialising particles stochastically (horizontally) according to the presented density field.

  • size – Initial size of particle set

  • mode – Type of random sampling. Currently only ‘monte_carlo’ is implemented

  • depth – Optional list of initial depth values for particles. Default is 0m

  • time – Optional start time value for particles. Default is fieldset.U.time[0]

  • repeatdt – Optional interval (in seconds) on which to repeat the release of the ParticleSet

  • lonlatdepth_dtype – Floating precision for lon, lat, depth particle coordinates. It is either np.float32 or np.float64. Default is np.float32 if fieldset.U.interp_method is ‘linear’ and np.float64 if the interpolation method is ‘cgrid_velocity’

classmethod from_line(fieldset, pclass, start, finish, size, depth=None, time=None, repeatdt=None, lonlatdepth_dtype=None)[source]

Initialise the ParticleSet from start/finish coordinates with equidistant spacing Note that this method uses simple numpy.linspace calls and does not take into account great circles, so may not be a exact on a globe

Parameters
  • fieldsetparcels.fieldset.FieldSet object from which to sample velocity

  • pclass – mod:parcels.particle.JITParticle or parcels.particle.ScipyParticle object that defines custom particle

  • start – Starting point for initialisation of particles on a straight line.

  • finish – End point for initialisation of particles on a straight line.

  • size – Initial size of particle set

  • depth – Optional list of initial depth values for particles. Default is 0m

  • time – Optional start time value for particles. Default is fieldset.U.time[0]

  • repeatdt – Optional interval (in seconds) on which to repeat the release of the ParticleSet

  • lonlatdepth_dtype – Floating precision for lon, lat, depth particle coordinates. It is either np.float32 or np.float64. Default is np.float32 if fieldset.U.interp_method is ‘linear’ and np.float64 if the interpolation method is ‘cgrid_velocity’

For usage examples see the following tutorials:

classmethod from_list(fieldset, pclass, lon, lat, depth=None, time=None, repeatdt=None, lonlatdepth_dtype=None, **kwargs)[source]

Initialise the ParticleSet from lists of lon and lat

Parameters
  • fieldsetparcels.fieldset.FieldSet object from which to sample velocity

  • pclass – mod:parcels.particle.JITParticle or parcels.particle.ScipyParticle object that defines custom particle

  • lon – List of initial longitude values for particles

  • lat – List of initial latitude values for particles

  • depth – Optional list of initial depth values for particles. Default is 0m

  • time – Optional list of start time values for particles. Default is fieldset.U.time[0]

  • repeatdt – Optional interval (in seconds) on which to repeat the release of the ParticleSet

  • lonlatdepth_dtype – Floating precision for lon, lat, depth particle coordinates. It is either np.float32 or np.float64. Default is np.float32 if fieldset.U.interp_method is ‘linear’ and np.float64 if the interpolation method is ‘cgrid_velocity’

Other Variables can be initialised using further arguments (e.g. v=… for a Variable named ‘v’)

For usage examples see the following tutorials:

classmethod from_particlefile(fieldset, pclass, filename, restart=True, repeatdt=None, lonlatdepth_dtype=None)[source]

Initialise the ParticleSet from a netcdf ParticleFile. This creates a new ParticleSet based on the last locations and time of all particles in the netcdf ParticleFile. Particle IDs are preserved if restart=True

Parameters
  • fieldsetparcels.fieldset.FieldSet object from which to sample velocity

  • pclass – mod:parcels.particle.JITParticle or parcels.particle.ScipyParticle object that defines custom particle

  • filename – Name of the particlefile from which to read initial conditions

  • restart – Boolean to signal if pset is used for a restart (default is True). In that case, Particle IDs are preserved.

  • repeatdt – Optional interval (in seconds) on which to repeat the release of the ParticleSet

  • lonlatdepth_dtype – Floating precision for lon, lat, depth particle coordinates. It is either np.float32 or np.float64. Default is np.float32 if fieldset.U.interp_method is ‘linear’ and np.float64 if the interpolation method is ‘cgrid_velocity’

remove_booleanvector(indices)[source]

Method to remove particles from the ParticleSet, based on an array of booleans

remove_indices(indices)[source]

Method to remove particles from the ParticleSet, based on their indices

show(with_particles=True, show_time=None, field=None, domain=None, projection=None, land=True, vmin=None, vmax=None, savefile=None, animation=False, **kwargs)[source]

Method to ‘show’ a Parcels ParticleSet

Parameters
  • with_particles – Boolean whether to show particles

  • show_time – Time at which to show the ParticleSet

  • field – Field to plot under particles (either None, a Field object, or ‘vector’)

  • domain – dictionary (with keys ‘N’, ‘S’, ‘E’, ‘W’) defining domain to show

  • projection – type of cartopy projection to use (default PlateCarree)

  • land – Boolean whether to show land. This is ignored for flat meshes

  • vmin – minimum colour scale (only in single-plot mode)

  • vmax – maximum colour scale (only in single-plot mode)

  • savefile – Name of a file to save the plot to

  • animation – Boolean whether result is a single plot, or an animation

parcels.fieldset module

class parcels.fieldset.FieldSet(U, V, fields=None)[source]

Bases: object

FieldSet class that holds hydrodynamic data needed to execute particles

Parameters
add_constant(name, value)[source]

Add a constant to the FieldSet. Note that all constants are stored as 32-bit floats. While constants can be updated during execution in SciPy mode, they can not be updated in JIT mode.

Tutorials using fieldset.add_constant: Analytical advection Diffusion Periodic boundaries

Parameters
  • name – Name of the constant

  • value – Value of the constant (stored as 32-bit float)

add_field(field, name=None)[source]

Add a parcels.field.Field object to the FieldSet

Parameters

For usage examples see the following tutorials:

add_periodic_halo(zonal=False, meridional=False, halosize=5)[source]

Add a ‘halo’ to all parcels.field.Field objects in a FieldSet, through extending the Field (and lon/lat) by copying a small portion of the field on one side of the domain to the other.

Parameters
  • zonal – Create a halo in zonal direction (boolean)

  • meridional – Create a halo in meridional direction (boolean)

  • halosize – size of the halo (in grid points). Default is 5 grid points

add_vector_field(vfield)[source]

Add a parcels.field.VectorField object to the FieldSet

Parameters

vfieldparcels.field.VectorField object to be added

advancetime(fieldset_new)[source]

Replace oldest time on FieldSet with new FieldSet

Parameters

fieldset_new – FieldSet snapshot with which the oldest time has to be replaced

computeTimeChunk(time, dt)[source]

Load a chunk of three data time steps into the FieldSet. This is used when FieldSet uses data imported from netcdf, with default option deferred_load. The loaded time steps are at or immediatly before time and the two time steps immediately following time if dt is positive (and inversely for negative dt)

Parameters
  • time – Time around which the FieldSet chunks are to be loaded. Time is provided as a double, relatively to Fieldset.time_origin

  • dt – time step of the integration scheme

classmethod from_b_grid_dataset(filenames, variables, dimensions, indices=None, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, tracer_interp_method='bgrid_tracer', field_chunksize='auto', **kwargs)[source]

Initialises FieldSet object from NetCDF files of Bgrid fields.

Parameters
  • filenames – Dictionary mapping variables to file(s). The filepath may contain wildcards to indicate multiple files, or be a list of file. filenames can be a list [files], a dictionary {var:[files]}, a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data), or a dictionary of dictionaries {var:{dim:[files]}} time values are in filenames[data]

  • variables – Dictionary mapping variables to variable names in the netCDF file(s).

  • dimensions

    Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the netCF file(s). Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable. U and V velocity nodes are not located as W velocity and T tracer nodes (see http://www.cesm.ucar.edu/models/cesm1.0/pop2/doc/sci/POPRefManual.pdf ).

    U[k,j+1,i],V[k,j+1,i]

    U[k,j+1,i+1],V[k,j+1,i+1]

    W[k:k+2,j+1,i+1],T[k,j+1,i+1]

    U[k,j,i],V[k,j,i]

    U[k,j,i+1],V[k,j,i+1]

    In 2D: U and V nodes are on the cell vertices and interpolated bilinearly as a A-grid.

    T node is at the cell centre and interpolated constant per cell as a C-grid.

    In 3D: U and V nodes are at the midlle of the cell vertical edges,

    They are interpolated bilinearly (independently of z) in the cell. W nodes are at the centre of the horizontal interfaces. They are interpolated linearly (as a function of z) in the cell. T node is at the cell centre, and constant per cell.

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • tracer_interp_method – Method for interpolation of tracer fields. It is recommended to use ‘bgrid_tracer’ (default) Note that in the case of from_pop() and from_bgrid(), the velocity fields are default to ‘bgrid_velocity’

  • field_chunksize – size of the chunks in dask loading

classmethod from_c_grid_dataset(filenames, variables, dimensions, indices=None, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, tracer_interp_method='cgrid_tracer', field_chunksize='auto', **kwargs)[source]

Initialises FieldSet object from NetCDF files of curvilinear c-grid fields.

See here for a more detailed explanation of the different methods that can be used for c-grid datasets.

Parameters
  • filenames – Dictionary mapping variables to file(s). The filepath may contain wildcards to indicate multiple files, or be a list of file. filenames can be a list [files], a dictionary {var:[files]}, a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data), or a dictionary of dictionaries {var:{dim:[files]}} time values are in filenames[data]

  • variables – Dictionary mapping variables to variable names in the netCDF file(s).

  • dimensions

    Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the netCF file(s). Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable. Watch out: NEMO is discretised on a C-grid: U and V velocities are not located on the same nodes (see https://www.nemo-ocean.eu/doc/node19.html ).

    V[k,j+1,i+1]

    U[k,j+1,i]

    W[k:k+2,j+1,i+1],T[k,j+1,i+1]

    U[k,j+1,i+1]

    V[k,j,i+1]

    To interpolate U, V velocities on the C-grid, Parcels needs to read the f-nodes, which are located on the corners of the cells. (for indexing details: https://www.nemo-ocean.eu/doc/img360.png ) In 3D, the depth is the one corresponding to W nodes

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • tracer_interp_method – Method for interpolation of tracer fields. It is recommended to use ‘cgrid_tracer’ (default) Note that in the case of from_nemo() and from_cgrid(), the velocity fields are default to ‘cgrid_velocity’

  • field_chunksize – size of the chunks in dask loading

classmethod from_data(data, dimensions, transpose=False, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, **kwargs)[source]

Initialise FieldSet object from raw data

Parameters
  • data

    Dictionary mapping field names to numpy arrays. Note that at least a ‘U’ and ‘V’ numpy array need to be given, and that the built-in Advection kernels assume that U and V are in m/s

    1. If data shape is [xdim, ydim], [xdim, ydim, zdim], [xdim, ydim, tdim] or [xdim, ydim, zdim, tdim], whichever is relevant for the dataset, use the flag transpose=True

    2. If data shape is [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim], use the flag transpose=False (default value)

    3. If data has any other shape, you first need to reorder it

  • dimensions – Dictionary mapping field dimensions (lon, lat, depth, time) to numpy arrays. Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable (e.g. dimensions[‘U’], dimensions[‘V’], etc).

  • transpose – Boolean whether to transpose data on read-in

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation, see also https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_unitconverters.ipynb:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

For usage examples see the following tutorials:

classmethod from_nemo(filenames, variables, dimensions, indices=None, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, tracer_interp_method='cgrid_tracer', field_chunksize='auto', **kwargs)[source]

Initialises FieldSet object from NetCDF files of Curvilinear NEMO fields.

See here for a detailed tutorial on the setup for 2D NEMO fields and here for the tutorial on the setup for 3D NEMO fields.

See here for a more detailed explanation of the different methods that can be used for c-grid datasets.

Parameters
  • filenames – Dictionary mapping variables to file(s). The filepath may contain wildcards to indicate multiple files, or be a list of file. filenames can be a list [files], a dictionary {var:[files]}, a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data), or a dictionary of dictionaries {var:{dim:[files]}} time values are in filenames[data]

  • variables – Dictionary mapping variables to variable names in the netCDF file(s). Note that the built-in Advection kernels assume that U and V are in m/s

  • dimensions

    Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the netCF file(s). Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable. Watch out: NEMO is discretised on a C-grid: U and V velocities are not located on the same nodes (see https://www.nemo-ocean.eu/doc/node19.html ).

    V[k,j+1,i+1]

    U[k,j+1,i]

    W[k:k+2,j+1,i+1],T[k,j+1,i+1]

    U[k,j+1,i+1]

    V[k,j,i+1]

    To interpolate U, V velocities on the C-grid, Parcels needs to read the f-nodes, which are located on the corners of the cells. (for indexing details: https://www.nemo-ocean.eu/doc/img360.png ) In 3D, the depth is the one corresponding to W nodes

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation, see also https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_unitconverters.ipynb:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • tracer_interp_method – Method for interpolation of tracer fields. It is recommended to use ‘cgrid_tracer’ (default) Note that in the case of from_nemo() and from_cgrid(), the velocity fields are default to ‘cgrid_velocity’

  • field_chunksize – size of the chunks in dask loading

classmethod from_netcdf(filenames, variables, dimensions, indices=None, fieldtype=None, mesh='spherical', timestamps=None, allow_time_extrapolation=None, time_periodic=False, deferred_load=True, field_chunksize='auto', **kwargs)[source]

Initialises FieldSet object from NetCDF files

Parameters
  • filenames – Dictionary mapping variables to file(s). The filepath may contain wildcards to indicate multiple files or be a list of file. filenames can be a list [files], a dictionary {var:[files]}, a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data), or a dictionary of dictionaries {var:{dim:[files]}}. time values are in filenames[data]

  • variables – Dictionary mapping variables to variable names in the netCDF file(s). Note that the built-in Advection kernels assume that U and V are in m/s

  • dimensions – Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the netCF file(s). Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable (e.g. dimensions[‘U’], dimensions[‘V’], etc).

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation, see also https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_unitconverters.ipynb:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • timestamps – list of lists or array of arrays containing the timestamps for each of the files in filenames. Outer list/array corresponds to files, inner array corresponds to indices within files. Default is None if dimensions includes time.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • deferred_load – boolean whether to only pre-load data (in deferred mode) or fully load them (default: True). It is advised to deferred load the data, since in that case Parcels deals with a better memory management during particle set execution. deferred_load=False is however sometimes necessary for plotting the fields.

  • interp_method – Method for interpolation. Options are ‘linear’ (default), ‘nearest’, ‘linear_invdist_land_tracer’, ‘cgrid_velocity’, ‘cgrid_tracer’ and ‘bgrid_velocity’

  • field_chunksize – size of the chunks in dask loading

  • netcdf_engine – engine to use for netcdf reading in xarray. Default is ‘netcdf’, but in cases where this doesn’t work, setting netcdf_engine=’scipy’ could help

For usage examples see the following tutorials:

classmethod from_parcels(basename, uvar='vozocrtx', vvar='vomecrty', indices=None, extra_fields=None, allow_time_extrapolation=None, time_periodic=False, deferred_load=True, field_chunksize='auto', **kwargs)[source]

Initialises FieldSet data from NetCDF files using the Parcels FieldSet.write() conventions.

Parameters
  • basename – Base name of the file(s); may contain wildcards to indicate multiple files.

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • extra_fields – Extra fields to read beyond U and V

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • deferred_load – boolean whether to only pre-load data (in deferred mode) or fully load them (default: True). It is advised to deferred load the data, since in that case Parcels deals with a better memory management during particle set execution. deferred_load=False is however sometimes necessary for plotting the fields.

  • field_chunksize – size of the chunks in dask loading

classmethod from_pop(filenames, variables, dimensions, indices=None, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, tracer_interp_method='bgrid_tracer', field_chunksize='auto', **kwargs)[source]
Initialises FieldSet object from NetCDF files of POP fields.

It is assumed that the velocities in the POP fields is in cm/s.

Parameters
  • filenames – Dictionary mapping variables to file(s). The filepath may contain wildcards to indicate multiple files, or be a list of file. filenames can be a list [files], a dictionary {var:[files]}, a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data), or a dictionary of dictionaries {var:{dim:[files]}} time values are in filenames[data]

  • variables – Dictionary mapping variables to variable names in the netCDF file(s). Note that the built-in Advection kernels assume that U and V are in m/s

  • dimensions

    Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the netCF file(s). Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable. Watch out: POP is discretised on a B-grid: U and V velocity nodes are not located as W velocity and T tracer nodes (see http://www.cesm.ucar.edu/models/cesm1.0/pop2/doc/sci/POPRefManual.pdf ).

    U[k,j+1,i],V[k,j+1,i]

    U[k,j+1,i+1],V[k,j+1,i+1]

    W[k:k+2,j+1,i+1],T[k,j+1,i+1]

    U[k,j,i],V[k,j,i]

    U[k,j,i+1],V[k,j,i+1]

    In 2D: U and V nodes are on the cell vertices and interpolated bilinearly as a A-grid.

    T node is at the cell centre and interpolated constant per cell as a C-grid.

    In 3D: U and V nodes are at the middle of the cell vertical edges,

    They are interpolated bilinearly (independently of z) in the cell. W nodes are at the centre of the horizontal interfaces. They are interpolated linearly (as a function of z) in the cell. T node is at the cell centre, and constant per cell.

  • indices – Optional dictionary of indices for each dimension to read from file(s), to allow for reading of subset of data. Default is to read the full extent of each dimension. Note that negative indices are not allowed.

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation, see also https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_unitconverters.ipynb:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

  • tracer_interp_method – Method for interpolation of tracer fields. It is recommended to use ‘bgrid_tracer’ (default) Note that in the case of from_pop() and from_bgrid(), the velocity fields are default to ‘bgrid_velocity’

  • field_chunksize – size of the chunks in dask loading

classmethod from_xarray_dataset(ds, variables, dimensions, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, **kwargs)[source]

Initialises FieldSet data from xarray Datasets.

Parameters
  • ds – xarray Dataset. Note that the built-in Advection kernels assume that U and V are in m/s

  • variables – Dictionary mapping parcels variable names to data variables in the xarray Dataset.

  • dimensions – Dictionary mapping data dimensions (lon, lat, depth, time, data) to dimensions in the xarray Dataset. Note that dimensions can also be a dictionary of dictionaries if dimension names are different for each variable (e.g. dimensions[‘U’], dimensions[‘V’], etc).

  • fieldtype – Optional dictionary mapping fields to fieldtypes to be used for UnitConverter. (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation, see also https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_unitconverters.ipynb:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). (Default: False) This flag overrides the allow_time_interpolation and sets it to False

get_fields()[source]

Returns a list of all the parcels.field.Field and parcels.field.VectorField objects associated with this FieldSet

write(filename)[source]

Write FieldSet to NetCDF file using NEMO convention

Parameters

filename – Basename of the output fileset

parcels.field module

class parcels.field.Field(name, data, lon=None, lat=None, depth=None, time=None, grid=None, mesh='flat', timestamps=None, fieldtype=None, transpose=False, vmin=None, vmax=None, time_origin=None, interp_method='linear', allow_time_extrapolation=None, time_periodic=False, **kwargs)[source]

Bases: object

Class that encapsulates access to field data.

Parameters
  • name – Name of the field

  • data

    2D, 3D or 4D numpy array of field data.

    1. If data shape is [xdim, ydim], [xdim, ydim, zdim], [xdim, ydim, tdim] or [xdim, ydim, zdim, tdim], whichever is relevant for the dataset, use the flag transpose=True

    2. If data shape is [ydim, xdim], [zdim, ydim, xdim], [tdim, ydim, xdim] or [tdim, zdim, ydim, xdim], use the flag transpose=False

    3. If data has any other shape, you first need to reorder it

  • lon – Longitude coordinates (numpy vector or array) of the field (only if grid is None)

  • lat – Latitude coordinates (numpy vector or array) of the field (only if grid is None)

  • depth – Depth coordinates (numpy vector or array) of the field (only if grid is None)

  • time – Time coordinates (numpy vector) of the field (only if grid is None)

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation: (only if grid is None)

    1. spherical: Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat (default): No conversion, lat/lon are assumed to be in m.

  • timestamps – A numpy array containing the timestamps for each of the files in filenames, for loading from netCDF files only. Default is None if the netCDF dimensions dictionary includes time.

  • gridparcels.grid.Grid object containing all the lon, lat depth, time mesh and time_origin information. Can be constructed from any of the Grid objects

  • fieldtype – Type of Field to be used for UnitConverter when using SummedFields (either ‘U’, ‘V’, ‘Kh_zonal’, ‘Kh_meridional’ or None)

  • transpose – Transpose data to required (lon, lat) layout

  • vmin – Minimum allowed value on the field. Data below this value are set to zero

  • vmax – Maximum allowed value on the field. Data above this value are set to zero

  • time_origin – Time origin (TimeConverter object) of the time axis (only if grid is None)

  • interp_method – Method for interpolation. Options are ‘linear’ (default), ‘nearest’, ‘linear_invdist_land_tracer’, ‘cgrid_velocity’, ‘cgrid_tracer’ and ‘bgrid_velocity’

  • allow_time_extrapolation – boolean whether to allow for extrapolation in time (i.e. beyond the last available time snapshot)

  • time_periodic – To loop periodically over the time component of the Field. It is set to either False or the length of the period (either float in seconds or datetime.timedelta object). The last value of the time series can be provided (which is the same as the initial one) or not (Default: False) This flag overrides the allow_time_interpolation and sets it to False

For usage examples see the following tutorials:

add_periodic_halo(zonal, meridional, halosize=5, data=None)[source]

Add a ‘halo’ to all Fields in a FieldSet, through extending the Field (and lon/lat) by copying a small portion of the field on one side of the domain to the other. Before adding a periodic halo to the Field, it has to be added to the Grid on which the Field depends

See this tutorial for a detailed explanation on how to set up periodic boundaries

Parameters
  • zonal – Create a halo in zonal direction (boolean)

  • meridional – Create a halo in meridional direction (boolean)

  • halosize – size of the halo (in grid points). Default is 5 grid points

  • data – if data is not None, the periodic halo will be achieved on data instead of self.data and data will be returned

calc_cell_edge_sizes()[source]

Method to calculate cell sizes based on numpy.gradient method Currently only works for Rectilinear Grids

cell_areas()[source]

Method to calculate cell sizes based on cell_edge_sizes Currently only works for Rectilinear Grids

property ctypes_struct

Returns a ctypes struct object containing all relevant pointers and sizes for this field.

depth_index(depth, lat, lon)[source]

Find the index in the depth array associated with a given depth

eval(time, z, y, x, applyConversion=True)[source]

Interpolate field values in space and time.

We interpolate linearly in time and apply implicit unit conversion to the result. Note that we defer to scipy.interpolate to perform spatial interpolation.

classmethod from_netcdf(filenames, variable, dimensions, indices=None, grid=None, mesh='spherical', timestamps=None, allow_time_extrapolation=None, time_periodic=False, deferred_load=True, **kwargs)[source]

Create field from netCDF file

Parameters
  • filenames – list of filenames to read for the field. filenames can be a list [files] or a dictionary {dim:[files]} (if lon, lat, depth and/or data not stored in same files as data) In the latetr case, time values are in filenames[data]

  • variable – Tuple mapping field name to variable name in the NetCDF file.

  • dimensions – Dictionary mapping variable names for the relevant dimensions in the NetCDF file

  • indices – dictionary mapping indices for each dimension to read from file. This can be used for reading in only a subregion of the NetCDF file. Note that negative indices are not allowed.

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • timestamps – A numpy array of datetime64 objects containing the timestamps for each of the files in filenames. Default is None if dimensions includes time.

  • allow_time_extrapolation – boolean whether to allow for extrapolation in time (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – boolean whether to loop periodically over the time component of the FieldSet This flag overrides the allow_time_interpolation and sets it to False

  • deferred_load – boolean whether to only pre-load data (in deferred mode) or fully load them (default: True). It is advised to deferred load the data, since in that case Parcels deals with a better memory management during particle set execution. deferred_load=False is however sometimes necessary for plotting the fields.

  • field_chunksize – size of the chunks in dask loading

For usage examples see the following tutorial:

classmethod from_xarray(da, name, dimensions, mesh='spherical', allow_time_extrapolation=None, time_periodic=False, **kwargs)[source]

Create field from xarray Variable

Parameters
  • da – Xarray DataArray

  • name – Name of the Field

  • dimensions – Dictionary mapping variable names for the relevant dimensions in the DataArray

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

  • allow_time_extrapolation – boolean whether to allow for extrapolation in time (i.e. beyond the last available time snapshot) Default is False if dimensions includes time, else True

  • time_periodic – boolean whether to loop periodically over the time component of the FieldSet This flag overrides the allow_time_interpolation and sets it to False

set_depth_from_field(field)[source]

Define the depth dimensions from another (time-varying) field

See this tutorial for a detailed explanation on how to set up time-evolving depth dimensions

set_scaling_factor(factor)[source]

Scales the field data by some constant factor.

Parameters

factor – scaling factor

For usage examples see the following tutorial:

show(animation=False, show_time=None, domain=None, depth_level=0, projection=None, land=True, vmin=None, vmax=None, savefile=None, **kwargs)[source]

Method to ‘show’ a Parcels Field

Parameters
  • animation – Boolean whether result is a single plot, or an animation

  • show_time – Time at which to show the Field (only in single-plot mode)

  • domain – dictionary (with keys ‘N’, ‘S’, ‘E’, ‘W’) defining domain to show

  • depth_level – depth level to be plotted (default 0)

  • projection – type of cartopy projection to use (default PlateCarree)

  • land – Boolean whether to show land. This is ignored for flat meshes

  • vmin – minimum colour scale (only in single-plot mode)

  • vmax – maximum colour scale (only in single-plot mode)

  • savefile – Name of a file to save the plot to

spatial_interpolation(ti, z, y, x, time)[source]

Interpolate horizontal field values using a SciPy interpolator

temporal_interpolate_fullfield(ti, time)[source]

Calculate the data of a field between two snapshots, using linear interpolation

Parameters
  • ti – Index in time array associated with time (via time_index())

  • time – Time to interpolate to

Return type

Linearly interpolated field

time_index(time)[source]

Find the index in the time array associated with a given time

Note that we normalize to either the first or the last index if the sampled value is outside the time value range.

write(filename, varname=None)[source]

Write a Field to a netcdf file

Parameters
  • filename – Basename of the file

  • varname – Name of the field, to be appended to the filename

class parcels.field.NestedField(name, F, V=None, W=None)[source]

Bases: list

Class NestedField is a list of Fields from which the first one to be not declared out-of-boundaries at particle position is interpolated. This induces that the order of the fields in the list matters. Each one it its turn, a field is interpolated: if the interpolation succeeds or if an error other than ErrorOutOfBounds is thrown, the function is stopped. Otherwise, next field is interpolated. NestedField returns an ErrorOutOfBounds only if last field is as well out of boundaries. NestedField is composed of either Fields or VectorFields.

See here for a detailed tutorial

Parameters
  • name – Name of the NestedField

  • F – List of fields (order matters). F can be a scalar Field, a VectorField, or the zonal component (U) of the VectorField

  • V – List of fields defining the meridional component of a VectorField, if F is the zonal component. (default: None)

  • W – List of fields defining the vertical component of a VectorField, if F and V are the zonal and meridional components (default: None)

class parcels.field.SummedField(name, F, V=None, W=None)[source]

Bases: list

Class SummedField is a list of Fields over which Field interpolation is summed. This can e.g. be used when combining multiple flow fields, where the total flow is the sum of all the individual flows. Note that the individual Fields can be on different Grids. Also note that, since SummedFields are lists, the individual Fields can still be queried through their list index (e.g. SummedField[1]). SummedField is composed of either Fields or VectorFields.

See here for a detailed tutorial

Parameters
  • name – Name of the SummedField

  • F – List of fields. F can be a scalar Field, a VectorField, or the zonal component (U) of the VectorField

  • V – List of fields defining the meridional component of a VectorField, if F is the zonal component. (default: None)

  • W – List of fields defining the vertical component of a VectorField, if F and V are the zonal and meridional components (default: None)

class parcels.field.VectorField(name, U, V, W=None)[source]

Bases: object

Class VectorField stores 2 or 3 fields which defines together a vector field. This enables to interpolate them as one single vector field in the kernels.

Parameters
  • name – Name of the vector field

  • U – field defining the zonal component

  • V – field defining the meridional component

  • W – field defining the vertical component (default: None)

spatial_c_grid_interpolation3D(ti, z, y, x, time)[source]

U0 U1 | __ V0 __ | The interpolation is done in the following by interpolating linearly U depending on the longitude coordinate and interpolating linearly V depending on the latitude coordinate. Curvilinear grids are treated properly, since the element is projected to a rectilinear parent element.

parcels.gridset module

class parcels.gridset.GridSet[source]

Bases: object

GridSet class that holds the Grids on which the Fields are defined

dimrange(dim)[source]

Returns maximum value of a dimension (lon, lat, depth or time) on ‘left’ side and minimum value on ‘right’ side for all grids in a gridset. Useful for finding e.g. longitude range that overlaps on all grids in a gridset

parcels.grid module

class parcels.grid.CGrid[source]

Bases: _ctypes.Structure

class parcels.grid.CurvilinearSGrid(lon, lat, depth, time=None, time_origin=None, mesh='flat')[source]

Bases: parcels.grid.CurvilinearGrid

Curvilinear S Grid.

Parameters
  • lon – 2D array containing the longitude coordinates of the grid

  • lat – 2D array containing the latitude coordinates of the grid

  • depth – 4D (time-evolving) or 3D (time-independent) array containing the vertical coordinates of the grid, which are s-coordinates. s-coordinates can be terrain-following (sigma) or iso-density (rho) layers, or any generalised vertical discretisation. The depth of each node depends then on the horizontal position (lon, lat), the number of the layer and the time is depth is a 4D array. depth array is either a 4D array[xdim][ydim][zdim][tdim] or a 3D array[xdim][ydim[zdim].

  • time – Vector containing the time coordinates of the grid

  • time_origin – Time origin (TimeConverter object) of the time axis

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

class parcels.grid.CurvilinearZGrid(lon, lat, depth=None, time=None, time_origin=None, mesh='flat')[source]

Bases: parcels.grid.CurvilinearGrid

Curvilinear Z Grid.

Parameters
  • lon – 2D array containing the longitude coordinates of the grid

  • lat – 2D array containing the latitude coordinates of the grid

  • depth – Vector containing the vertical coordinates of the grid, which are z-coordinates. The depth of the different layers is thus constant.

  • time – Vector containing the time coordinates of the grid

  • time_origin – Time origin (TimeConverter object) of the time axis

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

class parcels.grid.Grid(lon, lat, time, time_origin, mesh)[source]

Bases: object

Grid class that defines a (spatial and temporal) grid on which Fields are defined

property child_ctypes_struct

Returns a ctypes struct object containing all relevant pointers and sizes for this grid.

class parcels.grid.GridCode(value)[source]

Bases: enum.IntEnum

An enumeration.

class parcels.grid.RectilinearSGrid(lon, lat, depth, time=None, time_origin=None, mesh='flat')[source]

Bases: parcels.grid.RectilinearGrid

Rectilinear S Grid. Same horizontal discretisation as a rectilinear z grid,

but with s vertical coordinates

Parameters
  • lon – Vector containing the longitude coordinates of the grid

  • lat – Vector containing the latitude coordinates of the grid

  • depth – 4D (time-evolving) or 3D (time-independent) array containing the vertical coordinates of the grid, which are s-coordinates. s-coordinates can be terrain-following (sigma) or iso-density (rho) layers, or any generalised vertical discretisation. The depth of each node depends then on the horizontal position (lon, lat), the number of the layer and the time is depth is a 4D array. depth array is either a 4D array[xdim][ydim][zdim][tdim] or a 3D array[xdim][ydim[zdim].

  • time – Vector containing the time coordinates of the grid

  • time_origin – Time origin (TimeConverter object) of the time axis

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

class parcels.grid.RectilinearZGrid(lon, lat, depth=None, time=None, time_origin=None, mesh='flat')[source]

Bases: parcels.grid.RectilinearGrid

Rectilinear Z Grid

Parameters
  • lon – Vector containing the longitude coordinates of the grid

  • lat – Vector containing the latitude coordinates of the grid

  • depth – Vector containing the vertical coordinates of the grid, which are z-coordinates. The depth of the different layers is thus constant.

  • time – Vector containing the time coordinates of the grid

  • time_origin – Time origin (TimeConverter object) of the time axis

  • mesh

    String indicating the type of mesh coordinates and units used during velocity interpolation:

    1. spherical (default): Lat and lon in degree, with a correction for zonal velocity U near the poles.

    2. flat: No conversion, lat/lon are assumed to be in m.

parcels.particle module

class parcels.particle.JITParticle(*args, **kwargs)[source]

Bases: parcels.particle.ScipyParticle

Particle class for JIT-based (Just-In-Time) Particle objects

Parameters
  • lon – Initial longitude of particle

  • lat – Initial latitude of particle

  • fieldsetparcels.fieldset.FieldSet object to track this particle on

  • dt – Execution timestep for this particle

  • time – Current time of the particle

Additional Variables can be added via the :Class Variable: objects

Users should use JITParticles for faster advection computation.

class parcels.particle.ScipyParticle(lon, lat, pid, fieldset, depth=0.0, time=0.0, cptr=None)[source]

Bases: parcels.particle._Particle

Class encapsulating the basic attributes of a particle, to be executed in SciPy mode

Parameters
  • lon – Initial longitude of particle

  • lat – Initial latitude of particle

  • depth – Initial depth of particle

  • fieldsetparcels.fieldset.FieldSet object to track this particle on

  • time – Current time of the particle

Additional Variables can be added via the :Class Variable: objects

class parcels.particle.Variable(name, dtype=<class 'numpy.float32'>, initial=0, to_write=True)[source]

Bases: object

Descriptor class that delegates data access to particle data

Parameters
  • name – Variable name as used within kernels

  • dtype – Data type (numpy.dtype) of the variable

  • initial – Initial value of the variable. Note that this can also be a Field object, which will then be sampled at the location of the particle

  • to_write ((bool, 'once', optional)) – Boolean or ‘once’. Controls whether Variable is written to NetCDF file. If to_write = ‘once’, the variable will be written as a time-independent 1D array

is64bit()[source]

Check whether variable is 64-bit

parcels.kernels.advection module

Collection of pre-built advection kernels

parcels.kernels.advection.AdvectionAnalytical(particle, fieldset, time)[source]

Advection of particles using ‘analytical advection’ integration

Based on Ariane/TRACMASS algorithm, as detailed in e.g. Doos et al (https://doi.org/10.5194/gmd-10-1733-2017). Note that the time-dependent scheme is currently implemented with ‘intermediate timesteps’ (default 10 per model timestep) and not yet with the full analytical time integration

parcels.kernels.advection.AdvectionEE(particle, fieldset, time)[source]

Advection of particles using Explicit Euler (aka Euler Forward) integration.

Function needs to be converted to Kernel object before execution

parcels.kernels.advection.AdvectionRK4(particle, fieldset, time)[source]

Advection of particles using fourth-order Runge-Kutta integration.

Function needs to be converted to Kernel object before execution

parcels.kernels.advection.AdvectionRK45(particle, fieldset, time)[source]

Advection of particles using adadptive Runge-Kutta 4/5 integration.

Times-step dt is halved if error is larger than tolerance, and doubled if error is smaller than 1/10th of tolerance, with tolerance set to 1e-5 * dt by default.

parcels.kernels.advection.AdvectionRK4_3D(particle, fieldset, time)[source]

Advection of particles using fourth-order Runge-Kutta integration including vertical velocity.

Function needs to be converted to Kernel object before execution

parcels.kernels.advectiondiffusion module

Collection of pre-built advection-diffusion kernels

See this tutorial for a detailed explanation

parcels.kernels.advectiondiffusion.AdvectionDiffusionEM(particle, fieldset, time)[source]

Kernel for 2D advection-diffusion, solved using the Euler-Maruyama scheme (EM).

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be at least an order of magnitude less than the typical grid resolution.

The Wiener increment dW should be normally distributed with zero mean and a standard deviation of sqrt(dt). Instead, here a uniform distribution with the same mean and std is used for efficiency and to keep random increments bounded. This substitution is valid for the convergence of particle distributions. If convergence of individual particle paths is required, use normally distributed random increments instead. See Gräwe et al (2012) doi.org/10.1007/s10236-012-0523-y for more information.

parcels.kernels.advectiondiffusion.AdvectionDiffusionM1(particle, fieldset, time)[source]

Kernel for 2D advection-diffusion, solved using the Milstein scheme at first order (M1).

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be at least an order of magnitude less than the typical grid resolution.

The Milstein scheme is superior to the Euler-Maruyama scheme, experiencing less spurious background diffusivity by including extra correction terms that are computationally cheap.

The Wiener increment dW should be normally distributed with zero mean and a standard deviation of sqrt(dt). Instead, here a uniform distribution with the same mean and std is used for efficiency and to keep random increments bounded. This substitution is valid for the convergence of particle distributions. If convergence of individual particle paths is required, use normally distributed random increments instead. See Gräwe et al (2012) doi.org/10.1007/s10236-012-0523-y for more information.

parcels.kernels.advectiondiffusion.AdvectionRK4DiffusionEM(particle, fieldset, time)[source]

Kernel for 2D advection-diffusion, with advection solved using fourth order Runge-Kutta (RK4) and diffusion using the Euler-Maruyama scheme (EM). Using the RK4 scheme for diffusion is only advantageous in areas where the contribution from diffusion is negligible.

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be at least an order of magnitude less than the typical grid resolution.

The Wiener increment dW should be normally distributed with zero mean and a standard deviation of sqrt(dt). Instead, here a uniform distribution with the same mean and std is used for efficiency and to keep random increments bounded. This substitution is valid for the convergence of particle distributions. If convergence of individual particle paths is required, use normally distributed random increments instead. See Gräwe et al (2012) doi.org/10.1007/s10236-012-0523-y for more information.

parcels.kernels.advectiondiffusion.AdvectionRK4DiffusionM1(particle, fieldset, time)[source]

Kernel for 2D advection-diffusion, with advection solved using fourth order Runge-Kutta (RK4) and diffusion using the Milstein scheme at first order (M1). Using the RK4 scheme for diffusion is only advantageous in areas where the contribution from diffusion is negligible.

Assumes that fieldset has fields Kh_zonal and Kh_meridional and variable fieldset.dres, setting the resolution for the central difference gradient approximation. This should be at least an order of magnitude less than the typical grid resolution.

The Milstein scheme is superior to the Euler-Maruyama scheme, experiencing less spurious background diffusivity by including extra correction terms that are computationally cheap.

The Wiener increment dW should be normally distributed with zero mean and a standard deviation of sqrt(dt). Instead, here a uniform distribution with the same mean and std is used for efficiency and to keep random increments bounded. This substitution is valid for the convergence of particle distributions. If convergence of individual particle paths is required, use normally distributed random increments instead. See Gräwe et al (2012) doi.org/10.1007/s10236-012-0523-y for more information.

parcels.kernels.advectiondiffusion.DiffusionUniformKh(particle, fieldset, time)[source]

Kernel for simple 2D diffusion where diffusivity (Kh) is assumed uniform. Assumes that fieldset has fields Kh_zonal and Kh_meridional.

This kernel neglects gradients in the diffusivity field and is therefore more efficient in cases with uniform diffusivity. Since the perturbation due to diffusion is in this case spatially independent, this kernel contains no advection and can be used in combination with a seperate advection kernel.

The Wiener increment dW should be normally distributed with zero mean and a standard deviation of sqrt(dt). Instead, here a uniform distribution with the same mean and std is used for efficiency and to keep random increments bounded. This substitution is valid for the convergence of particle distributions. If convergence of individual particle paths is required, use normally distributed random increments instead. See Gräwe et al (2012) doi.org/10.1007/s10236-012-0523-y for more information.

parcels.codegenerator module

class parcels.codegenerator.IntrinsicTransformer(fieldset, ptype)[source]

Bases: ast.NodeTransformer

AST transformer that catches any mention of intrinsic variable names, such as ‘particle’ or ‘fieldset’, inserts placeholder objects and propagates attribute access.

get_tmp()[source]

Create a new temporary veriable name

visit_Name(node)[source]

Inject IntrinsicNode objects into the tree according to keyword

class parcels.codegenerator.KernelGenerator(fieldset, ptype)[source]

Bases: ast.NodeVisitor

Code generator class that translates simple Python kernel functions into C functions by populating and accessing the ccode attriibute on nodes in the Python AST.

visit_Call(node)[source]

Generate C code for simple C-style function calls. Please note that starred and keyword arguments are currently not supported.

visit_FieldNode(node)[source]

Record intrinsic fields used in kernel

visit_Name(node)[source]

Catches any mention of intrinsic variable names, such as ‘particle’ or ‘fieldset’ and inserts our placeholder objects

visit_NestedFieldNode(node)[source]

Record intrinsic fields used in kernel

visit_NestedVectorFieldNode(node)[source]

Record intrinsic fields used in kernel

visit_SummedFieldNode(node)[source]

Record intrinsic fields used in kernel

visit_SummedVectorFieldNode(node)[source]

Record intrinsic fields used in kernel

visit_VectorFieldNode(node)[source]

Record intrinsic fields used in kernel

class parcels.codegenerator.LoopGenerator(fieldset, ptype=None)[source]

Bases: object

Code generator class that adds type definitions and the outer loop around kernel functions to generate compilable C code.

class parcels.codegenerator.TupleSplitter[source]

Bases: ast.NodeTransformer

AST transformer that detects and splits Pythonic tuple assignments into multiple statements for conversion to C.

parcels.compiler module

class parcels.compiler.Compiler(cc=None, cppargs=None, ldargs=None)[source]

Bases: object

A compiler object for creating and loading shared libraries.

Parameters
  • cc – C compiler executable (uses environment variable CC if not provided).

  • cppargs – A list of arguments to the C compiler (optional).

  • ldargs – A list of arguments to the linker (optional).

class parcels.compiler.GNUCompiler(cppargs=None, ldargs=None)[source]

Bases: parcels.compiler.Compiler

A compiler object for the GNU Linux toolchain.

Parameters
  • cppargs – A list of arguments to pass to the C compiler (optional).

  • ldargs – A list of arguments to pass to the linker (optional).

parcels.kernel module

class parcels.kernel.Kernel(fieldset, ptype, pyfunc=None, funcname=None, funccode=None, py_ast=None, funcvars=None, c_include='', delete_cfiles=True)[source]

Bases: object

Kernel object that encapsulates auto-generated code.

Parameters
  • fieldset – FieldSet object providing the field information

  • ptype – PType object for the kernel particle

  • delete_cfiles – Boolean whether to delete the C-files after compilation in JIT mode (default is True)

Note: A Kernel is either created from a compiled <function …> object or the necessary information (funcname, funccode, funcvars) is provided. The py_ast argument may be derived from the code string, but for concatenation, the merged AST plus the new header definition is required.

compile(compiler)[source]

Writes kernel code to file and compiles it.

execute(pset, endtime, dt, recovery=None, output_file=None, execute_once=False)[source]

Execute this Kernel over a ParticleSet for several timesteps

execute_jit(pset, endtime, dt)[source]

Invokes JIT engine to perform the core update loop

execute_python(pset, endtime, dt)[source]

Performs the core update loop via Python

parcels.particlefile module

Module controlling the writing of ParticleSets to NetCDF file

class parcels.particlefile.ParticleFile(name, particleset, outputdt=inf, write_ondelete=False, convert_at_end=True, tempwritedir=None, pset_info=None)[source]

Initialise trajectory output.

Parameters
  • name – Basename of the output file

  • particleset – ParticleSet to output

  • outputdt – Interval which dictates the update frequency of file output while ParticleFile is given as an argument of ParticleSet.execute() It is either a timedelta object or a positive double.

  • write_ondelete – Boolean to write particle data only when they are deleted. Default is False

  • convert_at_end – Boolean to convert npy files to netcdf at end of run. Default is True

  • tempwritedir – directories to write temporary files to during executing. Default is out-XXXXXX where Xs are random capitals. Files for individual processors are written to subdirectories 0, 1, 2 etc under tempwritedir

  • pset_info – dictionary of info on the ParticleSet, stored in tempwritedir/XX/pset_info.npy, used to create NetCDF file from npy-files.

add_metadata(name, message)[source]

Add metadata to parcels.particleset.ParticleSet

Parameters
  • name – Name of the metadata variabale

  • message – message to be written

close(delete_tempfiles=True)[source]

Close the ParticleFile object by exporting and then deleting the temporary npy files

convert_pset_to_dict(pset, time, deleted_only=False)[source]

Convert all Particle data from one time step to a python dictionary.

Parameters
  • pset – ParticleSet object to write

  • time – Time at which to write ParticleSet

  • deleted_only – Flag to write only the deleted Particles

returns two dictionaries: one for all variables to be written each outputdt,

and one for all variables to be written once

delete_tempwritedir(tempwritedir=None)[source]

Deleted all temporary npy files

:param tempwritedir Optional path of the directory to delete

dump_dict_to_npy(data_dict, data_dict_once)[source]

Buffer data to set of temporary numpy files, using np.save

dump_psetinfo_to_npy()[source]
export()[source]

Exports outputs in temporary NPY-files to NetCDF file

open_netcdf_file(data_shape)[source]

Initialise NetCDF4.Dataset for trajectory output. The output follows the format outlined in the Discrete Sampling Geometries section of the CF-conventions: http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#discrete-sampling-geometries The current implementation is based on the NCEI template: http://www.nodc.noaa.gov/data/formats/netcdf/v2.0/trajectoryIncomplete.cdl

Parameters

data_shape – shape of the variables in the NetCDF4 file

read_from_npy(file_list, time_steps, var)[source]

Read NPY-files for one variable using a loop over all files.

Parameters
  • file_list – List that contains all file names in the output directory

  • time_steps – Number of time steps that were written in out directory

  • var – name of the variable to read

write(pset, time, deleted_only=False)[source]

Write all data from one time step to a temporary npy-file using a python dictionary. The data is saved in the folder ‘out’.

Parameters
  • pset – ParticleSet object to write

  • time – Time at which to write ParticleSet

  • deleted_only – Flag to write only the deleted Particles

parcels.rng module

parcels.rng.expovariate(lamb)[source]

Returns a randome float of an exponential distribution with parameter lamb

parcels.rng.normalvariate(loc, scale)[source]

Returns a random float on normal distribution with mean loc and width scale

parcels.rng.randint(low, high)[source]

Returns a random int between low and high

parcels.rng.random()[source]

Returns a random float between 0. and 1.

parcels.rng.seed(seed)[source]

Sets the seed for parcels internal RNG

parcels.rng.uniform(low, high)[source]

Returns a random float between low and high

parcels.rng.vonmisesvariate(mu, kappa)[source]

Returns a randome float of a Von Mises distribution with mean angle mu and concentration parameter kappa

parcels.tools.error module

Collection of pre-built recovery kernels

exception parcels.tools.error.FieldOutOfBoundError(x, y, z, field=None)[source]

Bases: RuntimeError

Utility error class to propagate out-of-bound field sampling in Scipy mode

exception parcels.tools.error.FieldSamplingError(x, y, z, field=None)[source]

Bases: RuntimeError

Utility error class to propagate erroneous field sampling in Scipy mode

exception parcels.tools.error.KernelError(particle, fieldset=None, msg=None)[source]

Bases: RuntimeError

General particle kernel error with optional custom message

exception parcels.tools.error.OutOfBoundsError(particle, fieldset=None, lon=None, lat=None, depth=None)[source]

Bases: parcels.tools.error.KernelError

Particle kernel error for out-of-bounds field sampling

exception parcels.tools.error.OutOfTimeError(particle, fieldset)[source]

Bases: parcels.tools.error.KernelError

Particle kernel error for time extrapolation field sampling

exception parcels.tools.error.ThroughSurfaceError(particle, fieldset=None, lon=None, lat=None, depth=None)[source]

Bases: parcels.tools.error.KernelError

Particle kernel error for field sampling at surface

exception parcels.tools.error.TimeExtrapolationError(time, field=None, msg='allow_time_extrapoltion')[source]

Bases: RuntimeError

Utility error class to propagate erroneous time extrapolation sampling in Scipy mode

parcels.tools.converters module

class parcels.tools.converters.Geographic[source]

Bases: parcels.tools.converters.UnitConverter

Unit converter from geometric to geographic coordinates (m to degree)

class parcels.tools.converters.GeographicPolar[source]

Bases: parcels.tools.converters.UnitConverter

Unit converter from geometric to geographic coordinates (m to degree) with a correction to account for narrower grid cells closer to the poles.

class parcels.tools.converters.GeographicPolarSquare[source]

Bases: parcels.tools.converters.UnitConverter

Square distance converter from geometric to geographic coordinates (m2 to degree2) with a correction to account for narrower grid cells closer to the poles.

class parcels.tools.converters.GeographicSquare[source]

Bases: parcels.tools.converters.UnitConverter

Square distance converter from geometric to geographic coordinates (m2 to degree2)

class parcels.tools.converters.TimeConverter(time_origin=0)[source]

Bases: object

Converter class for dates with different calendars in FieldSets

Param

time_origin: time origin of the class. Currently supported formats are float, integer, numpy.datetime64, and netcdftime.DatetimeNoLeap

fulltime(time)[source]

Method to convert a time difference in seconds to a date, based on the time_origin

Param

time: input time

Returns

self.time_origin + time

reltime(time)[source]

Method to compute the difference, in seconds, between a time and the time_origin of the TimeConverter

Param

time: input time

Returns

time - self.time_origin

class parcels.tools.converters.UnitConverter[source]

Bases: object

Interface class for spatial unit conversion during field sampling that performs no conversion.

parcels.tools.converters.convert_xarray_time_units(ds, time)[source]

Fixes DataArrays that have time.Unit instead of expected time.units

parcels.tools.loggers module

Script to create a logger for Parcels

parcels.plotting module

parcels.plotting.cartopy_colorbar(cs, plt, fig, ax)[source]
parcels.plotting.create_parcelsfig_axis(spherical, land=True, projection=None, central_longitude=0)[source]
parcels.plotting.parsedomain(domain, field)[source]
parcels.plotting.parsetimestr(time_origin, show_time)[source]
parcels.plotting.plotfield(field, show_time=None, domain=None, depth_level=0, projection=None, land=True, vmin=None, vmax=None, savefile=None, **kwargs)[source]

Function to plot a Parcels Field

Parameters
  • show_time – Time at which to show the Field

  • domain – dictionary (with keys ‘N’, ‘S’, ‘E’, ‘W’) defining domain to show

  • depth_level – depth level to be plotted (default 0)

  • projection – type of cartopy projection to use (default PlateCarree)

  • land – Boolean whether to show land. This is ignored for flat meshes

  • vmin – minimum colour scale (only in single-plot mode)

  • vmax – maximum colour scale (only in single-plot mode)

  • savefile – Name of a file to save the plot to

  • animation – Boolean whether result is a single plot, or an animation

parcels.plotting.plotparticles(particles, with_particles=True, show_time=None, field=None, domain=None, projection=None, land=True, vmin=None, vmax=None, savefile=None, animation=False, **kwargs)[source]

Function to plot a Parcels ParticleSet

Parameters
  • show_time – Time at which to show the ParticleSet

  • with_particles – Boolean whether particles are also plotted on Field

  • field – Field to plot under particles (either None, a Field object, or ‘vector’)

  • domain – dictionary (with keys ‘N’, ‘S’, ‘E’, ‘W’) defining domain to show

  • projection – type of cartopy projection to use (default PlateCarree)

  • land – Boolean whether to show land. This is ignored for flat meshes

  • vmin – minimum colour scale (only in single-plot mode)

  • vmax – maximum colour scale (only in single-plot mode)

  • savefile – Name of a file to save the plot to

  • animation – Boolean whether result is a single plot, or an animation

scripts.plottrajectoriesfile module

scripts.plottrajectoriesfile.plotTrajectoriesFile(filename, mode='2d', tracerfile=None, tracerfield='P', tracerlon='x', tracerlat='y', recordedvar=None, movie_forward=True, bins=20, show_plt=True)[source]

Quick and simple plotting of Parcels trajectories

Parameters
  • filename – Name of Parcels-generated NetCDF file with particle positions

  • mode – Type of plot to show. Supported are ‘2d’, ‘3d’, ‘hist2d’, ‘movie2d’ and ‘movie2d_notebook’. The latter two give animations, with ‘movie2d_notebook’ specifically designed for jupyter notebooks

  • tracerfile – Name of NetCDF file to show as background

  • tracerfield – Name of variable to show as background

  • tracerlon – Name of longitude dimension of variable to show as background

  • tracerlat – Name of latitude dimension of variable to show as background

  • recordedvar – Name of variable used to color particles in scatter-plot. Only works in ‘movie2d’ or ‘movie2d_notebook’ mode.

  • movie_forward – Boolean whether to show movie in forward or backward mode (default True)

  • bins – Number of bins to use in hist2d mode. See also https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist2d.html

  • show_plt – Boolean whether plot should directly be show (for py.test)

scripts.get_examples module

Get example scripts, notebooks, and data files.

scripts.get_examples.copy_data_and_examples_from_package_to(target_path)[source]

Copy example data from Parcels directory.

Return thos parths of the list file_names that were not found in the package.

scripts.get_examples.download_files(source_url, file_names, target_path)[source]

Mirror file_names from source_url to target_path.

scripts.get_examples.main(target_path=None)[source]

Get example scripts, example notebooks, and example data.

Copy the examples from the package directory and get the example data either from the package directory or from the Parcels website.

Indices and tables