statwt – Compute and set weights based on variance of data. – manipulation task

Description

Parameters

Title

Parameter	Default	Description
vis	''	Name of measurement set
selectdata	True	Enable data selection parameters
field	''	Selection based on field names or field index numbers. Default is all.
spw	''	Selection based on spectral windows:channels. Default is all.
intent	''	Selection based on intents. Default is all.
array	''	Selection based on array IDs. Default is all.
observation	''	Selection based on observation IDs. Default is all.
scan	''	Select data by scan numbers.
combine	''	Ignore changes in these columns (scan, field, and/or state) when aggregating samples to compute weights. The value “corr” is also supported to aggregate samples across correlations.
timebin	int(1)	Length for binning in time to determine statistics. Can either be integer to be multiplied by the representative integration time, a quantity (string) in time units
slidetimebin	False	Use a sliding window for time binning, as opposed to time block processing?
chanbin	'spw'	Channel bin width for computing weights. Can either be integer, in which case it is interpreted as number of channels to include in each bin, or a string “spw” or quantity with frequency units.
minsamp	int(2)	Minimum number of unflagged visibilities required for computing weights in a sample. Must be >= 2.
statalg	'classic'	Statistics algorithm to use for computing variances. Supported values are “chauvenet”, “classic”, “fit-half”, and “hinges-fences”. Minimum match is supported, although the full string must be specified for the subparameters to appear in the inputs list.
fence	float(-1)	Fence value for statalg=”hinges-fences”. A negative value means use the entire data set (ie default to the “classic” algorithm). Ignored if statalg is not “hinges-fences”.
center	'mean'	Center to use for statalg=”fit-half”. Valid choices are “mean”, “median”, and “zero”. Ignored if statalg is not “fit-half”.
lside	True	For statalg=”fit-half”, real data are <=; center? If false, real data are >= center. Ignored if statalg is not “fit-half”.
zscore	float(-1)	For statalg=”chauvenet”, this is the target maximum number of standard deviations data may have to be included. If negative, use Chauvenet's criterion. Ignored if statalg is not “chauvenet”.
maxiter	int(-1)	For statalg=”chauvenet”, this is the maximum number of iterations to attempt. Iterating will stop when either this limit is reached, or the zscore criterion is met. If negative, iterate until the zscore criterion is met. Ignored if statalg is not “chauvenet”.
fitspw	''	Channels to include in the computation of weights. Specified as an MS select channel selection string.
excludechans	False	If True: invert the channel selection in fitspw and exclude the fitspw selection from the computation of the weights.
wtrange	numpy.array( [  ] )	Range of acceptable weights. Data with weights outside this range will be flagged. Empty array (default) means all weights are good.
flagbackup	True	Back up the state of flags before the run?
preview	False	Preview mode. If True, no data is changed, although the amount of data that would have been flagged is reported.
datacolumn	'corrected'	Data column to use to compute weights. Supported values are “data”, “corrected”, “residual”, and “residual_data” (case insensitive, minimum match supported).

Parameter Explanations

vis

''

Name of measurement set

selectdata

True

Enable data selection parameters

field

''

Selection based on field names or field index numbers. Default is all.

spw

''

Selection based on spectral windows:channels. Default is all.

intent

''

Selection based on intents. Default is all.

array

''

Selection based on array IDs. Default is all.

observation

''

Selection based on observation IDs. Default is all.

scan

''

Select data by scan numbers.

combine

''

Ignore changes in these columns (scan, field, and/or state) when aggregating samples to compute weights. The value “corr” is also supported to aggregate samples across correlations.

timebin

int(1)

Length for binning in time to determine statistics. Can either be integer to be multiplied by the representative integration time, a quantity (string) in time units

slidetimebin

False

Use a sliding window for time binning, as opposed to time block processing?

chanbin

'spw'

Channel bin width for computing weights. Can either be integer, in which case it is interpreted as number of channels to include in each bin, or a string “spw” or quantity with frequency units.

minsamp

int(2)

Minimum number of unflagged visibilities required for computing weights in a sample. Must be >= 2.

statalg

'classic'

Statistics algorithm to use for computing variances. Supported values are “chauvenet”, “classic”, “fit-half”, and “hinges-fences”. Minimum match is supported, although the full string must be specified for the subparameters to appear in the inputs list.

fence

float(-1)

Fence value for statalg=”hinges-fences”. A negative value means use the entire data set (ie default to the “classic” algorithm). Ignored if statalg is not “hinges-fences”.

center

'mean'

Center to use for statalg=”fit-half”. Valid choices are “mean”, “median”, and “zero”. Ignored if statalg is not “fit-half”.

lside

True

For statalg=”fit-half”, real data are <=; center? If false, real data are >= center. Ignored if statalg is not “fit-half”.

zscore

float(-1)

For statalg=”chauvenet”, this is the target maximum number of standard deviations data may have to be included. If negative, use Chauvenet's criterion. Ignored if statalg is not “chauvenet”.

maxiter

int(-1)

For statalg=”chauvenet”, this is the maximum number of iterations to attempt. Iterating will stop when either this limit is reached, or the zscore criterion is met. If negative, iterate until the zscore criterion is met. Ignored if statalg is not “chauvenet”.

fitspw

''

Channels to include in the computation of weights. Specified as an MS select channel selection string.

excludechans

False

If True: invert the channel selection in fitspw and exclude the fitspw selection from the computation of the weights.

wtrange

numpy.array( [  ] )

Range of acceptable weights. Data with weights outside this range will be flagged. Empty array (default) means all weights are good.

flagbackup

True

Back up the state of flags before the run?

preview

False

Preview mode. If True, no data is changed, although the amount of data that would have been flagged is reported.

datacolumn

'corrected'

Data column to use to compute weights. Supported values are “data”, “corrected”, “residual”, and “residual_data” (case insensitive, minimum match supported).