def plotVisibilities(self,star_grid=[],cfg='',no_models=0,\
fn_add_star=0):
"""
Plot visibilities as a function of baseline.
Wavelengths plotted are what is requested in the ray tracing
Includes data preparation on the spot.
Data location is that of correlated flux (for the visibilities), but
also requires an sed object to retrieve the MIDI spectrum. If one of
them is not available, models are be plotted without data.
@keyword star_grid: list of Star() models to plot. If star_grid is [],
only data are plotted.
(default: [])
@type star_grid: list[Star()]
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword no_models: Only show data.
(default: 0)
@type no_models: bool
@keyword fn_add_star: Add the star name to the requested plot filename.
(default: 1)
@type fn_add_star: bool
"""
if not cc.path.dcflux:
print 'No dcflux given in Path.dat. Aborting...'
return
if not self.sed or 'MIDI' not in self.sed.data_types:
print 'No dsed given in Path.dat or no MIDI spectral data found. Aborting.'
return
print '***********************************'
print '** Creating Visibilities plot.'
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('no_models'):
no_models = cfg_dict['no_models']
if cfg_dict.has_key('fn_add_star'):
fn_add_star = bool(cfg_dict['fn_add_star'])
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = ''
#- filename settings and copying inputfiles to plot output folder
if not fn_plt:
fn_plt = os.path.join(self.pplot,'Visibilities')
if fn_add_star:
fn_plt = '_'.join([fn_plt,self.star_name])
if self.inputfilename <> None:
ipfn = os.path.split(self.inputfilename)[1]
subprocess.call(['cp ' + self.inputfilename + ' ' + \
os.path.join(self.pplot,ipfn)],\
shell=True)
#-- Read the models. Wavelengths are taken from the ray-tracing output
models = []
if not no_models:
for s in star_grid:
model_id = s['LAST_MCMAX_MODEL']
dpath = os.path.join(cc.path.mout,'models',model_id)
model = MCMax.readVisibilities(dpath=dpath,\
fn_vis='basevis01.0.dat')
models.append(model)
real_models = [model for model in models if model]
if not real_models:
no_models = 1
else:
wavelengths = sorted(real_models[0]['wavelength'].keys())
if no_models: wavelengths = (8.,10.,13.)
#-- Grab the MIDI spectrum
fn = self.sed.data_filenames[self.sed.data_types.index('MIDI')]
midi_flux = self.sed.data[('MIDI',fn)][1]
midi_err = self.sed.data[('MIDI',fn)][2]
midi_relerr = (midi_err/midi_flux)**2
#-- Select MIDI data. Assumes baseline at the end of the filename.
ssd = os.path.join(cc.path.dcflux,self.star_name,\
'_'.join([self.star_name,'MIDI','*.fits']))
files = [os.path.splitext(gi)[0] for gi in glob.glob(ssd)]
ggd = dict([(float(gi.split('_')[-1].strip('m')),gi+'.fits')
for gi in files])
#-- Collect MIDI data from the fits file and calculate visibilities
ddf = dict()
for k,v in sorted(ggd.items()):
ddf[k] = dict()
dfits = pyfits.open(v)
#-- Read the wavelength
cwave = 1e6*dfits['OI_WAVELENGTH'].data['EFF_WAVE'][::-1]
#-- Read flux + err and select the right range
cflux = dfits['OI_VIS'].data['VISAMP'][0][::-1]
cflux = cflux[(cwave<=13.)*(cwave>=8.)]
cflux_err = dfits['OI_VIS'].data['VISAMPERR'][0][::-1]
cflux_err = cflux_err[(cwave<=13.)*(cwave>=8.)]
#-- The visibilities are correlated flux divided by real flux
ddf[k]['y'] = cflux/midi_flux
#-- Error propagation
cflux_relerr = (cflux_err/cflux)**2
yerr = np.sqrt(midi_relerr + cflux_relerr)*cflux/midi_flux
ddf[k]['yerr'] = yerr
#-- Wavelength grid
ddf[k]['x'] = cwave[(cwave<=13.)*(cwave>=8.)]
dfits.close()
#-- prepare and collect plot data, keytags and line types
data = []
for w in wavelengths:
ddict = dict()
data.append(ddict)
#-- Set the plot x and y
bls = [k for k in sorted(ddf.keys())]
ddict['x'] = [[bl for bl in bls]]
ddict['y'] = [[ddf[bl]['y'][np.argmin(abs(ddf[bl]['x']-w))]
for bl in bls]]
ddict['yerr'] = [[ddf[bl]['yerr'][np.argmin(abs(ddf[bl]['x']-w))]
for bl in bls]]
#-- Set limits and labels
ddict['labels'] = [('MIDI %s $\\mu$m'%w,0.85,0.9)]
if no_models:
continue
#-- Extract models from the model folders
for model in models:
ddict['yerr'].append(None)
if not model:
ddict['x'].append(np.empty(0))
ddict['y'].append(np.empty(0))
continue
ddict['x'].append(model['baseline'])
ddict['y'].append(model['wavelength'][w])
kwargs = dict()
kwargs['keytags'] = ['MIDI']
if not no_models:
kwargs['keytags'].extend([s['LAST_MCMAX_MODEL'].replace('_','\_')
for s in star_grid])
kwargs['xaxis'] = 'Baseline (m)'
kwargs['yaxis'] = 'Visibility'
kwargs['dimensions'] = (1,len(data)+1)
kwargs['figsize'] = (10,15)
kwargs['fontsize_axis'] = 20
kwargs['fontsize_ticklabels'] = 20
kwargs['fontsize_key'] = 18
kwargs['fontsize_label'] = 14
kwargs['linewidth'] = 3
kwargs['cfg'] = cfg_dict
kwargs['extension'] = '.pdf'
kwargs['hspace'] = 0.3
kwargs['ws_bot'] = 0.01
kwargs['ws_top'] = 0.99
kwargs['ws_left'] = 0.10
kwargs['ws_right'] = 0.98
filename = Plotting2.plotTiles(data=data,filename=fn_plt,**kwargs)
print '** Your Correlated Flux plots can be found at:'
print filename
print '***********************************'
def plotCorrflux(self, star_grid=[], cfg='', no_models=0, fn_add_star=0):
"""
Plot correlated fluxes with 0, 1 or more models and data.
Includes data preparation on the spot.
@keyword star_grid: list of Star() models to plot. If star_grid is [],
only data are plotted.
(default: [])
@type star_grid: list[Star()]
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword no_models: Only show data.
(default: 0)
@type no_models: bool
@keyword fn_add_star: Add the star name to the requested plot filename.
(default: 1)
@type fn_add_star: bool
"""
if not cc.path.dcflux:
print 'No dcflux given in Path.dat. Cannot plot Correlated Fluxes. Aborting...'
return
print '***********************************'
print '** Creating Correlated Fluxes plot.'
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('no_models'):
no_models = cfg_dict['no_models']
if cfg_dict.has_key('fn_add_star'):
fn_add_star = bool(cfg_dict['fn_add_star'])
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = ''
#- filename settings and copying inputfiles to plot output folder
if not fn_plt:
fn_plt = os.path.join(self.pplot, 'CorrFlux')
if fn_add_star:
fn_plt = '_'.join([fn_plt, self.star_name])
if self.inputfilename <> None:
ipfn = os.path.split(self.inputfilename)[1]
subprocess.call(['cp ' + self.inputfilename + ' ' + \
os.path.join(self.pplot,ipfn)],\
shell=True)
#-- Select MIDI data. Assumes baseline at the end of the filename.
ssd = os.path.join(cc.path.dcflux,self.star_name,\
'_'.join([self.star_name,'MIDI','*.fits']))
files = [os.path.splitext(gi)[0] for gi in glob.glob(ssd)]
ggd = dict([(float(gi.split('_')[-1].strip('m')), gi + '.fits')
for gi in files])
#-- Read the models
models = []
if not no_models:
for s in star_grid:
model_id = s['LAST_MCMAX_MODEL']
dpath = os.path.join(cc.path.mout, 'models', model_id)
model = MCMax.readVisibilities(dpath=dpath,\
fn_vis='visibility01.0.dat')
models.append(model)
real_models = [model for model in models if model]
if not real_models:
no_models = 1
baselines = sorted(ggd.keys())
else:
baselines = sorted(real_models[0]['baseline'].keys())
#-- prepare and collect data, keytags and line types
data = []
for bl in baselines:
ddict = dict()
data.append(ddict)
#-- Extract data from the fits file
if ggd.has_key(bl):
dfits = pyfits.open(ggd[bl])
x = 1e6 * dfits['OI_WAVELENGTH'].data['EFF_WAVE'][::-1]
ddict['x'] = [x]
ddict['y'] = [dfits['OI_VIS'].data['VISAMP'][0]][::-1]
ddict['yerr'] = [dfits['OI_VIS'].data['VISAMPERR'][0]][::-1]
dfits.close()
else:
ddict['x'] = []
ddict['y'] = []
ddict['yerr'] = []
if no_models:
continue
#-- Extract models from the model folders
for model in models:
ddict['yerr'].append(None)
if not model:
ddict['x'].append(np.empty(0))
ddict['y'].append(np.empty(0))
continue
ddict['x'].append(model['wavelength'])
ddict['y'].append(model['flux'] * model['baseline'][bl])
#-- Set some plot limits
ddict['xmin'] = 8
ddict['xmax'] = 13
ddict['ymin'] = -0.1
ddict['labels'] = [('MIDI %.1f m' % bl, 0.05, 0.9)]
#-- Wavelength limits between 8 and 13 micron, limits of the N band
# atmospheric transmission. Outside these ranges, the flux is not
# relevant
ddict['ymax'] = 1.1 * max([
max(iy[(ix <= 13.) * (ix >= 8.)])
for ix, iy in zip(ddict['x'], ddict['y']) if iy.size
])
kwargs = dict()
kwargs['keytags'] = ['MIDI']
if not no_models:
kwargs['keytags'].extend(
[s['LAST_MCMAX_MODEL'].replace('_', '\_') for s in star_grid])
kwargs['xaxis'] = '$\lambda$ ($\mu$m)'
kwargs['yaxis'] = 'Corr.~FLux (Jy)'
kwargs['dimensions'] = (1, len(data) + 1)
kwargs['figsize'] = (10, 15)
kwargs['fontsize_axis'] = 20
kwargs['fontsize_ticklabels'] = 20
kwargs['fontsize_key'] = 18
kwargs['fontsize_label'] = 14
kwargs['linewidth'] = 3
kwargs['cfg'] = cfg_dict
kwargs['extension'] = '.pdf'
kwargs['hspace'] = 0.3
kwargs['ws_bot'] = 0.01
kwargs['ws_top'] = 0.99
kwargs['ws_left'] = 0.10
kwargs['ws_right'] = 0.98
filename = Plotting2.plotTiles(data=data, filename=fn_plt, **kwargs)
print '** Your Correlated Flux plots can be found at:'
print filename
print '***********************************'
def plotCorrflux(self,star_grid=[],cfg='',no_models=0,fn_add_star=0):
"""
Plot correlated fluxes with 0, 1 or more models and data.
Includes data preparation on the spot.
@keyword star_grid: list of Star() models to plot. If star_grid is [],
only data are plotted.
(default: [])
@type star_grid: list[Star()]
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword no_models: Only show data.
(default: 0)
@type no_models: bool
@keyword fn_add_star: Add the star name to the requested plot filename.
(default: 1)
@type fn_add_star: bool
"""
if not cc.path.dcflux:
print 'No dcflux given in Path.dat. Cannot plot Correlated Fluxes. Aborting...'
return
print '***********************************'
print '** Creating Correlated Fluxes plot.'
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('no_models'):
no_models = cfg_dict['no_models']
if cfg_dict.has_key('fn_add_star'):
fn_add_star = bool(cfg_dict['fn_add_star'])
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = ''
#- filename settings and copying inputfiles to plot output folder
if not fn_plt:
fn_plt = os.path.join(self.pplot,'CorrFlux')
if fn_add_star:
fn_plt = '_'.join([fn_plt,self.star_name])
if self.inputfilename <> None:
ipfn = os.path.split(self.inputfilename)[1]
subprocess.call(['cp ' + self.inputfilename + ' ' + \
os.path.join(self.pplot,ipfn)],\
shell=True)
#-- Select MIDI data. Assumes baseline at the end of the filename.
ssd = os.path.join(cc.path.dcflux,self.star_name,\
'_'.join([self.star_name,'MIDI','*.fits']))
files = [os.path.splitext(gi)[0] for gi in glob.glob(ssd)]
ggd = dict([(float(gi.split('_')[-1].strip('m')),gi+'.fits')
for gi in files])
#-- Read the models
models = []
if not no_models:
for s in star_grid:
model_id = s['LAST_MCMAX_MODEL']
dpath = os.path.join(cc.path.mout,'models',model_id)
model = MCMax.readVisibilities(dpath=dpath,\
fn_vis='visibility01.0.dat')
models.append(model)
real_models = [model for model in models if model]
if not real_models:
no_models = 1
baselines = sorted(ggd.keys())
else:
baselines = sorted(real_models[0]['baseline'].keys())
#-- prepare and collect data, keytags and line types
data = []
for bl in baselines:
ddict = dict()
data.append(ddict)
#-- Extract data from the fits file
if ggd.has_key(bl):
dfits = pyfits.open(ggd[bl])
x = 1e6*dfits['OI_WAVELENGTH'].data['EFF_WAVE'][::-1]
ddict['x'] = [x]
ddict['y'] = [dfits['OI_VIS'].data['VISAMP'][0]][::-1]
ddict['yerr'] = [dfits['OI_VIS'].data['VISAMPERR'][0]][::-1]
dfits.close()
else:
ddict['x'] = []
ddict['y'] = []
ddict['yerr'] = []
if no_models:
continue
#-- Extract models from the model folders
for model in models:
ddict['yerr'].append(None)
if not model:
ddict['x'].append(np.empty(0))
ddict['y'].append(np.empty(0))
continue
ddict['x'].append(model['wavelength'])
ddict['y'].append(model['flux']*model['baseline'][bl])
#-- Set some plot limits
ddict['xmin'] = 8
ddict['xmax'] = 13
ddict['ymin'] = -0.1
ddict['labels'] = [('MIDI %.1f m'%bl,0.05,0.9)]
#-- Wavelength limits between 8 and 13 micron, limits of the N band
# atmospheric transmission. Outside these ranges, the flux is not
# relevant
ddict['ymax'] = 1.1*max([max(iy[(ix<=13.)*(ix>=8.)])
for ix,iy in zip(ddict['x'],ddict['y'])
if iy.size])
kwargs = dict()
kwargs['keytags'] = ['MIDI']
if not no_models:
kwargs['keytags'].extend([s['LAST_MCMAX_MODEL'].replace('_','\_')
for s in star_grid])
kwargs['xaxis'] = '$\lambda$ ($\mu$m)'
kwargs['yaxis'] = 'Corr.~FLux (Jy)'
kwargs['dimensions'] = (1,len(data)+1)
kwargs['figsize'] = (10,15)
kwargs['fontsize_axis'] = 20
kwargs['fontsize_ticklabels'] = 20
kwargs['fontsize_key'] = 18
kwargs['fontsize_label'] = 14
kwargs['linewidth'] = 3
kwargs['cfg'] = cfg_dict
kwargs['extension'] = '.pdf'
kwargs['hspace'] = 0.3
kwargs['ws_bot'] = 0.01
kwargs['ws_top'] = 0.99
kwargs['ws_left'] = 0.10
kwargs['ws_right'] = 0.98
filename = Plotting2.plotTiles(data=data,filename=fn_plt,**kwargs)
print '** Your Correlated Flux plots can be found at:'
print filename
print '***********************************'
