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### Config file for Purify
Version: 4.2.0
GeneralConfiguration:
InputOutput:
input:
# If the following parameter is "measurements", only the measurements block should
# be provided, and the measurements will be read from file. If it's "simulation", only the
# simulation block should be provided, and random measurements of the given
# image will be generated with the requested noise.
source: simulation # one from measurements, simulation
simulation:
skymodel: /path/to/sky/image # path to the sky model image file
signal_to_noise: 10 # signal-to-noise ratio to be added to the sky model
number_of_measurements: 10000 # number of measurements per a node
w_rms: 30. # RMS w value in random uvw coverage
sim_J: 8
coverage_files: #path to converage units. This is only used if it is not an empty string
- path/to/coverage/measurement/file
coverage_units: lambda # one from lambda, radians, pixels
output_prefix: /path/to/output/dir # results directory will be created inside this path, and called output_<timestamp>
### Execution parameters
logging: debug # How much logging you want (debug, critical, ..)# FIXME: add the options, compile?
iterations: 100 # number of iterations (>= 0)
epsilonScaling: 1 # RMS noise (> 0)
## modelImage: # If none epsilonL2 * RealImage # FIXME - maybe to delete
########## Measure Operators ##########
MeasureOperators:
pixelSize:
cellsizex: 1
cellsizey: 1
imageSize: # for the moment: power of two
width: 1024
height: 1024
J: # Interpolation pixels (default 4x4). Input either a value or pair
Jx: 4
Jy: 4
Jw: 30
kernel: kb # kernel, choose between: kb, Gauss, box
oversampling: 2 # value > 1
powermethod:
iters: 100 # value > 0
tolerance: 1e-4 # value > 0
eigenvector:
real: ""
imag: ""
wide-field:
wprojection: False # using radially symmetric w projection kernel
mpi_wstacking: False # applies average w-stack correction on each node (always True with wprojection)
mpi_all_to_all: False # performs all to all operation of the grid to even out computation
conjugate_w: True #reflects measurements onto the positive w-domain (can reduce computation)
kmeans_iterations: 1000 #number of iterations in w-stacking clustering algorithm
gpu: False
# TODO: Add others like weighting. (at the moment natural)
########## SARA ##########
SARA:
# wavelet bases, to choose from: Dirac, DB1, ..., DBn (where n=0 is dirac, and 1 <= n <= 35 is DB basis)
wavelet_dict: 0..8 # sequence of desired basis: 0,23,31 or 0,12..16,20
wavelet_levels: 4 # value for all
realValueConstraint: True # Boolean
positiveValueConstraint: True #boolean
AlgorithmOptions:
algorithm: primaldual # will just read the options of that one.
########## PADMM ##########
padmm:
epsilonConvergenceScaling: 1 # Default 1 (>0)
# Following is only accepted when MPI is used
mpiAlgorithm: serial-equivalent # one of none, serial-equivalent, fully-distributed
# Be careful changing the following unless you know what you are doing
relVarianceConvergence: 1e-3 # (>0)
dualFBVarianceConvergence: 1e-3 # (>0) relative convergence tolerance of l1 proximal
stepsize: # Stepsize, choose value between: default, variable, 100, x
update_tolerance: 1e-1 #when predicted stepsize is greater than this value, it will be updated
update_iters: 0 #how many iterations to allow updating of step size
fb: #solve the unconstrained problem
mpiAlgorithm: serial-equivalent # one of none, serial-equivalent
regularisation_parameter: 1
stepsize: 1
dualFBVarianceConvergence: 1e-3 # (>0) relative convergence tolerance of l1 proximal
relVarianceConvergence: 1e-3 # (>0) relative convergence of the objective function
primaldual: #solve the constrained problem
# Following is only accepted when MPI is used
mpiAlgorithm: serial-equivalent # one of none, serial-equivalent, fully-distributed
# Be careful changing the following unless you know what you are doing
relVarianceConvergence: 1e-3 # (>0) relative converegence tolerance of objective function
precondition_iters: 0 #use sampling density (based on field of view) to accelerate primal dual
epsilonConvergenceScaling: 1 # (>0) l2 convergence tolerance for residuals
stepsize: # Stepsize, choose value between: default, variable, 100, x
update_tolerance: 1e-1 #when predicted stepsize is greater than this value, it will be updated
update_iters: 0 #how many iterations to allow updating of step size
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