def runStatistics(self):
'''
Run the statistics module.
'''
self.pacsstats = None
self.spirestats = None
self.resostats = None
if self.statistics and self.pacs <> None:
print '************************************************'
print '**** Doing PACS statistics for %s.'%self.star_name
print '************************************************'
self.pacsstats = UnresoStats.UnresoStats(star_name=self.star_name,\
path_code=self.path_gastronoom)
self.pacsstats.setInstrument(instrument_name='PACS',\
instrument_instance=self.pacs,\
stat_method=self.stat_method)
self.pacsstats.setModels(star_grid=self.star_grid)
self.pacsstats.setRatios(chi2_type=self.stat_chi2)
self.pacsstats.plotRatioWav(inputfilename=self.inputfilename)
if self.statistics and self.spire <> None:
print '************************************************'
print '**** Doing SPIRE statistics for %s.'%self.star_name
print '************************************************'
self.spirestats = UnresoStats.UnresoStats(star_name=self.star_name,\
path_code=self.path_gastronoom)
self.spirestats.setInstrument(instrument_name='SPIRE',\
instrument_instance=self.spire,\
stat_method=self.stat_method)
self.spirestats.setModels(star_grid=self.star_grid)
self.spirestats.setRatios(chi2_type=self.stat_chi2)
self.spirestats.plotRatioWav(inputfilename=self.inputfilename)
if self.statistics:
trans_sel = Transition.extractTransFromStars(self.star_grid,\
dtype='resolved')
if not trans_sel:
return
print '************************************************'
print '**** Doing statistics for spectrally resolved lines in %s.'\
%self.star_name
print '**** Use cc_session.resostats for more interactive tools.'
print '************************************************'
self.resostats = ResoStats.ResoStats(star_name=self.star_name,\
path_code=self.path_gastronoom,\
lll_p=self.stat_lll_p)
self.resostats.setInstrument(trans_sel)
self.resostats.setModels(star_grid=self.star_grid)
self.resostats.setIntensities()
if self.stat_print:
self.resostats.printStats()
def setRatios(self, chi2_type='normal'):
'''
Find the peak to peak ratios of data versus model.
The result are saved in the self.peak_ratios dictionary, see
__init__.__doc__()
@keyword chi2_type: The type of chi-squared calculated for integrated
fluxes. 'normal' for the usual kind, 'log' for chi2
of the log of the integrated fluxes and noise.
(default: normal)
@type chi2_type: string
'''
inst = self.instrument
print '***********************************'
print '** Calculating integrated/peak intensity ratios for %s.'\
%inst.instrument
#-- Make a sample selection of Transition()s.
sample_trans = Transition.extractTransFromStars(self.star_grid,\
dtype=inst.instrument)
#-- Set the tolerance: a factor that is multiplied with half the
# wavelength resolution of data when looking for the peak value
# around a central wavelength in the data. As a default, this is
# equal to the PACS_OVERSAMPLING. No point in changing this.
self.tolerance = inst.oversampling
for ifn,(fn,dwav) in enumerate(zip(inst.data_filenames,\
inst.data_wave_list)):
#-- Create a list of sample transitions
self.sample_trans[fn] = [trans
for trans in sample_trans
if trans.wavelength*10**4 >= dwav[0]\
and trans.wavelength*10**4 <= dwav[-2]]
#-- Get the central wavelength of the lines, corrected for
# Doppler shift due to vlsr of the central source. In micron.
self.central_mwav[fn] = [
t.wavelength * 10**4 * 1. / (1 - inst.vlsr / t.c)
for t in self.sample_trans[fn]
]
self.__setPeakRatios(ifn, fn)
if inst.linefit <> None:
self.__setIntRatios(ifn, fn, chi2_type=chi2_type)
self.calcChiSquared()
print '***********************************'
def setRatios(self,chi2_type='normal'):
'''
Find the peak to peak ratios of data versus model.
The result are saved in the self.peak_ratios dictionary, see
__init__.__doc__()
@keyword chi2_type: The type of chi-squared calculated for integrated
fluxes. 'normal' for the usual kind, 'log' for chi2
of the log of the integrated fluxes and noise.
(default: normal)
@type chi2_type: string
'''
inst = self.instrument
print '***********************************'
print '** Calculating integrated/peak intensity ratios for %s.'\
%inst.instrument
#-- Make a sample selection of Transition()s.
sample_trans = Transition.extractTransFromStars(self.star_grid,\
dtype=inst.instrument)
#-- Set the tolerance: a factor that is multiplied with half the
# wavelength resolution of data when looking for the peak value
# around a central wavelength in the data. As a default, this is
# equal to the PACS_OVERSAMPLING. No point in changing this.
self.tolerance = inst.oversampling
for ifn,(fn,dwav) in enumerate(zip(inst.data_filenames,\
inst.data_wave_list)):
#-- Create a list of sample transitions
self.sample_trans[fn] = [trans
for trans in sample_trans
if trans.wavelength*10**4 >= dwav[0]\
and trans.wavelength*10**4 <= dwav[-2]]
#-- Get the central wavelength of the lines, corrected for
# Doppler shift due to vlsr of the central source. In micron.
self.central_mwav[fn] = [t.wavelength*10**4*1./(1-inst.vlsr/t.c)
for t in self.sample_trans[fn]]
self.__setPeakRatios(ifn,fn)
if inst.linefit <> None:
self.__setIntRatios(ifn,fn,chi2_type=chi2_type)
self.calcChiSquared()
print '***********************************'
def setLineStrengths(self):
'''
Find the peak to peak ratios of data versus model.
The result are saved in the self.peak_ratios dictionary, see
__init__.__doc__()
'''
inst = self.instrument
print '***********************************'
print '** Gathering model and observed line strengths for %s.'\
%inst.instrument
#-- Make a sample selection of Transition()s.
sample_trans = Transition.extractTransFromStars(self.star_grid,\
dtype=inst.instrument)
#-- Set the tolerance: a factor that is multiplied with half the
# wavelength resolution of data when looking for the peak value
# around a central wavelength in the data. As a default, this is
# equal to the PACS_OVERSAMPLING. No point in changing this.
self.tolerance = inst.oversampling
for ifn,(fn,dwav) in enumerate(zip(inst.data_filenames,\
inst.data_wave_list)):
#-- Create a list of sample transitions
self.sample_trans[fn] = [trans
for trans in sample_trans
if trans.wavelength*10**4 >= dwav[0]\
and trans.wavelength*10**4 <= dwav[-2]]
#-- Get the central wavelength of the lines, corrected for
# Doppler shift due to vlsr of the central source. In micron.
self.central_mwav[fn] = [
t.wavelength * 10**4 * 1. / (1 - inst.vlsr / t.c)
for t in self.sample_trans[fn]
]
self.__setPeakRatios(ifn, fn)
if not inst.linefit is None:
self.__setIntRatios(ifn, fn)
print '***********************************'
def setLineStrengths(self):
'''
Find the peak to peak ratios of data versus model.
The result are saved in the self.peak_ratios dictionary, see
__init__.__doc__()
'''
inst = self.instrument
print '***********************************'
print '** Gathering model and observed line strengths for %s.'\
%inst.instrument
#-- Make a sample selection of Transition()s.
sample_trans = Transition.extractTransFromStars(self.star_grid,\
dtype=inst.instrument)
#-- Set the tolerance: a factor that is multiplied with half the
# wavelength resolution of data when looking for the peak value
# around a central wavelength in the data. As a default, this is
# equal to the PACS_OVERSAMPLING. No point in changing this.
self.tolerance = inst.oversampling
for ifn,(fn,dwav) in enumerate(zip(inst.data_filenames,\
inst.data_wave_list)):
#-- Create a list of sample transitions
self.sample_trans[fn] = [trans
for trans in sample_trans
if trans.wavelength*10**4 >= dwav[0]\
and trans.wavelength*10**4 <= dwav[-2]]
#-- Get the central wavelength of the lines, corrected for
# Doppler shift due to vlsr of the central source. In micron.
self.central_mwav[fn] = [t.wavelength*10**4*1./(1-inst.vlsr/t.c)
for t in self.sample_trans[fn]]
self.__setPeakRatios(ifn,fn)
if not inst.linefit is None:
self.__setIntRatios(ifn,fn)
print '***********************************'
