##################### generated by xml-casa (v2) from hsd_baseline.xml ##############
##################### 47f8e57177d97a8585a9e44caca86fea ##############################
from __future__ import absolute_import
import numpy
from casatools.typecheck import CasaValidator as _val_ctor
_pc = _val_ctor( )
from casatools.coercetype import coerce as _coerce
from .private.task_hsd_baseline import hsd_baseline as _hsd_baseline_t
from casatasks.private.task_logging import start_log as _start_log
from casatasks.private.task_logging import end_log as _end_log
class _hsd_baseline:
"""
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 -----------------------------------------------------------
"""
_info_group_ = """pipeline"""
_info_desc_ = """Detect and validate spectral lines, subtract baseline by masking detected lines"""
def __call__( self, fitfunc='cspline', fitorder=int(-1), switchpoly=True, linewindow='', linewindowmode='replace', edge=[ ], broadline=True, clusteringalgorithm='hierarchy', deviationmask=True, pipelinemode='automatic', infiles=[ ], field='', antenna='', spw='', pol='', dryrun=False, acceptresults=True, parallel='automatic' ):
schema = {'fitfunc': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'cspline', 'spline', 'CSPLINE', 'SPLINE' ]}, 'fitorder': {'type': 'cInt'}, 'switchpoly': {'type': 'cBool'}, 'linewindow': {'type': 'cVariant', 'coerce': [_coerce.to_variant]}, 'linewindowmode': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'replace', 'merge' ]}, 'edge': {'type': 'cIntVec', 'coerce': [_coerce.to_list,_coerce.to_intvec]}, 'broadline': {'type': 'cBool'}, 'clusteringalgorithm': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'hierarchy', 'kmean', 'both' ]}, 'deviationmask': {'type': 'cBool'}, 'pipelinemode': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'automatic', 'interactive', 'getinputs' ]}, 'infiles': {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}, 'field': {'type': 'cStr', 'coerce': _coerce.to_str}, 'antenna': {'type': 'cStr', 'coerce': _coerce.to_str}, 'spw': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'pol': {'anyof': [{'type': 'cStr', 'coerce': _coerce.to_str}, {'type': 'cStrVec', 'coerce': [_coerce.to_list,_coerce.to_strvec]}]}, 'dryrun': {'type': 'cBool'}, 'acceptresults': {'type': 'cBool'}, 'parallel': {'type': 'cStr', 'coerce': _coerce.to_str, 'allowed': [ 'automatic', 'true', 'false' ]}}
doc = {'fitfunc': fitfunc, 'fitorder': fitorder, 'switchpoly': switchpoly, 'linewindow': linewindow, 'linewindowmode': linewindowmode, 'edge': edge, 'broadline': broadline, 'clusteringalgorithm': clusteringalgorithm, 'deviationmask': deviationmask, 'pipelinemode': pipelinemode, 'infiles': infiles, 'field': field, 'antenna': antenna, 'spw': spw, 'pol': pol, 'dryrun': dryrun, 'acceptresults': acceptresults, 'parallel': parallel}
assert _pc.validate(doc,schema), str(_pc.errors)
_logging_state_ = _start_log( 'hsd_baseline', [ 'fitfunc=' + repr(_pc.document['fitfunc']), 'fitorder=' + repr(_pc.document['fitorder']), 'switchpoly=' + repr(_pc.document['switchpoly']), 'linewindow=' + repr(_pc.document['linewindow']), 'linewindowmode=' + repr(_pc.document['linewindowmode']), 'edge=' + repr(_pc.document['edge']), 'broadline=' + repr(_pc.document['broadline']), 'clusteringalgorithm=' + repr(_pc.document['clusteringalgorithm']), 'deviationmask=' + repr(_pc.document['deviationmask']), 'pipelinemode=' + repr(_pc.document['pipelinemode']), 'infiles=' + repr(_pc.document['infiles']), 'field=' + repr(_pc.document['field']), 'antenna=' + repr(_pc.document['antenna']), 'spw=' + repr(_pc.document['spw']), 'pol=' + repr(_pc.document['pol']), 'dryrun=' + repr(_pc.document['dryrun']), 'acceptresults=' + repr(_pc.document['acceptresults']), 'parallel=' + repr(_pc.document['parallel']) ] )
return _end_log( _logging_state_, 'hsd_baseline', _hsd_baseline_t( _pc.document['fitfunc'], _pc.document['fitorder'], _pc.document['switchpoly'], _pc.document['linewindow'], _pc.document['linewindowmode'], _pc.document['edge'], _pc.document['broadline'], _pc.document['clusteringalgorithm'], _pc.document['deviationmask'], _pc.document['pipelinemode'], _pc.document['infiles'], _pc.document['field'], _pc.document['antenna'], _pc.document['spw'], _pc.document['pol'], _pc.document['dryrun'], _pc.document['acceptresults'], _pc.document['parallel'] ) )
hsd_baseline = _hsd_baseline( )