def calnod(scantab, scannos=[], smooth=1, tsysval=0.0, tauval=0.0, tcalval=0.0, verify=False):
"""
Do full (but a pair of scans at time) processing of GBT Nod data
calibration.
Adopted from GBTIDL's getnod
Parameters:
scantab: scantable
scannos: a pair of scan numbers, or the first scan number of the pair
smooth: box car smoothing order
tsysval: optional user specified Tsys value
tauval: optional user specified tau value (not implemented yet)
tcalval: optional user specified Tcal value
verify: Verify calibration if true
"""
varlist = vars()
from asap._asap import stmath
from asap._asap import srctype
stm = stmath()
stm._setinsitu(False)
# check for the appropriate data
## s = scantab.get_scan('*_nod*')
## if s is None:
## msg = "The input data appear to contain no Nod observing mode data."
## raise TypeError(msg)
s = scantab.copy()
sel = selector()
sel.set_types( srctype.nod )
try:
s.set_selection( sel )
except Exception, e:
msg = "The input data appear to contain no Nod observing mode data."
raise TypeError(msg)
def _selected_stats(self,rows=None,regions=None):
# check for the validity of plotter and get the plotter
theplotter = self._get_plotter()
scan = theplotter._data
if not scan:
asaplog.post()
asaplog.push("Invalid scantable")
asaplog.post("ERROR")
mathobj = stmath( rcParams['insitu'] )
statval = {}
statstr = ['max', 'min', 'mean', 'median', 'sum', 'stddev', 'rms']
if isinstance(rows, list) and len(rows) > 0:
for irow in rows:
for stat in statstr:
statval[stat] = mathobj._statsrow(scan,[],stat,irow)[0]
self._print_stats(scan,irow,statval,statstr=statstr)
del irow
if isinstance(regions,dict) and len(regions) > 0:
for srow, masklist in regions.iteritems():
if not isinstance(masklist,list) or len(masklist) ==0:
msg = "Ignoring invalid region selection for row = "+srow
asaplog.post()
asaplog.push(msg)
asaplog.post("WARN")
continue
irow = int(srow)
mask = scan.create_mask(masklist,invert=False,row=irow)
for stat in statstr:
statval[stat] = mathobj._statsrow(scan,mask,stat,irow)[0]
self._print_stats(scan,irow,statval,statstr=statstr,
mask=masklist)
del irow, mask
del srow, masklist
del scan, statval, mathobj
def _array2dOp(scan,
value,
mode="ADD",
tsys=False,
insitu=None,
skip_flaggedrow=False):
"""
This function is workaround on the basic operation of scantable
with 2 dimensional float list.
scan: scantable operand
value: float list operand
mode: operation mode (ADD, SUB, MUL, DIV)
tsys: if True, operate tsys as well
insitu: if False, a new scantable is returned.
Otherwise, the array operation is done in-sitsu.
skip_flaggedrow: skip operation for row-flagged spectra.
"""
if insitu is None: insitu = rcParams['insitu']
nrow = scan.nrow()
s = None
from asap._asap import stmath
stm = stmath()
stm._setinsitu(insitu)
if len(value) == 1:
s = scantable(stm._arrayop(scan, value[0], mode, tsys,
skip_flaggedrow))
elif len(value) != nrow:
raise ValueError('len(value) must be 1 or conform to scan.nrow()')
else:
from asap._asap import stmath
if not insitu:
s = scan.copy()
else:
s = scan
# insitu must be True as we go row by row on the same data
stm._setinsitu(True)
basesel = s.get_selection()
# generate a new selector object based on basesel
sel = selector(basesel)
for irow in range(nrow):
sel.set_rows(irow)
s.set_selection(sel)
if len(value[irow]) == 1:
stm._unaryop(s, value[irow][0], mode, tsys, skip_flaggedrow)
else:
#stm._arrayop( s, value[irow], mode, tsys, 'channel' )
stm._arrayop(s, value[irow], mode, tsys, skip_flaggedrow)
s.set_selection(basesel)
return s
def dototalpower(calon, caloff, tcalval=0.0):
"""
Do calibration for CAL on,off signals.
Adopted from GBTIDL dototalpower
Parameters:
calon: the 'cal on' subintegration
caloff: the 'cal off' subintegration
tcalval: user supplied Tcal value
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._dototalpower(calon, caloff, tcalval))
s._add_history("dototalpower", varlist)
return s
def dototalpower(calon, caloff, tcalval=0.0):
"""
Do calibration for CAL on,off signals.
Adopted from GBTIDL dototalpower
Parameters:
calon: the 'cal on' subintegration
caloff: the 'cal off' subintegration
tcalval: user supplied Tcal value
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._dototalpower(calon, caloff, tcalval))
s._add_history("dototalpower",varlist)
return s
def dosigref(sig, ref, smooth, tsysval=0.0, tauval=0.0):
"""
Calculate a quotient (sig-ref/ref * Tsys)
Adopted from GBTIDL dosigref
Parameters:
sig: on source data
ref: reference data
smooth: width of box car smoothing for reference
tsysval: user specified Tsys (scalar only)
tauval: user specified Tau (required if tsysval is set)
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._dosigref(sig, ref, smooth, tsysval, tauval))
s._add_history("dosigref", varlist)
return s
def dosigref(sig, ref, smooth, tsysval=0.0, tauval=0.0):
"""
Calculate a quotient (sig-ref/ref * Tsys)
Adopted from GBTIDL dosigref
Parameters:
sig: on source data
ref: reference data
smooth: width of box car smoothing for reference
tsysval: user specified Tsys (scalar only)
tauval: user specified Tau (required if tsysval is set)
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._dosigref(sig, ref, smooth, tsysval, tauval))
s._add_history("dosigref",varlist)
return s
def _array2dOp( scan, value, mode="ADD", tsys=False, insitu=None, skip_flaggedrow=False):
"""
This function is workaround on the basic operation of scantable
with 2 dimensional float list.
scan: scantable operand
value: float list operand
mode: operation mode (ADD, SUB, MUL, DIV)
tsys: if True, operate tsys as well
insitu: if False, a new scantable is returned.
Otherwise, the array operation is done in-sitsu.
skip_flaggedrow: skip operation for row-flagged spectra.
"""
if insitu is None: insitu = rcParams['insitu']
nrow = scan.nrow()
s = None
from asap._asap import stmath
stm = stmath()
stm._setinsitu(insitu)
if len( value ) == 1:
s = scantable( stm._arrayop( scan, value[0], mode, tsys, skip_flaggedrow ) )
elif len( value ) != nrow:
raise ValueError( 'len(value) must be 1 or conform to scan.nrow()' )
else:
from asap._asap import stmath
if not insitu:
s = scan.copy()
else:
s = scan
# insitu must be True as we go row by row on the same data
stm._setinsitu( True )
basesel = s.get_selection()
# generate a new selector object based on basesel
sel = selector(basesel)
for irow in range( nrow ):
sel.set_rows( irow )
s.set_selection( sel )
if len( value[irow] ) == 1:
stm._unaryop( s, value[irow][0], mode, tsys, skip_flaggedrow )
else:
#stm._arrayop( s, value[irow], mode, tsys, 'channel' )
stm._arrayop( s, value[irow], mode, tsys, skip_flaggedrow )
s.set_selection(basesel)
return s
def merge(*args, **kwargs):
"""
Merge a list of scanatables, or comma-sperated scantables into one
scnatble.
Parameters:
A list [scan1, scan2] or scan1, scan2.
freq_tol: frequency tolerance for merging IFs. numeric values
in units of Hz (1.0e6 -> 1MHz) and string ('1MHz')
is allowed.
Example:
myscans = [scan1, scan2]
allscans = merge(myscans)
# or equivalent
sameallscans = merge(scan1, scan2)
# with freqtol
allscans = merge(scan1, scan2, freq_tol=1.0e6)
# or equivalently
allscans = merge(scan1, scan2, freq_tol='1MHz')
"""
varlist = vars()
if isinstance(args[0], list):
lst = tuple(args[0])
elif isinstance(args[0], tuple):
lst = args[0]
else:
lst = tuple(args)
if kwargs.has_key('freq_tol'):
freq_tol = str(kwargs['freq_tol'])
if len(freq_tol) > 0 and re.match('.+[GMk]Hz$', freq_tol) is None:
freq_tol += 'Hz'
else:
freq_tol = ''
varlist["args"] = "%d scantables" % len(lst)
# need special formatting her for history...
from asap._asap import stmath
stm = stmath()
for s in lst:
if not isinstance(s, scantable):
msg = "Please give a list of scantables"
raise TypeError(msg)
s = scantable(stm._merge(lst, freq_tol))
s._add_history("merge", varlist)
return s
def merge(*args, **kwargs):
"""
Merge a list of scanatables, or comma-sperated scantables into one
scnatble.
Parameters:
A list [scan1, scan2] or scan1, scan2.
freq_tol: frequency tolerance for merging IFs. numeric values
in units of Hz (1.0e6 -> 1MHz) and string ('1MHz')
is allowed.
Example:
myscans = [scan1, scan2]
allscans = merge(myscans)
# or equivalent
sameallscans = merge(scan1, scan2)
# with freqtol
allscans = merge(scan1, scan2, freq_tol=1.0e6)
# or equivalently
allscans = merge(scan1, scan2, freq_tol='1MHz')
"""
varlist = vars()
if isinstance(args[0],list):
lst = tuple(args[0])
elif isinstance(args[0],tuple):
lst = args[0]
else:
lst = tuple(args)
if kwargs.has_key('freq_tol'):
freq_tol = str(kwargs['freq_tol'])
if len(freq_tol) > 0 and re.match('.+[GMk]Hz$', freq_tol) is None:
freq_tol += 'Hz'
else:
freq_tol = ''
varlist["args"] = "%d scantables" % len(lst)
# need special formatting her for history...
from asap._asap import stmath
stm = stmath()
for s in lst:
if not isinstance(s,scantable):
msg = "Please give a list of scantables"
raise TypeError(msg)
s = scantable(stm._merge(lst, freq_tol))
s._add_history("merge", varlist)
return s
def quotient(source, reference, preserve=True):
"""
Return the quotient of a 'source' (signal) scan and a 'reference' scan.
The reference can have just one scan, even if the signal has many. Otherwise
they must have the same number of scans.
The cursor of the output scan is set to 0
Parameters:
source: the 'on' scan
reference: the 'off' scan
preserve: you can preserve (default) the continuum or
remove it. The equations used are
preserve: Output = Toff * (on/off) - Toff
remove: Output = Toff * (on/off) - Ton
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._quotient(source, reference, preserve))
s._add_history("quotient", varlist)
return s
def quotient(source, reference, preserve=True):
"""
Return the quotient of a 'source' (signal) scan and a 'reference' scan.
The reference can have just one scan, even if the signal has many. Otherwise
they must have the same number of scans.
The cursor of the output scan is set to 0
Parameters:
source: the 'on' scan
reference: the 'off' scan
preserve: you can preserve (default) the continuum or
remove it. The equations used are
preserve: Output = Toff * (on/off) - Toff
remove: Output = Toff * (on/off) - Ton
"""
varlist = vars()
from asap._asap import stmath
stm = stmath()
stm._setinsitu(False)
s = scantable(stm._quotient(source, reference, preserve))
s._add_history("quotient",varlist)
return s
def almacal(scantab, scannos=[], calmode='none', verify=False):
"""
Calibrate ALMA data
Parameters:
scantab: scantable
scannos: list of scan number
calmode: calibration mode
verify: verify calibration
"""
from asap._asap import stmath
stm = stmath()
selection = selector()
selection.set_scans(scannos)
orig = scantab.get_selection()
scantab.set_selection(orig + selection)
## ssub = scantab.get_scan( scannos )
## scal = scantable( stm.almacal( ssub, calmode ) )
scal = scantable(stm.almacal(scantab, calmode))
scantab.set_selection(orig)
return scal
def calnod(scantab,
scannos=[],
smooth=1,
tsysval=0.0,
tauval=0.0,
tcalval=0.0,
verify=False):
"""
Do full (but a pair of scans at time) processing of GBT Nod data
calibration.
Adopted from GBTIDL's getnod
Parameters:
scantab: scantable
scannos: a pair of scan numbers, or the first scan number of the pair
smooth: box car smoothing order
tsysval: optional user specified Tsys value
tauval: optional user specified tau value (not implemented yet)
tcalval: optional user specified Tcal value
verify: Verify calibration if true
"""
varlist = vars()
from asap._asap import stmath
from asap._asap import srctype
stm = stmath()
stm._setinsitu(False)
# check for the appropriate data
## s = scantab.get_scan('*_nod*')
## if s is None:
## msg = "The input data appear to contain no Nod observing mode data."
## raise TypeError(msg)
s = scantab.copy()
sel = selector()
sel.set_types(srctype.nod)
try:
s.set_selection(sel)
except Exception, e:
msg = "The input data appear to contain no Nod observing mode data."
raise TypeError(msg)
def almacal( scantab, scannos=[], calmode='none', verify=False ):
"""
Calibrate ALMA data
Parameters:
scantab: scantable
scannos: list of scan number
calmode: calibration mode
verify: verify calibration
"""
from asap._asap import stmath
stm = stmath()
selection=selector()
selection.set_scans(scannos)
orig = scantab.get_selection()
scantab.set_selection(orig+selection)
## ssub = scantab.get_scan( scannos )
## scal = scantable( stm.almacal( ssub, calmode ) )
scal = scantable( stm.almacal( scantab, calmode ) )
scantab.set_selection(orig)
return scal
def calfs(scantab,
scannos=[],
smooth=1,
tsysval=0.0,
tauval=0.0,
tcalval=0.0,
verify=False):
"""
Calibrate GBT frequency switched data.
Adopted from GBTIDL getfs.
Currently calfs identify the scans as frequency switched data if source
type enum is fson and fsoff. The data must contains 'CAL' signal
on/off in each integration. To identify 'CAL' on state, the source type
enum of foncal and foffcal need to be present.
Parameters:
scantab: scantable
scannos: list of scan numbers
smooth: optional box smoothing order for the reference
(default is 1 = no smoothing)
tsysval: optional user specified Tsys (default is 0.0,
use Tsys in the data)
tauval: optional user specified Tau
verify: Verify calibration if true
"""
varlist = vars()
from asap._asap import stmath
from asap._asap import srctype
stm = stmath()
stm._setinsitu(False)
# check = scantab.get_scan('*_fs*')
# if check is None:
# msg = "The input data appear to contain no Nod observing mode data."
# raise TypeError(msg)
s = scantab.get_scan(scannos)
del scantab
resspec = scantable(stm._dofs(s, scannos, smooth, tsysval, tauval,
tcalval))
###
if verify:
# get data
ssub = s.get_scan(scannos)
#ssubon = ssub.get_scan('*calon')
#ssuboff = ssub.get_scan('*[^calon]')
sel = selector()
sel.set_types([srctype.foncal, srctype.foffcal])
ssub.set_selection(sel)
ssubon = ssub.copy()
ssub.set_selection()
sel.reset()
sel.set_types([srctype.fson, srctype.fsoff])
ssub.set_selection(sel)
ssuboff = ssub.copy()
ssub.set_selection()
sel.reset()
import numpy
precal = {}
postcal = []
keys = ['fs', 'fs_calon', 'fsr', 'fsr_calon']
types = [srctype.fson, srctype.foncal, srctype.fsoff, srctype.foffcal]
ifnos = list(ssub.getifnos())
polnos = list(ssub.getpolnos())
for i in range(2):
#ss=ssuboff.get_scan('*'+keys[2*i])
ll = []
for j in range(len(ifnos)):
for k in range(len(polnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
sel.set_types(types[2 * i])
try:
#ss.set_selection(sel)
ssuboff.set_selection(sel)
except:
continue
ll.append(numpy.array(ss._getspectrum(0)))
sel.reset()
#ss.set_selection()
ssuboff.set_selection()
precal[keys[2 * i]] = ll
#del ss
#ss=ssubon.get_scan('*'+keys[2*i+1])
ll = []
for j in range(len(ifnos)):
for k in range(len(polnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
sel.set_types(types[2 * i + 1])
try:
#ss.set_selection(sel)
ssubon.set_selection(sel)
except:
continue
ll.append(numpy.array(ss._getspectrum(0)))
sel.reset()
#ss.set_selection()
ssubon.set_selection()
precal[keys[2 * i + 1]] = ll
#del ss
#sig=resspec.get_scan('*_fs')
#ref=resspec.get_scan('*_fsr')
sel.set_types(srctype.fson)
resspec.set_selection(sel)
sig = resspec.copy()
resspec.set_selection()
sel.reset()
sel.set_type(srctype.fsoff)
resspec.set_selection(sel)
ref = resspec.copy()
resspec.set_selection()
sel.reset()
for k in range(len(polnos)):
for j in range(len(ifnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
try:
sig.set_selection(sel)
postcal.append(numpy.array(sig._getspectrum(0)))
except:
ref.set_selection(sel)
postcal.append(numpy.array(ref._getspectrum(0)))
sel.reset()
resspec.set_selection()
del sel
# plot
asaplog.post()
asaplog.push(
'Plot only first spectrum for each [if,pol] pairs to verify calibration.'
)
asaplog.post('WARN')
p = new_asaplot()
rcp('lines', linewidth=1)
#nr=min(6,len(ifnos)*len(polnos))
nr = len(ifnos) / 2 * len(polnos)
titles = []
btics = []
if nr > 3:
asaplog.post()
asaplog.push('Only first 3 [if,pol] pairs are plotted.')
asaplog.post('WARN')
nr = 3
p.set_panels(rows=nr, cols=3, nplots=3 * nr, ganged=False)
for i in range(3 * nr):
b = False
if i >= 3 * nr - 3:
b = True
btics.append(b)
for i in range(nr):
p.subplot(3 * i)
p.color = 0
title = 'raw data IF%s,%s POL%s' % (
ifnos[2 * int(i / len(polnos))],
ifnos[2 * int(i / len(polnos)) + 1], polnos[i % len(polnos)])
titles.append(title)
#p.set_axes('title',title,fontsize=40)
ymin = 1.0e100
ymax = -1.0e100
nchan = s.nchan(ifnos[2 * int(i / len(polnos))])
edge = int(nchan * 0.01)
for j in range(4):
spmin = min(precal[keys[j]][i][edge:nchan - edge])
spmax = max(precal[keys[j]][i][edge:nchan - edge])
ymin = min(ymin, spmin)
ymax = max(ymax, spmax)
for j in range(4):
if i == 0:
p.set_line(label=keys[j])
else:
p.legend()
p.plot(precal[keys[j]][i])
p.axes.set_ylim(ymin - 0.1 * abs(ymin), ymax + 0.1 * abs(ymax))
if not btics[3 * i]:
p.axes.set_xticks([])
p.subplot(3 * i + 1)
p.color = 0
title = 'sig data IF%s POL%s' % (ifnos[2 * int(i / len(polnos))],
polnos[i % len(polnos)])
titles.append(title)
#p.set_axes('title',title)
p.legend()
ymin = postcal[2 * i][edge:nchan - edge].min()
ymax = postcal[2 * i][edge:nchan - edge].max()
p.plot(postcal[2 * i])
p.axes.set_ylim(ymin - 0.1 * abs(ymin), ymax + 0.1 * abs(ymax))
if not btics[3 * i + 1]:
p.axes.set_xticks([])
p.subplot(3 * i + 2)
p.color = 0
title = 'ref data IF%s POL%s' % (ifnos[2 * int(i / len(polnos)) +
1], polnos[i % len(polnos)])
titles.append(title)
#p.set_axes('title',title)
p.legend()
ymin = postcal[2 * i + 1][edge:nchan - edge].min()
ymax = postcal[2 * i + 1][edge:nchan - edge].max()
p.plot(postcal[2 * i + 1])
p.axes.set_ylim(ymin - 0.1 * abs(ymin), ymax + 0.1 * abs(ymax))
if not btics[3 * i + 2]:
p.axes.set_xticks([])
for i in range(3 * nr):
p.subplot(i)
p.set_axes('title', titles[i], fontsize='medium')
x = raw_input('Accept calibration ([y]/n): ')
if x.upper() == 'N':
p.quit()
del p
return scantab
p.quit()
del p
###
resspec._add_history("calfs", varlist)
return resspec
def calfs(scantab, scannos=[], smooth=1, tsysval=0.0, tauval=0.0, tcalval=0.0, verify=False):
"""
Calibrate GBT frequency switched data.
Adopted from GBTIDL getfs.
Currently calfs identify the scans as frequency switched data if source
type enum is fson and fsoff. The data must contains 'CAL' signal
on/off in each integration. To identify 'CAL' on state, the source type
enum of foncal and foffcal need to be present.
Parameters:
scantab: scantable
scannos: list of scan numbers
smooth: optional box smoothing order for the reference
(default is 1 = no smoothing)
tsysval: optional user specified Tsys (default is 0.0,
use Tsys in the data)
tauval: optional user specified Tau
verify: Verify calibration if true
"""
varlist = vars()
from asap._asap import stmath
from asap._asap import srctype
stm = stmath()
stm._setinsitu(False)
# check = scantab.get_scan('*_fs*')
# if check is None:
# msg = "The input data appear to contain no Nod observing mode data."
# raise TypeError(msg)
s = scantab.get_scan(scannos)
del scantab
resspec = scantable(stm._dofs(s, scannos, smooth, tsysval,tauval,tcalval))
###
if verify:
# get data
ssub = s.get_scan(scannos)
#ssubon = ssub.get_scan('*calon')
#ssuboff = ssub.get_scan('*[^calon]')
sel = selector()
sel.set_types( [srctype.foncal,srctype.foffcal] )
ssub.set_selection( sel )
ssubon = ssub.copy()
ssub.set_selection()
sel.reset()
sel.set_types( [srctype.fson,srctype.fsoff] )
ssub.set_selection( sel )
ssuboff = ssub.copy()
ssub.set_selection()
sel.reset()
import numpy
precal={}
postcal=[]
keys=['fs','fs_calon','fsr','fsr_calon']
types=[srctype.fson,srctype.foncal,srctype.fsoff,srctype.foffcal]
ifnos=list(ssub.getifnos())
polnos=list(ssub.getpolnos())
for i in range(2):
#ss=ssuboff.get_scan('*'+keys[2*i])
ll=[]
for j in range(len(ifnos)):
for k in range(len(polnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
sel.set_types(types[2*i])
try:
#ss.set_selection(sel)
ssuboff.set_selection(sel)
except:
continue
ll.append(numpy.array(ss._getspectrum(0)))
sel.reset()
#ss.set_selection()
ssuboff.set_selection()
precal[keys[2*i]]=ll
#del ss
#ss=ssubon.get_scan('*'+keys[2*i+1])
ll=[]
for j in range(len(ifnos)):
for k in range(len(polnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
sel.set_types(types[2*i+1])
try:
#ss.set_selection(sel)
ssubon.set_selection(sel)
except:
continue
ll.append(numpy.array(ss._getspectrum(0)))
sel.reset()
#ss.set_selection()
ssubon.set_selection()
precal[keys[2*i+1]]=ll
#del ss
#sig=resspec.get_scan('*_fs')
#ref=resspec.get_scan('*_fsr')
sel.set_types( srctype.fson )
resspec.set_selection( sel )
sig=resspec.copy()
resspec.set_selection()
sel.reset()
sel.set_type( srctype.fsoff )
resspec.set_selection( sel )
ref=resspec.copy()
resspec.set_selection()
sel.reset()
for k in range(len(polnos)):
for j in range(len(ifnos)):
sel.set_ifs(ifnos[j])
sel.set_polarizations(polnos[k])
try:
sig.set_selection(sel)
postcal.append(numpy.array(sig._getspectrum(0)))
except:
ref.set_selection(sel)
postcal.append(numpy.array(ref._getspectrum(0)))
sel.reset()
resspec.set_selection()
del sel
# plot
asaplog.post()
asaplog.push('Plot only first spectrum for each [if,pol] pairs to verify calibration.')
asaplog.post('WARN')
p=new_asaplot()
rcp('lines', linewidth=1)
#nr=min(6,len(ifnos)*len(polnos))
nr=len(ifnos)/2*len(polnos)
titles=[]
btics=[]
if nr>3:
asaplog.post()
asaplog.push('Only first 3 [if,pol] pairs are plotted.')
asaplog.post('WARN')
nr=3
p.set_panels(rows=nr,cols=3,nplots=3*nr,ganged=False)
for i in range(3*nr):
b=False
if i >= 3*nr-3:
b=True
btics.append(b)
for i in range(nr):
p.subplot(3*i)
p.color=0
title='raw data IF%s,%s POL%s' % (ifnos[2*int(i/len(polnos))],ifnos[2*int(i/len(polnos))+1],polnos[i%len(polnos)])
titles.append(title)
#p.set_axes('title',title,fontsize=40)
ymin=1.0e100
ymax=-1.0e100
nchan=s.nchan(ifnos[2*int(i/len(polnos))])
edge=int(nchan*0.01)
for j in range(4):
spmin=min(precal[keys[j]][i][edge:nchan-edge])
spmax=max(precal[keys[j]][i][edge:nchan-edge])
ymin=min(ymin,spmin)
ymax=max(ymax,spmax)
for j in range(4):
if i==0:
p.set_line(label=keys[j])
else:
p.legend()
p.plot(precal[keys[j]][i])
p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
if not btics[3*i]:
p.axes.set_xticks([])
p.subplot(3*i+1)
p.color=0
title='sig data IF%s POL%s' % (ifnos[2*int(i/len(polnos))],polnos[i%len(polnos)])
titles.append(title)
#p.set_axes('title',title)
p.legend()
ymin=postcal[2*i][edge:nchan-edge].min()
ymax=postcal[2*i][edge:nchan-edge].max()
p.plot(postcal[2*i])
p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
if not btics[3*i+1]:
p.axes.set_xticks([])
p.subplot(3*i+2)
p.color=0
title='ref data IF%s POL%s' % (ifnos[2*int(i/len(polnos))+1],polnos[i%len(polnos)])
titles.append(title)
#p.set_axes('title',title)
p.legend()
ymin=postcal[2*i+1][edge:nchan-edge].min()
ymax=postcal[2*i+1][edge:nchan-edge].max()
p.plot(postcal[2*i+1])
p.axes.set_ylim(ymin-0.1*abs(ymin),ymax+0.1*abs(ymax))
if not btics[3*i+2]:
p.axes.set_xticks([])
for i in range(3*nr):
p.subplot(i)
p.set_axes('title',titles[i],fontsize='medium')
x=raw_input('Accept calibration ([y]/n): ' )
if x.upper() == 'N':
p.quit()
del p
return scantab
p.quit()
del p
###
resspec._add_history("calfs",varlist)
return resspec
def average_time(*args, **kwargs):
"""
Return the (time) average of a scan or list of scans. [in channels only]
The cursor of the output scan is set to 0
Parameters:
one scan or comma separated scans or a list of scans
mask: an optional mask (only used for 'var' and 'tsys' weighting)
scanav: True averages each scan separately.
False (default) averages all scans together,
weight: Weighting scheme.
'none' (mean no weight)
'var' (1/var(spec) weighted)
'tsys' (1/Tsys**2 weighted)
'tint' (integration time weighted)
'tintsys' (Tint/Tsys**2)
'median' ( median averaging)
align: align the spectra in velocity before averaging. It takes
the time of the first spectrum in the first scantable
as reference time.
compel: True forces to average overwrapped IFs.
Example:
# return a time averaged scan from scana and scanb
# without using a mask
scanav = average_time(scana,scanb)
# or equivalent
# scanav = average_time([scana, scanb])
# return the (time) averaged scan, i.e. the average of
# all correlator cycles
scanav = average_time(scan, scanav=True)
"""
scanav = False
if kwargs.has_key('scanav'):
scanav = kwargs.get('scanav')
weight = 'tint'
if kwargs.has_key('weight'):
weight = kwargs.get('weight')
mask = ()
if kwargs.has_key('mask'):
mask = kwargs.get('mask')
align = False
if kwargs.has_key('align'):
align = kwargs.get('align')
compel = False
if kwargs.has_key('compel'):
compel = kwargs.get('compel')
varlist = vars()
if isinstance(args[0],list):
lst = args[0]
elif isinstance(args[0],tuple):
lst = list(args[0])
else:
lst = list(args)
del varlist["kwargs"]
varlist["args"] = "%d scantables" % len(lst)
# need special formatting here for history...
from asap._asap import stmath
stm = stmath()
for s in lst:
if not isinstance(s,scantable):
msg = "Please give a list of scantables"
raise TypeError(msg)
if scanav: scanav = "SCAN"
else: scanav = "NONE"
alignedlst = []
if align:
refepoch = lst[0].get_time(0)
for scan in lst:
alignedlst.append(scan.freq_align(refepoch,insitu=False))
else:
alignedlst = lst
if weight.upper() == 'MEDIAN':
# median doesn't support list of scantables - merge first
merged = None
if len(alignedlst) > 1:
merged = merge(alignedlst)
else:
merged = alignedlst[0]
s = scantable(stm._averagechannel(merged, 'MEDIAN', scanav))
del merged
else:
#s = scantable(stm._average(alignedlst, mask, weight.upper(), scanav))
s = scantable(stm._new_average(alignedlst, compel, mask,
weight.upper(), scanav))
s._add_history("average_time",varlist)
return s
def average_time(*args, **kwargs):
"""
Return the (time) average of a scan or list of scans. [in channels only]
The cursor of the output scan is set to 0
Parameters:
one scan or comma separated scans or a list of scans
mask: an optional mask (only used for 'var' and 'tsys' weighting)
scanav: True averages each scan separately.
False (default) averages all scans together,
weight: Weighting scheme.
'none' (mean no weight)
'var' (1/var(spec) weighted)
'tsys' (1/Tsys**2 weighted)
'tint' (integration time weighted)
'tintsys' (Tint/Tsys**2)
'median' ( median averaging)
align: align the spectra in velocity before averaging. It takes
the time of the first spectrum in the first scantable
as reference time.
compel: True forces to average overwrapped IFs.
Example:
# return a time averaged scan from scana and scanb
# without using a mask
scanav = average_time(scana,scanb)
# or equivalent
# scanav = average_time([scana, scanb])
# return the (time) averaged scan, i.e. the average of
# all correlator cycles
scanav = average_time(scan, scanav=True)
"""
scanav = False
if kwargs.has_key('scanav'):
scanav = kwargs.get('scanav')
weight = 'tint'
if kwargs.has_key('weight'):
weight = kwargs.get('weight')
mask = ()
if kwargs.has_key('mask'):
mask = kwargs.get('mask')
align = False
if kwargs.has_key('align'):
align = kwargs.get('align')
compel = False
if kwargs.has_key('compel'):
compel = kwargs.get('compel')
varlist = vars()
if isinstance(args[0], list):
lst = args[0]
elif isinstance(args[0], tuple):
lst = list(args[0])
else:
lst = list(args)
del varlist["kwargs"]
varlist["args"] = "%d scantables" % len(lst)
# need special formatting here for history...
from asap._asap import stmath
stm = stmath()
for s in lst:
if not isinstance(s, scantable):
msg = "Please give a list of scantables"
raise TypeError(msg)
if scanav: scanav = "SCAN"
else: scanav = "NONE"
alignedlst = []
if align:
refepoch = lst[0].get_time(0)
for scan in lst:
alignedlst.append(scan.freq_align(refepoch, insitu=False))
else:
alignedlst = lst
if weight.upper() == 'MEDIAN':
# median doesn't support list of scantables - merge first
merged = None
if len(alignedlst) > 1:
merged = merge(alignedlst)
else:
merged = alignedlst[0]
s = scantable(stm._averagechannel(merged, 'MEDIAN', scanav))
del merged
else:
#s = scantable(stm._average(alignedlst, mask, weight.upper(), scanav))
s = scantable(
stm._new_average(alignedlst, compel, mask, weight.upper(), scanav))
s._add_history("average_time", varlist)
return s
