# sdbaseline¶

sdbaseline(infile, datacolumn='data', antenna='', field='', spw='', timerange='', scan='', pol='', intent='', reindex=True, maskmode='list', thresh=5.0, avg_limit=4, minwidth=4, edge=[0, 0], blmode='fit', dosubtract=True, blformat='text', bloutput='', bltable='', blfunc='poly', order=5, npiece=2, applyfft=True, fftmethod='fft', fftthresh=3.0, addwn=[0], rejwn='', clipthresh=3.0, clipniter=0, blparam='', verbose=False, updateweight=False, sigmavalue='stddev', showprogress=False, minnrow=1000, outfile='', overwrite=False)[source]

Fit/subtract a spectral baseline

[Description] [Examples] [Development] [Details]

Parameters
• infile (path) - name of input SD dataset

• datacolumn (string=’data’) - name of data column to be used [“data”, “float_data”, or “corrected”]

• antenna (string=’’) - select data by antenna name or ID, e.g. “PM03”

• field (string=’’) - select data by field IDs and names, e.g. “3C2*” (“”=all)

• spw (string=’’) - select data by IF IDs (spectral windows), e.g. “3,5,7” (“”=all)

• timerange (string=’’) - select data by time range, e.g. “09:14:0~09:54:0” (“”=all) (see examples in help)

• scan (string=’’) - select data by scan numbers, e.g. “21~23” (“”=all)

• pol (string=’’) - select data by polarization IDs, e.g. “XX,YY” (“”=all)

• intent (string=’’) - select data by observational intent, e.g. “ON_SOURCE” (“”=all)

• reindex (bool=True) - Re-index indices in subtables based on data selection

• thresh (double=5.0) - S/N threshold for linefinder

• avg_limit (int=4) - channel averaging for broad lines

• minwidth (int=4) - the minimum channel width to detect as a line

• edge (intVec=[0, 0]) - channels to drop at beginning and end of spectrum

• blmode (string=’fit’) - baselining mode [“fit” or “apply”]

blmode = fit
• dosubtract (bool=True) - subtract baseline from input data [True, False]

• blformat ({string, stringVec}=’text’) - format(s) of file(s) in which best-fit parameters are written

• bloutput ({string, stringVec}=’’) - name(s) of file(s) in which best-fit parameters are written

blmode = apply
• bltable (string=’’) - name of baseline table to apply

• blfunc (string=’poly’) - baseline model function

blfunc = poly
• order (int=5) - order of baseline model function

• clipthresh (double=3.0) - clipping threshold for iterative fitting

• clipniter (int=0) - maximum iteration number for iterative fitting

blfunc = chebyshev
• order (int=5) - order of baseline model function

• clipthresh (double=3.0) - clipping threshold for iterative fitting

• clipniter (int=0) - maximum iteration number for iterative fitting

blfunc = cspline
• npiece (int=2) - number of element polynomials for cubic spline curve

• clipthresh (double=3.0) - clipping threshold for iterative fitting

• clipniter (int=0) - maximum iteration number for iterative fitting

blfunc = sinusoid
• applyfft (bool=True) - automatically set wave numbers of sinusoids

• fftmethod (string=’fft’) - method for automatically set wave numbers of sinusoids [“fft”]

• fftthresh (double=3.0) - threshold to select wave numbers of sinusoids

• rejwn (intVec=’’) - wave numbers NOT to use

• clipthresh (double=3.0) - clipping threshold for iterative fitting

• clipniter (int=0) - maximum iteration number for iterative fitting

blfunc = variable
• blparam (string=’’) - text file that stores per spectrum fit parameters

• verbose (bool=False) - output fitting parameters to logger [True, False]

• updateweight (bool=False) - update WEIGHT column [True, False]

updateweight = True
• sigmavalue (string=’stddev’) - value used for computing weight

• showprogress (bool=False) - (NOT SUPPORTED YET) show progress status for large data [True, False] (NOT SUPPORTED YET)

showprogress = True
• minnrow (int=1000) - (NOT SUPPORTED YET) minimum number of input spectra to show progress status

• outfile (string=’’) - name of output file

• overwrite (bool=False) - overwrite the output file if already exists [True, False]

Description

Task sdbaseline fits and/or subtracts a baseline from single-dish spectra in MS format. Given parameters that define the baseline to be fit (function type, order or the polynomial, etc.), sdbaseline computes the best-fit baseline for each spectrum using the least-squares fitting method and, if you want, subtracts it. The best-fit baseline parameters (including baseline type, coefficients of basis functions, etc.) and other values such as residual rms can be saved in various formats including ascii text (in human-readable format or CSV format) or a baseline table (a CASA table). Task sdbaseline also has a mode to ‘apply’ a baseline table to MS data. For each spectrum in the MS, the best-fit baseline is reproduced from baseline parameters stored in the specified baseline table, and subtracted. Putting the “fit” and “subtract” into separate processes can be useful for pipeline processing of huge datasets.

Baseline Model Functions

The user can specify the function to be used for the baseline with the blfunc parameter (e.g. blfunc = ‘poly’). In general, polynomial fitting is stable. Sinusoid fitting is a special mode that could be useful for data that clearly shows a standing wave in the spectral baseline.

In addition to fitting with a single function type, users can also specify unique baseline fitting parameters for each spectrum by setting blfunc=’variable’. See ‘Per-spectrum Fit Parameters’ section below for details.

Output Files

The task outputs the baseline-subtracted MS data set. Users should specify the output data file name with the outfile keyword.

Also, the fit parameters, terms, and rms of the baseline can be saved into an ascii text file (in human-readable format or CSV format) or a baseline table (a CASA table). By default, a text file named <infile name> + ‘_ blparam.txt’ is output. The saved baseline table can be used later to subtract the baselines from an MS.

Fitting and Clipping

In general, least-squares fitting is strongly affected by extreme data points, making the resulting fit poor. Sigma clipping is an iterative baseline fitting method that clips data based on a certain threshold. The threshold is set as a certain factor times the rms of the resulting (baseline-subtracted) spectra. If sigma clipping is on, baseline fit/removal is performed several times, iteratively. After each baseline subtraction, data whose absolute value is above the threshold are excluded from the next round of fitting. By using sigma clipping, extreme data are excluded from the fit so the resulting fit is more robust.

The user can control the rms multiplication factor using the parameter clipthresh, for the clipping threshold. The actual threshold for sigma clipping will then be (clipthresh) x (rms of spectra). Also, the user can specify the maximum number of iterations with the parameter clipniter.

In general, sigma clipping will make the procedure slower since it increases the number of fits per spectra. However, it is strongly recommended to turn on sigma clipping unless you are sure that the data is free from any kind of extreme values that may affect the fit.

Update Weight

Setting the parameter updateweight = True, the WEIGHT column is updated as $$1/(sigmavalue)^2$$ according to the sigmavalue parameter (“stddev” or “rms”), where “stddev” calculates the standard deviation of the baseline-subtracted spectrum and “rms” does the root mean square. The calculation is done with unflagged channels only.

Note that the SIGMA column is not updated; it keeps the values of the input MS data. In case the user wants to refer to the standard deviation of the output MS data, she or he needs to compute it using WEIGHT column values as $$1/\sqrt{WEIGHT}$$ - the SIGMA column should not be refered to.

Per-spectrum Fit Parameters

Per-spectrum baseline fitting parameters can be applied when blfunc = ‘variable’.

The fitting parameters can be defined in a text file and specified in the blparam parameter. Each line of the text file should store baseline fitting parameters for its corresponding spectrum in the input MS. It must be a comma-separated text and contain values in the following order:

1. ‘row’: row index

2. ‘pol’: polarization index in the specified row

3. ‘mask’: channel range(s) used for the fitting (see examples below).

4. ‘clipniter’: maximum number of times of iterative fitting (identical to the task parameter clipniter)

5. ‘clipthresh’: clipping threshold for iterative fitting (identical to the task parameter clipthresh)

6. ‘use_linefinder’: “true” or “false”. Note that linefinder does not run with per-spectrum fitting now even if setting “true”, due to a bug which will be fixed in the future

7. ‘thresh’: S/N threshold for linefinder (identical to the task parameter thresh). Blank is accepted when you don’t use linefinder

8. ‘left_edge’: channels to drop at beginning of spectrum (identical to the first element of the task parameter edge)

9. ‘right_edge’: channels to drop at end of spectrum (identical to the second element of the task parameter edge)

10. ‘avg_limit’: channel averaging for broad lines (identical to the task parameter avg_limit)

11. ‘blfunc’: baseline model function (identical to the task parameter blfunc)

12. ‘order’: order of polynomial function (identical to the task parameter order). Needed when (11) is “poly” or “chebyshev”. It will be ignored when other values are set for blfunc

13. ‘npiece’: number of the element polynomials of cubic spline curve (identical to the task parameter npiece). Needed when (11) is “cspline”

14. ‘nwave’: a list of sinusoidal wave numbers. Needed when (11) is “sinusoid” though, actually, sinusoidal fitting is yet to be available with per-spectrum fitting

Note that the following task parameters will be ignored/overwritten when blfunc = ‘variable’ is specified (i.e., when per-spectrum fitting is executed):

• for iterative clipping: clipniter, clipthresh

• for linefinder: thresh, edge, avg_limit

• for baseline model function: blfunc, order, npiece, applyfft, fftmethod, fftthresh, addwn, rejwn

Note also that:

1. lines starting with ‘#’ will be ignored and can be used as comments

2. for MS spectra which have no corresponding line in the text file, baseline fitting is not executed

Examples of text file:

1. a simple one:

0,0,,2,3,false,,,,,poly,5,,[]
0,1,1500~7500,0,3.,false,0.,0,0,0,chebyshev,10,0,[]
1,0,,4,2.5,true,5.,70,80,3,cspline,,6,[]
1,1,0~4000;6000~8000,0,,false,,,,,sinusoid,,,[0,1,2,3,4,5,6,7]
#2,0,,0,,false,,,,,poly,10,,[]

1. same setting as (1), but with detailed comments:

# for row 0, pol 0: no channel mask,
#                   iterative (twice at maximum) clipping at 3 sigma,
#                   no linefinder,
#                   fitting with polynomial of order 5
0,0,,2,3,false,,,,,poly,5,,[]
# for row 0, pol 1: use channel range 1500 to 7500,
#                   no iterative clipping (clipniter=0),
#                   no linefinder,
#                   fitting with Chebyshev polynomial of order 10
0,1,1500~7500,0,3.,false,0.,0,0,0,chebyshev,10,0,[]
# for row 1, pol 0: no channel mask,
#                   iterative (4 times at maximum) clipping at 2.5 sigma,
#                   using linefinder (thresh: 5.0 sigma,
#                                     left_edge: 70 channels,
#                                     right_edge: 80 channels,
#                                     avg_limit: 3),
#                   fitting with cubic spline with 6 elements
1,0,,4,2.5,true,5.,70,80,3,cspline,,6,[]
# for row 1, pol 1: use channel ranges (0 to 4000) and (6000 to 8000),
#                   no iterative clipping,
#                   no linefinder,
#                   fitting with sinusoids with wave numbers up to 7
1,1,0~4000;6000~8000,0,,false,,,,,sinusoid,,,[0,1,2,3,4,5,6,7]
# for row 2, pol 0: no baseline fitting as the line is commented out
#2,0,,0,,false,,,,,poly,10,,[]

Examples

Example 1

This is one of the simplest examples. To fit and remove a Chebyshev polynomial function (default is of 5th order) from the data ‘sd_data.ms’, using only spectral window 0, and fitting channels 100-800 and 1200-2000 (to avoid, for example, band-pass roll off at the edges, and perhaps an emission line that might occur over channels 800-1200).

sdbaseline(infile='sd_data.ms', spw='0:100~800;1200~2000', blfunc='chebyshev',
outfile='sd_data.ms.bl', overwrite=True)


Example 2

This example shows fitting and subtracting a sinusoidal baseline. To fit and remove a sinusoid from the data ‘sd_data.ms’, using spectral window 0 and scan number 0. Wave numbers of sinusoids are set autmatically in the fft method.

sdbaseline(infile='sd_data.ms', spw='0', scan='0', blfunc='sinusoid', applyfft=True,
fftmethod='fft', outfile='sd_data.ms.bl', overwrite=True)


Example 3

In this example, the user specifies different fitting parameters per spectrum, using blfunc=’variable’ and specifying the fit parameters using a text file.

sdbaseline(infile='sd_data.ms', blfunc='variable', blparam='blparam.txt',
outfile='sd_data.ms.bl', overwrite=True)


Here is the text file “blparam.txt” used in the above example.

#row,pol,mask,clipniter,clipthresh,use_linefinder,thresh,Ledge,Redge,avg_limit,blfunc,order,npiece,nwave
0,0,100~750;1250~1900,0,3.,false,0.,0,0,0,chebyshev,2,0,[]
0,1,,0,3.,false,0.,0,0,0,chebyshev,0,0,[]
1,0,0~500;1500~2000,0,3.,false,0.,0,0,0,poly,1,0,[]


Example 4

This is an example of fitting and subtracting a polynomial baseline, and also updating the WEIGHT column of the output MS ‘sd_data.ms.bl’ as $$1/RMS^2$$ .

sdbaseline(infile='sd_data.ms', blfunc='poly', updateweight=True, sigmavalue='rms',
outfile='sd_data.ms.bl', overwrite=True)


Example 5

This example shows a polynomial baseline fitting, but without subtraction; instead, the fitting results are saved as a text file ‘sd_data_blparam.txt’ and a baseline table ‘sd_data_blparam.bltable’, which can be used for actual baseline subtraction afterwards (see also Example 6).

sdbaseline(infile='sd_data.ms', blfunc='poly', dosubtract=False, blformat=['text','table'])


Example 6

This example shows applying a baseline table to a MS to actually subtract the best-fit baseline.

sdbaseline(infile='sd_data.ms', blmode='apply', bltable='sd_data_blparam.bltable',
outfile='sd_data.ms.bl')

Development

Parameter Details

Detailed descriptions of each function parameter

infile (path) - name of input SD dataset
datacolumn (string='data') - name of data column to be used [“data”, “float_data”, or “corrected”]
antenna (string='') - select data by antenna name or ID, e.g. “PM03”
field (string='') - select data by field IDs and names, e.g. “3C2*” (“”=all)
spw (string='') - select data by IF IDs (spectral windows), e.g. “3,5,7” (“”=all)
timerange (string='') - select data by time range, e.g. “09:14:0~09:54:0” (“”=all) (see examples in help)
scan (string='') - select data by scan numbers, e.g. “21~23” (“”=all)
pol (string='') - select data by polarization IDs, e.g. “XX,YY” (“”=all)
intent (string='') - select data by observational intent, e.g. “ON_SOURCE” (“”=all)
reindex (bool=True) - Re-index indices in subtables based on data selection. Ignored when blmode=’apply’.
maskmode (string='list') - mode of setting additional channel masks. “list” and “auto” are available now.
thresh (double=5.0) - S/N threshold for linefinder
avg_limit (int=4) - channel averaging for broad lines
minwidth (int=4) - the minimum channel width to detect as a line
edge (intVec=[0, 0]) - channels to drop at beginning and end of spectrum
blmode (string='fit') - baselining mode [“fit” or “apply”]
dosubtract (bool=True) - subtract baseline from input data [True, False]
blformat ({string, stringVec}='text') - format(s) of file(s) in which best-fit parameters are written [“text”, “csv”, “table” or “”]
bloutput ({string, stringVec}='') - name(s) of file(s) in which best-fit parameters are written
bltable (string='') - name of baseline table to apply
blfunc (string='poly') - baseline model function [“poly”, “chebyshev”, “cspline”, “sinusoid”, or “variable”(expert mode)]
order (int=5) - order of baseline model function
npiece (int=2) - number of element polynomials for cubic spline curve
applyfft (bool=True) - automatically set wave numbers of sinusoids
fftmethod (string='fft') - method for automatically set wave numbers of sinusoids
fftthresh (double=3.0) - threshold to select wave numbers of sinusoids
addwn (intVec=[0]) - additional wave numbers to use
rejwn (intVec='') - wave numbers NOT to use
clipthresh (double=3.0) - clipping threshold for iterative fitting
clipniter (int=0) - maximum iteration number for iterative fitting
blparam (string='') - text file that stores per spectrum fit parameters
verbose (bool=False) - output fitting parameters to logger
updateweight (bool=False) - update WEIGHT column based on sigmavalue computed over unmasked range
sigmavalue (string='stddev') - value used for computing weight [“stddev” or “rms”]
showprogress (bool=False) - (NOT SUPPORTED YET) show progress status for large data
minnrow (int=1000) - (NOT SUPPORTED YET) minimum number of input spectra to show progress status
outfile (string='') - name of output file
overwrite (bool=False) - overwrite the output file if already exists