rsexecute

rsexecute workflows can be used in two modes

delayed using Dask.delayed

serially executed immediately on definition,

Distribution is acheived by working on lists of data models, such as lists of BlockVisibilities.

For example:

from rascil.workflows import continuum_imaging_list_rsexecute_workflow, rsexecute
rsexecute.set_client(use_dask=True, threads_per_worker=1,
    memory_limit=32 * 1024 * 1024 * 1024, n_workers=8,
    local_dir=dask_dir, verbose=True)
continuum_imaging_list = continuum_imaging_list_rsexecute_workflow(vis_list,
    model_imagelist=model_list,
    context='wstack', vis_slices=51,
    scales=[0, 3, 10], algorithm='mmclean',
    nmoment=3, niter=1000,
    fractional_threshold=0.1, threshold=0.1,
    nmajor=5, gain=0.25,
    psf_support=64)

deconvolved_list, residual_list, restored_list = rsexecute.compute(continuum_imaging_list,
    sync=True)

The call to continuum_imaging_list_rsexecute_workflow does not execute immediately just generates a Dask.delayed object that can be computed subsequently. The higher level functions such as continuum_imaging_list_rsexecute_workflow are built from lower level functions such as invert_list_rsexecute_workflow.

In this example, changing use_dask to False will cause the definitions to be executed immediately.

The rsexecute framework relies upon a singleton object called rsexecute. This is documented below as the class _rsexecutebase.

rascil.workflows.rsexecute.calibration Package

Workflows for calibration

Functions

calibrate_list_rsexecute_workflow(vis_list, ...)

Create a set of components for (optionally global) calibration of a list of visibilities

rascil.workflows.rsexecute.image Package

Workflows for operating on images

Functions

`image_gather_channels_rsexecute`(image_list)	Gather a set of images in frequency, using a tree reduction or directly
`image_rsexecute_map_workflow`(im, imfunction)	Apply a function across an image: scattering to subimages, applying the function, and then gathering
`sum_images_rsexecute`(image_list[, split])	Sum a set of images, using a tree reduction

rascil.workflows.rsexecute.imaging Package

Functions

`deconvolve_list_channel_rsexecute_workflow`(...)	Create a graph for deconvolution by channels, adding to the model
`deconvolve_list_rsexecute_workflow`(...[, ...])	Create a graph for deconvolution, adding to the model
`invert_list_rsexecute_workflow`(vis_list, ...)	Sum results from invert, iterating over the scattered image and vis_list
`predict_list_rsexecute_workflow`(vis_list, ...)	Predict, iterating over both the scattered vis_list and image
`residual_list_rsexecute_workflow`(vis, ...[, ...])	Create a graph to calculate (list or graph) of residual images
`restore_centre_rsexecute_workflow`(...[, ...])	Create a graph to calculate the restored image
`restore_list_rsexecute_workflow`(...[, ...])	Create a graph to calculate the restored image
`subtract_list_rsexecute_workflow`(vis_list, ...)	Initialise vis to zero
`sum_invert_results_rsexecute`(image_list)	Sum a set of invert results with appropriate weighting
`sum_predict_results_rsexecute`(bvis_list[, split])	Sum a set of predict results
`taper_list_rsexecute_workflow`(vis_list, ...)	Taper to desired size
`threshold_list_rsexecute`(imagelist[, prefix])	Find actual threshold for list of results
`weight_list_rsexecute_workflow`(vis_list, ...)	Weight the visibility data
`zero_list_rsexecute_workflow`(vis_list[, copy])	Creates a new vis_list and initialises all to zero

rascil.workflows.rsexecute.pipelines Package

Functions

`continuum_imaging_skymodel_list_rsexecute_workflow`(...)	Create graph for the continuum imaging pipeline.
`ical_skymodel_list_rsexecute_workflow`(...[, ...])	Create graph for ICAL pipeline using SkyModel
`spectral_line_imaging_skymodel_list_rsexecute_workflow`(...)	Create graph for spectral line imaging pipeline

rascil.workflows.rsexecute.simulation Package

Functions

`corrupt_list_rsexecute_workflow`(vis_list[, ...])	Create a graph to apply gain errors to a vis_list
`create_atmospheric_errors_gaintable_rsexecute_workflow`(...)	Create gaintable for atmospheric errors
`create_heterogeneous_gaintable_rsexecute_workflow`(...)	Create gaintable for polarisation effects
`create_pointing_errors_gaintable_rsexecute_workflow`(...)	Create gaintable for pointing errors
`create_polarisation_gaintable_rsexecute_workflow`(...)	Create gaintable for polarisation effects
`create_standard_low_simulation_rsexecute_workflow`(...)	Create the standard LOW simulation
`create_standard_mid_simulation_rsexecute_workflow`(...)	Create the standard MID simulation
`create_surface_errors_gaintable_rsexecute_workflow`(...)	Create gaintable for surface errors :param band: B1, B2 or Ku :param sub_bvis_list: List of vis (or graph) :param sub_components: List of components (or graph) :param vp_directory: Location of voltage patterns :param elevation_sampling: Sampling in elevation (degrees) :return: (list of error-free gaintables, list of error gaintables) or graph
`create_voltage_pattern_gaintable_rsexecute_workflow`(...)	Create gaintable for nominal voltage pattern
`simulate_list_rsexecute_workflow`([config, ...])	A component to simulate an observation

rascil.workflows.rsexecute.skymodel Package

Functions

`deconvolve_skymodel_list_rsexecute_workflow`(...)	Deconvolve using a skymodel
`invert_skymodel_list_rsexecute_workflow`(...)	Calibrate and invert from a skymodel, iterating over the skymodel
`predict_skymodel_list_rsexecute_workflow`(...)	Predict from a list of skymodels
`restore_centre_skymodel_list_rsexecute_workflow`(...)	Create a graph to calculate the restored skymodel at the centre channel
`restore_skymodel_list_rsexecute_workflow`(...)	Create a graph to calculate the restored image

rascil.workflows.rsexecute.execution_support Package

Functions

get_dask_client([timeout, n_workers, ...])

Get a Dask.distributed Client to be used in rsexecute

Classes

The rsexecute framework relies upon a singleton object called rsexecute. This is documented below as the class _rsexecutebase. Note that by design it is not possible to create more than one _rsexecutebase object.

class _rsexecutebase(use_dask=True, use_dlg=False, verbose=False, optimize=True)[source]

Initialise rsexecute framework

A singleton of this class is created and is available globally as rsexecute. Hence it is not necessary to declare an instance of _rsexecutebase.

For example:

from rascil.workflows import continuum_imaging_list_rsexecute_workflow, rsexecute
rsexecute.set_client(use_dask=True,
    memory_limit=32 * 1024 * 1024 * 1024, n_workers=8,
    local_dir=dask_dir, verbose=True)
continuum_imaging_list = continuum_imaging_list_rsexecute_workflow(vis_list,
    model_imagelist=model_list,
    context='wstack', vis_slices=51,
    scales=[0, 3, 10], algorithm='mmclean',
    nmoment=3, niter=1000,
    fractional_threshold=0.1, threshold=0.1,
    nmajor=5, gain=0.25,
    psf_support=64)

deconvolved_list, residual_list, restored_list = rsexecute.compute(continuum_imaging_list,
    sync=True)

Parameters:

use_dask – Use dask (True)
use_dlg – Use daluige (False)
verbose – Be verbose in printing messages
optimize – Optimize if using dask (True)

execute(func, *args, **kwargs)[source]

Wrap for immediate or deferred execution

Passes through if dask is not being used

Parameters:

args –
kwargs –

Returns:

delayed func or func

type()[source]

Get the name of the execution system

Returns:

set_client(client=None, use_dask=True, use_dlg=False, verbose=False, optim=True, **kwargs)[source]

Set the Dask/DALiuGE client to be used

If you want to customise the Client or use an externally defined Scheduler use get_dask_client and pass it in.

Parameters:

use_dask – Use Dask?
client – If None and use_dask is True, a client will be created otherwise the client is None
use_dlg – Use Daliuge to execute graphs?
verbose – Be verbose in output
optim – Use dask.optimize via rsexecute.optimize function.

Returns:

compute(value, sync=False)[source]

Get the actual value

If not using dask then this returns the value directly since it already is computed If using dask and sync=True then this waits and resturns the actual wait. If using dask and sync=False then this returns a future, on which you will need to call .result()

Parameters:

value –
sync – Return synchronously? (False)

Returns:

persist(graph, **kwargs)[source]

Persist graph data on workers

The graphs are placed on the workers but not computed

No-op if not using_dask

Parameters:: graph –
Returns:

scatter(graph, **kwargs)[source]

Scatter graph data to workers

The data are placed on the workers

No-op if not using_dask :param graph: :return:

gather(graph)[source]

Gather graph from workers

The data are gathered from the workers

No-op if not using_dask

Parameters:: graph –
Returns:

run(func, *args, **kwargs)[source]

Run a function on the client

Parameters:: func –
Returns:

optimize(*args, **kwargs)[source]

Run Dask optimisation of graphs

Only does something when using dask

Parameters:

args – for Dask.optimize
kwargs – for Dask.optimize

Returns:

close()[source]: Close the client

init_statistics()[source]

Initialise the profile and task stream info

rsexecute can save the Dask profile and Task Stream information for later saving

Returns:

save_statistics(name='dask')[source]

Save the statistics to html files

rsexecute can save the Dask profile and Task Stream information for later saving. This saves the current statistics to html files.

Parameters:: name – prefix to name e.g. dask

memusage(memusage_file='memusage.csv')[source]

Install the dask-memusage plugin

https://github.com/itamarst/dask-memusage/blob/master/dask_memusage.py

Note that there can only be one dask thread per process.

This only works for the process scheduler. For the distributed scheduler, preload the plugin. For example:

dask-scheduler –port=8786 –preload dask_memusage –memusage-csv ./memusage.csv

Parameters:: memusage_file – Name of mem-usage file produced by dask-memusage plugin
Returns:

property client

Client being used

Returns:: client

property using_dask

Is dask being used?

Returns:

property using_dlg

Is daluige being used?

Returns:

property optimizing

Is Dask optimisation being performed?

Returns: