pipeline.hsd.cli.hsd_baseline

pipeline.hsd.cli.hsd_baseline = <pipeline.hsd.cli.hsd_baseline._hsd_baseline object>

hsd_baseline —- Detect and validate spectral lines, subtract baseline by masking detected lines

The hsd_baseline task subtracts baseline from calibrated spectra. By default, the task tries to find spectral line feature using line detection and validation algorithms. Then, the task puts a mask on detected lines and perform baseline subtraction. The user is able to turn off automatic line masking by setting linewindow parameter, which specifies pre-defined line window.

Fitting order is automatically determined by default. It can be disabled by specifying fitorder as non-negative value. In this case, the value specified by fitorder will be used.

*WARNING* Currently, hsd_baseline overwrites the result obtained by the previous run. Due to this behavior, users need to be careful about an order of the task execution when they run hsd_baseline multiple times with different data selection. Suppose there are two spectral windows (0 and 1) and hsd_baseline is executed separately for each spw as below,

hsd_baseline(pipelinemode=”interactive”, spw=”0”) hsd_baseline(pipelinemode=”interactive”, spw=”1”) hsd_blflag(pipelinemode=”automatic”) hsd_imaging(pipelinemode=”automatic”)

Since the second run of hsd_baseline overwrites the result for spw 0 with the data before baseline subtraction, this will not produce correct result for spw 0. Proper sequence for this use case is to process each spw to the imaging stage separately, which looks like as follows:

hsd_baseline(pipelinemode=”interactive”, spw=”0”) hsd_blflag(pipelinemode=”interactive”, spw=”0”) hsd_imaging(pipelinemode=”interactive”, spw=”0”)) hsd_baseline(pipelinemode=”interactive”, spw=”1”) hsd_blflag(pipelinemode=”interactive”, spw=”1”) hsd_imaging(pipelinemode=”interactive”, spw=”1”)

Output: results – If pipeline mode is ‘getinputs’ then None is returned. Otherwise the results object for the pipeline task is returned.

——— parameter descriptions ———————————————

fitfunc fitting function for baseline subtraction. You can only

choose cubic spline (‘spline’ or ‘cspline’)

fitorder Fitting order for polynomial. For cubic spline, it is used

to determine how much the spectrum is segmented into. Default (-1) is to determine the order automatically.

switchpoly If True, switch to 1st or 2nd order polynomial fit when

large mask exists at edge regardless of whatever fitfunc or fitorder are specified. Condition for switching is as follows:

if nmask > nchan/2 => 1st order polynomial else if nmask > nchan/4 => 2nd order polynomial else => use fitfunc and fitorder

where nmask is a number of channels for mask at edge while nchan is a number of channels of entire spectral window.

linewindow Pre-defined line window. If this is set, specified line

windows are used as a line mask for baseline subtraction

instead to determine masks based on line detection and validation stage. Several types of format are acceptable. One is channel-based window,

[min_chan, max_chan]

where min_chan and max_chan should be an integer. For multiple windows, nested list is also acceptable,

[[min_chan0, max_chan0], [min_chan1, max_chan1], …]

Another way is frequency-based window,

[min_freq, max_freq]

where min_freq and max_freq should be either a float or a string. If float value is given, it is interpreted as a frequency in Hz. String should be a quantity consisting of “value” and “unit”, e.g., ‘100GHz’. Multiple windows are also supported.

[[min_freq0, max_freq0], [min_freq1, max_freq1], …]

Note that the specified frequencies are assumed to be the value in LSRK frame. Note also that there is a limitation when multiple MSes are processed. If native frequency frame of the data is not LSRK (e.g. TOPO), frequencies need to be converted to that frame. As a result, corresponding channel range may vary between MSes. However, current implementation is not able to handle such case. Frequencies are converted to desired frame using representative MS (time, position, direction).

In the above cases, specified line windows are applied to all science spws. In case when line windows vary with spw, line windows can be specified by a dictionary whose key is spw id while value is line window. For example, the following dictionary gives different line windows to spws 17 and 19. Other spws, if available, will have an empty line window.

{17: [[100, 200], [1200, 1400]], 19: [‘112115MHz’, ‘112116MHz’]}

Furthermore, linewindow accepts MS selection string. The following string gives [[100,200],[1200,1400]] for spw 17 while [1000,1500] for spw 21.

“17:100~200;1200~1400,21:1000~1500”

The string also accepts frequency with units. Note, however, that frequency reference frame in this case is not fixed to LSRK. Instead, the frame will be taken from the MS (typically TOPO for ALMA). Thus, the following two frequency-based line windows result different channel selections.

{19: [‘112115MHz’, ‘112116MHz’]} # frequency frame is LSRK “19:11215MHz~11216MHz” # frequency frame is taken from the data

# (TOPO for ALMA)

example: [100,200] (channel), [115e9, 115.1e9] (frequency in Hz)

[‘115GHz’, ‘115.1GHz’], see above for more examples

linewindowmode Merge or replace given manual line window with line

detection/validation result. If ‘replace’ is given, line detection and validation will not be performed. On the other hand, when ‘merge’ is specified, line detection/validation will be performed and manually specified line windows are added to the result. Note that this has no effect when linewindow for target spw is empty. In that case, line detection/validation will be performed regardless of the value of linewindowmode.

edge Number of edge channels to be dropped from baseline

subtraction. The value must be a list with length of 2, whose values specify left and right edge channels, respectively.

example: [10,10]

broadline Try to detect broad component of spectral line if True. clusteringalgorithm Selection of the algorithm used in the clustering

analysis to check the validity of detected line features.

‘kmean’ algorithm and hierarchical clustering algorithm ‘hierarchy’, and their combination (‘both’) are so far implemented.

deviationmask Apply deviation mask in addition to masks determined by

the automatic line detection.

pipelinemode The pipeline operating mode. In ‘automatic’ mode the

pipeline determines the values of all context defined pipeline inputs automatically. In ‘interactive’ mode the user can set the pipeline context defined parameters manually. In ‘getinputs’ mode the user can check the settings of all pipeline parameters without running the task.

infiles List of data files. These must be a name of

MeasurementSets that are registered to context via hsd_importdata task.

example: vis=[‘X227.ms’, ‘X228.ms’]

field Data selection by field.

example: ‘1’ (select by FIELD_ID)

‘M100*’ (select by field name) ‘’ (all fields)

antenna Data selection by antenna.

example: ‘1’ (select by ANTENNA_ID)

‘PM03’ (select by antenna name) ‘’ (all antennas)

spw Data selection by spw.

example: ‘3,4’ (generate caltable for spw 3 and 4)

[‘0’,’2’] (spw 0 for first data, 2 for second) ‘’ (all spws)

pol Data selection by polarizations.

example: ‘0’ (generate caltable for pol 0)

[‘0~1’,’0’] (pol 0 and 1 for first data, only 0 for second) ‘’ (all polarizations)

dryrun Run the commands (True) or generate the commands to be

run but do not execute (False).

acceptresults Add the results of the task to the pipeline context (True)

or reject them (False).

parallel Execute using CASA HPC functionality, if available.

——— examples ———————————————————–