def run( fetch=False ):
"""Run the tasks and compare the results."""
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
nsigma = 3.0
advice = None
lazy = True
equinoxes = ['B1950_VLA', 'B1950', 'J2000']
# 'BMEAN', 'BTRUE', 'APP', 'JMEAN', 'JTRUE']
convert_phasecenter = False
origvisses = data()
passes = True
for origvis in origvisses:
peaks = run_tasks(origvis, advice, lazy, equinoxes, convert_phasecenter)
passes = passes and compare(peaks, nsigma)
if passes:
print ''
print 'Regression PASSED'
print ''
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
im.open('ic2233_1.ms')
npix = 1024
im.selectvis(spw='0', nchan=[6], start=[0], step=[1])
im.defineimage(nx=npix,
ny=npix,
cellx='3.0arcsec',
celly='3.0arcsec',
stokes="IV",
spw=[0])
im.weight(type='briggs', robust=0.7)
im.setoptions(imagetilevol=-1000000)
im.setvp(dovp=bool(1),
usedefaultvp=bool(1),
dosquint=bool(1),
parangleinc='5.0deg')
im.make('squint_corr')
im.clean(algorithm='mfhogbom',
niter=1000,
model=['squint_corr'],
residual=['squint_corr.residual'],
image=['squint_corr.restored'],
threshold='0Jy')
im.done()
return ['squint_corr.restored']
def run(fetch=False):
"""Run the tasks and compare the results."""
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
nsigma = 3.0
advice = None
lazy = True
equinoxes = ['B1950_VLA', 'B1950', 'J2000']
# 'BMEAN', 'BTRUE', 'APP', 'JMEAN', 'JTRUE']
convert_phasecenter = False
origvisses = data()
passes = True
for origvis in origvisses:
peaks = run_tasks(origvis, advice, lazy, equinoxes,
convert_phasecenter)
passes = passes and compare(peaks, nsigma)
if passes:
print ''
print 'Regression PASSED'
print ''
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
im=gl['casac'].imager()
me=gl['me']
time1=time.time()
im.open('coma.ms')
im.selectvis(spw=0, field=0);
mydir=me.direction('J2000', '12h30m48', '12d24m0')
im.defineimage(nx=200, ny=200, cellx='30arcsec', celly='30arcsec', phasecenter=mydir);
im.make('outlier1re');
im.defineimage(nx=2000, ny=2000, cellx='30arcsec',celly='30arcsec',phasecenter=0, facets=1);
im.setoptions(ftmachine='wproject',wprojplanes=400, padding=1.2)
im.make('bla1re')
im.clean(algorithm='wfclark',model=['bla1re', 'outlier1re'], image=['bla1re.restored', 'outlier1re.restored'], niter=10000);
im.done()
time2=time.time();
print 'Time taken for wproject= ', (time2-time1)/60,'mins'
#resturn the images that will be templated and compared in future runs
return ['bla1re.restored', 'outlier1re.restored']
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
#####locate the regression script
lepath = locatescript('accum_regression.py')
gl['regstate'] = True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
#####locate the regression script
try:
lepath = locatescript('alma-m100-analysis-regression.py')
print 'Script used is ', lepath
execfile(lepath, gl, pass_on)
except:
print 'execution failed: ', sys.exc_info()
raise
###return the images that will be templated and compared in future runs
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
#####locate the regression script
lepath = locatescript('wideband_regression.py')
print 'Script used is ', lepath
gl['regstate'] = True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
### return the images that will be templated and compared in future runs
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
#####locate the regression script
lepath=locatescript('wideband_regression.py')
print 'Script used is ',lepath
gl['regstate']=True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
### return the images that will be templated and compared in future runs
return []; #'reg_3C286.image.tt0','reg_3C286.image.alpha','reg_3C286.image.beta']
def run( fetch=False ):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
#####locate the regression script
try:
lepath=locatescript('alma-m100-analysis-hpc-regression.py')
print 'Script used is ',lepath
execfile(lepath, gl, pass_on)
except:
print 'execution failed: ', sys.exc_info()
raise
###return the images that will be templated and compared in future runs
return []
def run(fetch=False):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
#####locate the regression script
lepath=locatescript('ngc4826_tutorial_regression.py')
print 'Script used is ',lepath
gl['regstate']=True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
###return the images that will be templated and compared in future runs
return ['ngc4826.tutorial.16apr98.src.clean.image', 'ngc4826.tutorial.16apr98.moments.mom1']
def run( fetch=False ):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
#####locate the regression script
lepath=locatescript('3c129_tutorial_regression.py')
print 'Script used is ',lepath
gl['regstate']=True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
###return the images that will be templated and compared in future runs
# return ['3C129BC.clean.image', 'at166B.3c129.image', 'at166C.3c129.image']
return ['3C129BC.core.I']
def run(fetch=False) :
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
im.open('ic2233_1.ms')
npix=1024
im.selectvis(spw='0', nchan=[6], start=[0], step=[1])
im.defineimage(nx=npix, ny=npix, cellx='3.0arcsec', celly='3.0arcsec', stokes="IV", spw=[0])
im.weight(type='briggs', robust=0.7)
im.setoptions(imagetilevol=-1000000);
im.setvp(dovp=bool(1), usedefaultvp=bool(1), dosquint=bool(1),
parangleinc='5.0deg')
im.make('squint_corr')
im.clean(algorithm='mfhogbom', niter=1000, model=['squint_corr'], residual=['squint_corr.residual'], image=['squint_corr.restored'], threshold='0Jy')
im.done()
return ['squint_corr.restored']
def run(fetch=False):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
#####locate the regression script
lepath=locatescript('ngc2403_tutorial_regression.py')
print 'Script used is ',lepath
gl['regstate']=True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
###return the images that will be templated and compared in future runs
#not in repository: return ['n2403.tutorial.final.clean.image','n2403.tutorial.dirty.image']
return ['n2403.tutorial.final.clean.image']
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
#####locate the regression script
lepath = locatescript('ngc2403_tutorial_regression.py')
print 'Script used is ', lepath
gl['regstate'] = True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
###return the images that will be templated and compared in future runs
#not in repository: return ['n2403.tutorial.final.clean.image','n2403.tutorial.dirty.image']
return ['n2403.tutorial.final.clean.image']
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
#####locate the regression script
lepath = locatescript('ngc4826_tutorial_regression.py')
print 'Script used is ', lepath
gl['regstate'] = True
execfile(lepath, gl)
print 'regstate =', gl['regstate']
if not gl['regstate']:
raise Exception, 'regstate = False'
###return the images that will be templated and compared in future runs
return [
'ngc4826.tutorial.16apr98.src.clean.image',
'ngc4826.tutorial.16apr98.moments.mom1'
]
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
im = gl['casac'].imager()
me = gl['me']
time1 = time.time()
im.open('coma.ms')
im.selectvis(spw=0, field=0)
mydir = me.direction('J2000', '12h30m48', '12d24m0')
im.defineimage(nx=200,
ny=200,
cellx='30arcsec',
celly='30arcsec',
phasecenter=mydir)
im.make('outlier1re')
im.defineimage(nx=2000,
ny=2000,
cellx='30arcsec',
celly='30arcsec',
phasecenter=0,
facets=1)
im.setoptions(ftmachine='wproject', wprojplanes=400, padding=1.2)
im.make('bla1re')
im.clean(algorithm='wfclark',
model=['bla1re', 'outlier1re'],
image=['bla1re.restored', 'outlier1re.restored'],
niter=10000)
im.done()
time2 = time.time()
print 'Time taken for wproject= ', (time2 - time1) / 60, 'mins'
#resturn the images that will be templated and compared in future runs
return ['bla1re.restored', 'outlier1re.restored']
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
input_mses = data()
badcells = {}
for input_ms in input_mses:
tbin = str(expected[input_ms]['tav']) + 's'
cbin = expected[input_ms]['cav']
spwsels = expected[input_ms].keys()
spwsels.remove('tav')
spwsels.remove('cav')
spwsels.remove('dep')
spwsels.sort() # Prob. not needed.
for spwsel in spwsels:
tavms = re.sub(r'.ms$', '',
input_ms) + '_%s_timebin%s.ms' % (spwsel, tbin)
#print "run: tavms =", tavms
outputms = re.sub(r'.ms$', '', tavms) + '_width%d.ms' % cbin
#print "run: outputms =", outputms
for oms in (tavms, outputms): # Get rid of past problems.
if os.path.isdir(oms):
shutil.rmtree(oms)
# The actual run.
try:
(nrows_aft_tavg, nrows_aft_cavg,
shp_aft_cavg) = time_then_chan_avg(input_ms,
tbin,
cbin,
outms=outputms,
chanselstr=spwsel,
zaptemp=False)
except Exception, e:
raise Exception, "Error (%s) running time_then_chan_avg()." % e
###### Test # of rows.
if nrows_aft_tavg != expected[input_ms][spwsel]['nrows_aft_tavg']:
raise Exception, """
The number of rows after time averaging (%d) does not match
the expected number (%d) for input MS %s with selection string "%s"
""" % (nrows_aft_tavg,
expected[input_ms][spwsel]['nrows_aft_tavg'], input_ms,
spwsel)
if nrows_aft_cavg != expected[input_ms][spwsel]['nrows_aft_cavg']:
raise Exception, """
The number of rows after channel averaging (%d) does not match
the expected number (%d) for input MS %s with selection string "%s"
""" % (nrows_aft_cavg,
expected[input_ms][spwsel]['nrows_aft_cavg'], input_ms,
spwsel)
if shp_aft_cavg != expected[input_ms][spwsel]['datshp']:
raise Exception, """
The data shape (%s) after channel averaging does not match
the expectation (%s) for input MS %s with selection string "%s"
""" % (shp_aft_cavg, expected[input_ms][spwsel]['datshp'],
input_ms, spwsel)
###### Compare averaged data with expectation, and rm the averaged
###### data if it passes.
outputmses = {'tav': tavms, 'cav': outputms}
for avtype, avms in outputmses.iteritems():
expcells = expected[input_ms][spwsel]['cells'][avtype]
try:
tb.open(avms)
except Exception, e:
raise Exception, "Error (%s) opening %s." % (e, avms)
try:
for ((col, row), expent) in expcells.iteritems():
usetol = True
if isinstance(expent, tuple):
tol = expent[1]
expent = expent[0]
elif not isquantized(expent):
tol = 0.005 * expent
else:
usetol = False
gotval = tb.getcell(col, row)
if hasattr(gotval, 'flatten') and gotval.size == 1:
gotval = gotval.flatten()[0]
if usetol:
closeenough = (abs(gotval - expent) < tol)
else:
closeenough = (gotval == expent)
if hasattr(closeenough, 'all'):
closeenough = closeenough.all()
if not closeenough:
if not badcells.has_key(avms):
badcells[avms] = {}
badcells[avms][(col, row)] = (gotval, expent)
except Exception, e:
raise Exception, "Error (%s) checking %s's cell %s." % (
e, avms, (col, row))
finally:
def run(fetch=False):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
input_mses = data()
badcells = {}
for input_ms in input_mses:
tbin = str(expected[input_ms]['tav']) + 's'
cbin = expected[input_ms]['cav']
spwsels = expected[input_ms].keys()
spwsels.remove('tav')
spwsels.remove('cav')
spwsels.remove('dep')
spwsels.sort() # Prob. not needed.
for spwsel in spwsels:
tavms = re.sub(r'.ms$', '', input_ms) + '_%s_timebin%s.ms' % (spwsel, tbin)
#print "run: tavms =", tavms
outputms = re.sub(r'.ms$', '', tavms) + '_width%d.ms' % cbin
#print "run: outputms =", outputms
for oms in (tavms, outputms): # Get rid of past problems.
if os.path.isdir(oms):
shutil.rmtree(oms)
# The actual run.
try:
(nrows_aft_tavg,
nrows_aft_cavg,
shp_aft_cavg) = time_then_chan_avg(input_ms, tbin, cbin,
outms=outputms,
chanselstr=spwsel,
zaptemp=False)
except Exception, e:
raise Exception, "Error (%s) running time_then_chan_avg()." % e
###### Test # of rows.
if nrows_aft_tavg != expected[input_ms][spwsel]['nrows_aft_tavg']:
raise Exception, """
The number of rows after time averaging (%d) does not match
the expected number (%d) for input MS %s with selection string "%s"
""" % (nrows_aft_tavg, expected[input_ms][spwsel]['nrows_aft_tavg'],
input_ms, spwsel)
if nrows_aft_cavg != expected[input_ms][spwsel]['nrows_aft_cavg']:
raise Exception, """
The number of rows after channel averaging (%d) does not match
the expected number (%d) for input MS %s with selection string "%s"
""" % (nrows_aft_cavg, expected[input_ms][spwsel]['nrows_aft_cavg'],
input_ms, spwsel)
if shp_aft_cavg != expected[input_ms][spwsel]['datshp']:
raise Exception, """
The data shape (%s) after channel averaging does not match
the expectation (%s) for input MS %s with selection string "%s"
""" % (shp_aft_cavg, expected[input_ms][spwsel]['datshp'],
input_ms, spwsel)
###### Compare averaged data with expectation, and rm the averaged
###### data if it passes.
outputmses = {'tav': tavms, 'cav': outputms}
for avtype, avms in outputmses.iteritems():
expcells = expected[input_ms][spwsel]['cells'][avtype]
try:
tb.open(avms)
except Exception, e:
raise Exception, "Error (%s) opening %s." % (e, avms)
try:
for ((col, row), expent) in expcells.iteritems():
usetol = True
if isinstance(expent, tuple):
tol = expent[1]
expent = expent[0]
elif not isquantized(expent):
tol = 0.005 * expent
else:
usetol = False
gotval = tb.getcell(col, row)
if hasattr(gotval, 'flatten') and gotval.size == 1:
gotval = gotval.flatten()[0]
if usetol:
closeenough = (abs(gotval - expent) < tol)
else:
closeenough = (gotval == expent)
if hasattr(closeenough, 'all'):
closeenough = closeenough.all()
if not closeenough:
if not badcells.has_key(avms):
badcells[avms] = {}
badcells[avms][(col, row)] = (gotval, expent)
except Exception, e:
raise Exception, "Error (%s) checking %s's cell %s." % (e, avms,
(col, row))
finally:
def run(fetch=False):
#####fetch data
if fetch:
for f in data():
copydata(f, os.getcwd())
expected = {
'ggtau.3mm.ph.gcal0': {
'SPLINE_KNOTS_PHASE': {
'rms': 4362063360.0,
'medabsdevmed': 8704.0,
'min': 4362050048.0,
'max': 4362076160.0,
'sum': 872412620800.0,
'quartile': 26112.0,
'median': 4362058752.0,
'sumsq': 3.80551890468e+21,
'stddev': 11866.4301499,
'var': 140812164.503,
'npts': 200,
'mean': 4362063104.0
}
},
'ggtau.1mm.ph.gcal0': {
'SPLINE_KNOTS_PHASE': {
'rms': 4362063360.0,
'medabsdevmed': 8704.0,
'min': 4362050048.0,
'max': 4362076160.0,
'sum': 872412620800.0,
'quartile': 26112.0,
'median': 4362058752.0,
'sumsq': 3.80551890468e+21,
'stddev': 11866.4301499,
'var': 140812164.503,
'npts': 200,
'mean': 4362063104.0
}
}
}
for caltable in caltables:
print "Testing with data", caltable, "..."
if expected.has_key(caltable):
default(calstat)
axis = 'spline_knots_phase'
s = calstat(caltable=caltable, axis=axis)
if s.keys() != expected[caltable].keys():
raise Exception("Wrong dictionary keys. Expected %s, got %s" % \
(expected[caltable], s))
print "Expected =", expected[caltable]
print "Got = ", s
if not s.has_key('SPLINE_KNOTS_PHASE'):
raise Exception(
"Dictionary returned from calstat does not have key SPLINE_KNOTS_PHASE"
)
for e in expected[caltable]['SPLINE_KNOTS_PHASE'].keys():
print "Checking %s: %s vs %s" % \
(e, expected[caltable]['SPLINE_KNOTS_PHASE'][e], s['SPLINE_KNOTS_PHASE'][e])
failed = False
if expected[caltable]['SPLINE_KNOTS_PHASE'][e] == 0:
if s['SPLINE_KNOTS_PHASE'][e] != 0:
failed = True
else:
if abs((expected[caltable]['SPLINE_KNOTS_PHASE'][e] -
s['SPLINE_KNOTS_PHASE'][e]) / expected[caltable]
['SPLINE_KNOTS_PHASE'][e]) > epsilon:
failed = True
# Remove these 3 lines of code, once CAS-1671 is solved
if failed == True and e in ['var', 'stddev']:
print "Ignoring this known problem on 64bit!"
failed = False
if failed:
raise Exception("Numbers differ, expected %s, got %s" % \
(str(expected[caltable]['SPLINE_KNOTS_PHASE'][e]), str(s['SPLINE_KNOTS_PHASE'][e])))
tb.open(caltable)
cols = tb.colnames()
tb.close()
cplx = ['amp', 'amplitude', 'phase', 'imag', 'imaginary', 'real']
for x in cplx:
cols.append(x)
print cols
# remove complex columns
cols.remove('GAIN')
if 'SCALE_FACTOR' in cols: cols.remove('SCALE_FACTOR')
if 'SIDEBAND_REF' in cols: cols.remove('SIDEBAND_REF')
# don't try string columns
cols.remove('FREQ_GROUP_NAME')
cols.remove('FIELD_NAME')
cols.remove('FIELD_CODE')
cols.remove('SOURCE_NAME')
cols.remove('SOURCE_CODE')
if 'POLY_TYPE' in cols: cols.remove('POLY_TYPE')
if 'POLY_MODE' in cols: cols.remove('POLY_MODE')
if 'PHASE_UNITS' in cols: cols.remove('PHASE_UNITS')
# empty column:
if 'VALID_DOMAIN' in cols: cols.remove('VALID_DOMAIN')
cols = [x.lower() for x in cols]
print "Trying these column names", cols
for col in cols:
data_cols = ['']
if col in cplx:
data_cols = ['gain', 'scale_factor']
for dc in data_cols:
print "Call with caltable =", caltable, "; axis =", col, "; datacolumn =", dc
if dc != '':
s = calstat(caltable=caltable, axis=col, datacolumn=dc)
else:
s = calstat(caltable=caltable, axis=col)
if col.upper() == "FLAG_CATEGORY":
# The MSs used have no data in FLAG_CATEGORY, therefore
# calstat() should fail
if s != None:
raise Exception("Error! " + str(s))
elif not type(s) is dict:
raise Exception("Error! Return value " + str(s) +
" is not a dictionary")
print ''
print 'Regression PASSED'
print ''
return []
def run( fetch=False ):
#####fetch data
if fetch:
for f in data( ):
copydata( f, os.getcwd( ) )
expected = {'ggtau.3mm.ph.gcal0':
{'SPLINE_KNOTS_PHASE':{'rms': 4362063360.0,
'medabsdevmed': 13056.0,
'min': 4362050048.0,
'max': 4362076160.0,
'sum': 872412620800.0,
'quartile': 26112.0,
'median': 4362063104.0,
'sumsq': 3.80551890468e+21,
'stddev': 11866.4301499,
'var': 140812164.503,
'npts': 200,
'mean': 4362063104.0}},
'ggtau.1mm.ph.gcal0':
{'SPLINE_KNOTS_PHASE':{'rms': 4362063360.0,
'medabsdevmed': 13056.0,
'min': 4362050048.0,
'max': 4362076160.0,
'sum': 872412620800.0,
'quartile': 26112.0,
'median': 4362063104.0,
'sumsq': 3.80551890468e+21,
'stddev': 11866.4301499,
'var': 140812164.503,
'npts': 200,
'mean': 4362063104.0}}}
for caltable in caltables:
print "Testing with data", caltable, "..."
if expected.has_key(caltable):
default(calstat)
axis='spline_knots_phase'
s = calstat(caltable=caltable, axis=axis)
if s.keys() != expected[caltable].keys():
raise Exception("Wrong dictionary keys. Expected %s, got %s" % \
(expected[caltable], s))
print "Expected =", expected[caltable]
print "Got = ", s
if not s.has_key('SPLINE_KNOTS_PHASE'):
raise Exception("Dictionary returned from calstat does not have key SPLINE_KNOTS_PHASE")
for e in expected[caltable]['SPLINE_KNOTS_PHASE'].keys():
print "Checking %s: %s vs %s" % \
(e, expected[caltable]['SPLINE_KNOTS_PHASE'][e], s['SPLINE_KNOTS_PHASE'][e])
failed = False
if expected[caltable]['SPLINE_KNOTS_PHASE'][e] == 0:
if s['SPLINE_KNOTS_PHASE'][e] != 0:
failed = True
else:
if abs((expected[caltable]['SPLINE_KNOTS_PHASE'][e] - s['SPLINE_KNOTS_PHASE'][e])/expected[caltable]['SPLINE_KNOTS_PHASE'][e]) > epsilon:
failed = True
# Remove these 3 lines of code, once CAS-1671 is solved
if failed == True and e in ['var', 'stddev']:
print "Ignoring this known problem on 64bit!"
failed = False
if failed:
raise Exception("Numbers differ, expected %s, got %s" % \
(str(expected[caltable]['SPLINE_KNOTS_PHASE'][e]), str(s['SPLINE_KNOTS_PHASE'][e])))
tb.open(caltable)
cols = tb.colnames()
tb.close()
cplx = ['amp', 'amplitude', 'phase', 'imag', 'imaginary', 'real']
for x in cplx:
cols.append(x)
print cols
# remove complex columns
cols.remove('GAIN')
if 'SCALE_FACTOR' in cols: cols.remove('SCALE_FACTOR')
if 'SIDEBAND_REF' in cols: cols.remove('SIDEBAND_REF')
# don't try string columns
cols.remove('FREQ_GROUP_NAME')
cols.remove('FIELD_NAME')
cols.remove('FIELD_CODE')
cols.remove('SOURCE_NAME')
cols.remove('SOURCE_CODE')
if 'POLY_TYPE' in cols: cols.remove('POLY_TYPE')
if 'POLY_MODE' in cols: cols.remove('POLY_MODE')
if 'PHASE_UNITS' in cols: cols.remove('PHASE_UNITS')
# empty column:
if 'VALID_DOMAIN' in cols: cols.remove('VALID_DOMAIN')
cols = [x.lower() for x in cols]
print "Trying these column names", cols
for col in cols:
data_cols = ['']
if col in cplx:
data_cols = ['gain', 'scale_factor']
for dc in data_cols:
print "Call with caltable =", caltable, "; axis =", col, "; datacolumn =", dc
if dc != '':
s = calstat(caltable=caltable, axis=col, datacolumn=dc)
else:
s = calstat(caltable=caltable, axis=col)
if col.upper() == "FLAG_CATEGORY":
# The MSs used have no data in FLAG_CATEGORY, therefore
# calstat() should fail
if s != None:
raise Exception("Error! " + str(s))
elif not type(s) is dict:
raise Exception("Error! Return value " + str(s) + " is not a dictionary")
print ''
print 'Regression PASSED'
print ''
return []