def plotVelocity(self,
star_grid=[],
models=[],
fn_plt='',
force_plot=0,
cfg=''):
'''
Plot velocity versus radius for every model in star_grid.
@keyword star_grid: List of Star() instances. If default, model ids
have to be given.
(default: [])
@type star_grid: list[Star()]
@keyword models: The model ids, only required if star_grid is []
(default: [])
@type models: list[string]
@keyword fn_plt: A base plot filename. Includes folder. If not, a
default is added
(default: '')
@type fn_plt: string
@keyword force_plot: force a plotting if more than models are requested
(default: 0)
@type force_plot: bool
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
'''
print '***********************************'
print '** Plotting Gas Velocity Profiles'
if not star_grid and models:
star_grid = self.makeStars(models=models)
elif (not models and not star_grid) or (models and star_grid):
print '** Input is undefined or doubly defined. Aborting.'
return
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict.pop('filename')
if len(star_grid) < 20 or force_plot:
valid_sg = [
star for star in star_grid if star['LAST_CHEMISTRY_MODEL']
]
folders = [os.path.join(cc.path.cout,'models',\
star['LAST_CHEMISTRY_MODEL'])+'/' for star in valid_sg]
radii = [DataIO.getChemistryPhysPar(folder+'csphyspar.out', 'RADIUS') \
for folder in folders]
temps = [DataIO.getChemistryPhysPar(folder+'csphyspar.out', 'VELOCITY') \
for folder in folders]
if temps:
keytags = [
star['LAST_CHEMISTRY_MODEL'].replace('_', '\_')
for star in valid_sg
]
#-- Set filenames
pfn = fn_plt if fn_plt else 'velocity_profiles'
pfn = self.setFnPlt(pfn)
#-- Run the two plots
keys_cm = [
'Model %i' % (i + 1) for i in xrange(len(star_grid))
]
pfn = Plotting2.plotCols(x=radii,y=temps,cfg=cfg_dict,\
filename=pfn,xaxis='$r$ (cm)',\
yaxis=r'$v$ (km s$^{-1}$)',\
figsize=(12.5,8),fontsize_ticklabels=26,\
key_location=(0.05,0.05),xlogscale=1,ylogscale=1,\
keytags=keys_cm,fontsize_axis=26,fontsize_key=26)
print '** Plots can be found at:'
print pfn
print '***********************************'
def plotOpacities(self,star_grid=[],scaling=1,species=['AMC'],\
cfg='',index=0,*args,**kwargs):
"""
Plotting wavelength dependent mass extinction coefficients
(ie opacities).
If based on star_grid or modelslist, they are scaled with abundances if
wanted.
If no model info is given, the input opacities are plotted.
Args and kwargs can be given straight to the plot command.
@keyword star_grid: The input Star() models. If default, the MCMax
input opacities are plotted.
(default: [])
@type star_grid: list(Star())
@keyword scaling: allow species abundance scaling of opacities
(default: 1)
@type scaling: bool
@keyword species: If no star_grid or model list are given, this gives
the species requested to be plotted from Dust.dat
(default: ['AMC'])
@type species: list(string)
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword index: The index of the kappas in the .opacity/.particle file.
0: extinction, 1: absorption, 2: scattering
(default: 0)
@type index: int
"""
print '***********************************'
print '** Starting to plot dust opacities.'
#-- Set the filename
cfg_dict = Plotting2.readCfg(cfg)
ppars = dict()
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
elif kwargs.has_key('filename'):
fn_plt = kwargs['filename']
del kwargs['filename']
elif not star_grid:
fn_plt = os.path.join(cc.path.mopac,\
'dust_opacities_%s'%'_'.join(species))
else:
fn_plt = os.path.join(self.pplot, 'opacities_species')
#-- Set some plot parameters
ppars['xaxis'] = '$\lambda$ ($\mu \mathrm{m}$)'
ppars['yaxis'] = '$\kappa_\lambda$ ($\mathrm{cm}^2\mathrm{/g}$)'
ppars['fontsize_key'] = 20
ppars['xlogscale'] = 1
ppars['ylogscale'] = 1
ppars['key_location'] = (0.05, 0.05)
ppars.update(kwargs)
ppars.update(cfg_dict)
#-- Check if raw opacities or modeling results are requested
if not star_grid:
kr = KappaReader.KappaReader()
wl_list = [kr.getKappas(sp)[0] for sp in species]
q_list = [kr.getKappas(sp)[1] for sp in species]
fn_plt = Plotting2.plotCols(x=wl_list,y=q_list,filename=fn_plt,\
plot_title = 'Dust Opacities',\
keytags=species,*args,**ppars)
print '** Your plot can be found at:'
print fn_plt
else:
fns = []
for star in star_grid:
try:
wave, opacities = star.readKappas()
except IOError:
continue
opacities = [
(opacities[i] + opacities[i + len(star.getDustList())])
for i, species in enumerate(star.getDustList())
]
if scaling:
opacities = [
opa * star['A_%s' % sp]
for opa, sp in zip(opacities, species)
]
fn_mplt = '_'.join(fn_plt, star['LAST_MCMAX_MODEL'])
title = 'Dust Opacities in %s (%s)' \
%(self.star_name_plots,\
star['LAST_MCMAX_MODEL'].replace('_','\_'))
keys = ['%s with $A$ = %s and $T_{des} = %i$ K'\
%(sp,str(star['A_%s'%sp]),int(star['T_DES_%s'%sp]))
for sp in star.getDustList()]
fns.append(Plotting2.plotCols(x=wave,y=opacities,keytags=keys,\
plot_title=title,\
filename=fn_mplt,*args,**ppars))
if len(fns) != len(star_grid):
print 'At least one of the models requested does not yet ' + \
'have a MCMax model.'
print '** Your plots can be found at:'
if fns[-1][-4] == '.pdf':
fn_plt = fn_plt + '.pdf'
DataIO.joinPdf(old=fns, new=fn_plt)
print fn_plt
else:
print '\n'.join(fns)
print '***********************************'
def plotAbundanceProfiles(self,star_grid=[],models=[],force_plot=0,cfg='',\
fn_plt='',molecules=[],per_molecule=0,frac=1):
'''
Plot abundance profiles for all molecules in every model.
@keyword star_grid: List of Star() instances. If default, model ids
have to be given.
(default: [])
@type star_grid: list[Star()]
@keyword models: The model ids, only required if star_grid is []
(default: [])
@type models: list[string]
@keyword force_plot: force a plotting if more than models are requested
(default: 0)
@type force_plot: bool
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword fn_plt: A base plot filename. Includes folder. If not, a
default is added
(default: '')
@type fn_plt: string
@keyword molecules: Molecules to be plotted.
(default: [])
@type molecules: bool
@keyword per_molecule: Plot one molecule for all models in one figure.
(default: 0)
@type per_molecule: bool
@keyword per_model: Plot all molecules for one model in one figure.
(default: 0)
@type per_model: bool
@keyword frac: Plot the fractional abundances. If not frac, plot number
densities.
(default: 1)
@type frac: bool
'''
print '***********************************'
print '** Plotting Abundance Profiles'
if not star_grid and models:
star_grid = self.makeStars(models=models)
elif (not models and not star_grid) or (models and star_grid):
print '** Input is undefined or doubly defined. Aborting.'
return
pfns = []
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict.pop('filename')
if cfg_dict.has_key('molecules'):
molecules = cfg_dict.pop('molecules')
if cfg_dict.has_key('per_molecule'):
per_molecule = cfg_dict['per_molecule']
if cfg_dict.has_key('per_model'):
per_model = cfg_dict['per_model']
#-- Some general plot settings
extra_pars = dict()
extra_pars['ymin'] = 1e-9
extra_pars['ymax'] = 1e-3
extra_pars['ylogscale'] = 1
extra_pars['xlogscale'] = 1
extra_pars['figsize'] = (12.5, 8.5)
extra_pars['xaxis'] = 'cm'
#-- Dict to keep track of all data
ddata = dict()
for istar, star in enumerate(star_grid):
if not star['LAST_CHEMISTRY_MODEL']: continue
ddata[istar] = dict()
folder = os.path.join(cc.path.cout,'models',\
star['LAST_CHEMISTRY_MODEL'])+'/'
ddata[istar]['rad'] = DataIO.getChemistryPhysPar(folder+\
'csphyspar.out', 'RADIUS')
ddata[istar]['id'] = star['LAST_CHEMISTRY_MODEL']
if frac:
species = DataIO.getChemistryAbundances(folder + 'csfrac.out')
else:
species = DataIO.getChemistryAbundances(folder + 'csnum.out')
for molec in molecules:
ddata[istar][molec] = species[molec]
if not per_molecule:
#-- Collect all data
radii = [ddata[istar]['rad']] * len(molecules)
abuns = [ddata[istar][molec] for molec in molecules]
keytags = molecules
#ids = star['LAST_CHEMISTRY_MODEL']
ids = ddata[istar]['id']
#-- Set the yaxis tag
yaxis = '$n_\mathrm{molec}/n_{\mathrm{H}_2}$'
#-- Set filename
pfn = fn_plt if fn_plt else 'abundance_profiles'
suff = '_'.join(list(set(ids)))
pfn = self.setFnPlt(pfn, fn_suffix=suff)
pfns.append(Plotting2.plotCols(x=radii,y=abuns,cfg=cfg_dict,\
filename=pfn,keytags=keytags,\
plot_title=ids.replace('_','\_'),\
yaxis=yaxis,**extra_pars))
if per_molecule:
#-- Collect all data
#molecs = list(set([molec for istar in ddata.keys()
#for molec in ddata[istar].keys()]))
for molec in molecules:
#-- Collect data
radii = [dstar['rad'] for istar, dstar in ddata.items()]
abuns = [dstar[molec] for istar, dstar in ddata.items()]
keytags = [
dstar['id'].replace('_', '\_')
for istar, dstar in ddata.items()
]
#-- Set the y axis tag
#strmolec = ddata[0][molec]['key']
yaxis = '$n_\mathrm{%s}/n_{\mathrm{H}_2}$' % str(molec)
#-- Make filename
pfn = fn_plt if fn_plt else 'abundance_profiles'
pfn = self.setFnPlt(pfn, fn_suffix=molec)
pfns.append(Plotting2.plotCols(x=radii,y=abuns,yaxis=yaxis,\
filename=pfn,keytags=keytags,\
cfg=cfg_dict,**extra_pars))
if not per_molecule and pfns and pfns[0][-4:] == '.pdf':
pfn = fn_plt if fn_plt else 'abundance_profiles'
pfn = self.setFnPlt(pfn) + '.pdf'
DataIO.joinPdf(old=pfns, new=pfn)
print '** Plots can be found at:'
print pfn
print '***********************************'
else:
print '** Plots can be found at:'
print '\n'.join(pfns)
print '***********************************'
def plotTempSpecies(self,star_grid=[],models=[],include_total=1,\
power=[1],fn_plt='',cfg=''):
"""
Plotting the temperature stratification of the dust for the species
separately, per model.
@keyword star_grid: parameter sets, if [], the parameter
sets are determined from the model ids
(default: [])
@type star_grid: list[Star()]
@keyword models: The model_ids, if [], the parameter sets are expected
in star_grid
(default: [])
@type models: list[string]
@keyword include_total: Include the sum of all temperature profiles as
well for comparison.
(default: 0)
@type include_total: bool
@keyword power: A list of values for s in below formula. If [] no power
law is included. Power law parameters are taken from
star_grid[0].
See Thesis p32, where power is s in
T(r) = T_eff*(2*r/R_STAR)**(-2/(4+s)).
(default: [1])
@type power: list
@keyword fn_plt: A plot filename for the tiled plot.
(default: '')
@type fn_plt: string
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
"""
print '***********************************'
print '** Starting to plot dust temperature for separate species.'
if not star_grid and not models:
print 'Input is undefined. Aborting.'
return
elif not star_grid and models:
star_grid = self.makeMCMaxStars(models=models, id_type='MCMax')
raise IOError('Reading dust species temperatures from a model id'+\
' list only, not yet implemented.')
#- Requires star.dust_list and T_CONTACT to be taken from the log file.
#- It's possible, but needs some programming
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('power'):
power = cfg_dict['power']
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = os.path.join(self.pplot, 'Td_species')
plot_filenames = []
for star in star_grid:
if not int(star['T_CONTACT']):
rads = [
star.getDustRad(species=species)
for species in star.getDustList()
]
temps = [
star.getDustTemperature(species=species)
for species in star.getDustList()
]
rads = [
r[t <= star['T_DES_%s' % sp]]
for r, t, sp in zip(rads, temps, star.getDustList())
]
temps = [
t[t <= star['T_DES_%s' % sp]]
for t, sp in zip(temps, star.getDustList())
]
keytags = list(star.getDustList())
else:
include_total = 1
print 'Thermal contact is on. All dust species share the ' + \
'same temperature profile. Vertical lines indicate ' + \
'inner radii of dust species.'
rads, temps, keytags = [], [], []
if include_total:
rad = star.getDustRad()
temp, key = star.getDustTemperature(add_key=1)
rads.append(rad[rad>star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']])
temps.append(temp[rad>star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']])
keytags.append(key)
#-- Add power laws if requested
for s in power:
rad = star_grid[0].getDustRad(unit='rstar')
tstar = star_grid[0]['T_STAR']
temp,key = Profiler.dustTemperaturePowerLaw(rad=rad,add_key=1,\
tstar=tstar,s=s)
rads.append(rad)
temps.append(temp)
keytags.append(key)
filename = '_'.join([fn_plt, star['LAST_MCMAX_MODEL']])
plot_filenames.append(Plotting2.plotCols(x=rads,y=temps,\
cfg=cfg_dict,filename=filename,xaxis='$r$ (cm)',\
yaxis='$T_\mathrm{d}$ (K)',keytags=keytags,\
xmax=star['R_OUTER_DUST']*star.Rsun*star['R_STAR'],\
xmin=star['R_STAR']*star.Rsun,fontsize_axis=26,\
xlogscale=1,ylogscale=1,fontsize_key=16,\
figsize=(12.5,8),transparent=0,linewidth=3,\
fontsize_ticklabels=26,\
vert_lines=[star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']]))
if len(plot_filenames) != len(star_grid):
print 'At least one of the models does not yet have a MCMax model.'
if plot_filenames[0][-4:] == '.pdf':
new_filename = fn_plt + '.pdf'
DataIO.joinPdf(old=plot_filenames, new=new_filename)
print '** Your plots can be found at:'
print new_filename
print '***********************************'
else:
print '** Plots can be found at:'
print '\n'.join(plot_filenames)
print '***********************************'
def plotSed(self,star_grid=[],cfg='',iterative=0,no_models=0,\
fn_add_star=0):
"""
Creating an SED 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 iterative: add an extra suffix to the filename for each
iteratively calculated model, with this number
giving the model muber (index in star_grid),
0 if not used.
(default: 0)
@type iterative: int
@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 self.sed is None:
print 'No dsed given in Path.dat. Cannot plot SED. Aborting...'
return
print '***********************************'
print '** Creating SED 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 = ''
data_labels = dict([(dt,(n,ls))
for n,dt,ls in zip(DataIO.getInputData(path=cc.path.usr,\
keyword='PLOT_NAMES',\
filename='Sed.dat',\
remove_underscore=1),\
DataIO.getInputData(path=cc.path.usr,\
keyword='DATA_TYPES',\
filename='Sed.dat'),\
DataIO.getInputData(path=cc.path.usr,\
keyword='LINE_TYPES',\
filename='Sed.dat'))])
#- filename settings and copying inputfiles to plot output folder
if not fn_plt:
fn_plt = os.path.join(self.pplot, 'SED_%s' % self.star_name)
if fn_add_star:
fn_plt = '_'.join([fn_plt, self.star_name])
if iterative:
fn_plt = fn_plt + '_iterative_%i' % iterative
if self.inputfilename <> None:
ipfn = os.path.split(self.inputfilename)[1]
subprocess.call(['cp ' + self.inputfilename + ' ' + \
os.path.join(self.pplot,ipfn)],\
shell=True)
plot_title = 'SED %s' % self.star_name_plots
#- prepare and collect data, keytags and line types
keytags = []
data_x = []
data_y = []
data_err = []
line_types = []
for (dt, fn), tdata in sorted([
dset for dset in self.sed.data.items()
if 'PHOT' not in dset[0][0].upper()
]):
keytags.append(data_labels[dt][0])
data_x.append(tdata[0])
data_y.append(tdata[1])
#data_err.append(tdata[2])
#-- For now, no error bars for spectra.
data_err.append(None)
line_types.append(data_labels[dt][1])
for (dt, fn), (w, f, err) in sorted([
dset for dset in self.sed.data.items()
if 'PHOT' in dset[0][0].upper()
]):
keytags.append(data_labels[dt][0])
data_x.append(w)
data_y.append(f)
data_err.append(err)
line_types.append(data_labels[dt][1])
#- Collect model data as well as keytags and set line types
model_ids_mcm = [
s['LAST_MCMAX_MODEL'] for s in star_grid if s['LAST_MCMAX_MODEL']
]
#- Only if the model_ids list is not empty, MCMax models are available
#- Otherwise the ray tracing keyword is unnecessary.
if no_models:
model_ids_mcm = []
if model_ids_mcm:
rt_sed = star_grid[0]['RT_SED']
for model_id in model_ids_mcm:
dpath = os.path.join(cc.path.mout, 'models', model_id)
w, f = MCMax.readModelSpectrum(dpath, rt_sed)
data_x.append(w)
data_y.append(f)
data_err.append(None)
keytags.append(model_id.replace('_', '\_'))
line_types += [0] * len(star_grid)
keytags = [tag.replace('#', '') for tag in keytags]
extra_pars = dict()
try:
extra_pars['ymax'] = 1.3 * max([max(dy) for dy in data_y])
except ValueError:
pass
try:
extra_pars['ymin'] = 0.5 * min([min(dy) for dy in data_y])
except ValueError:
pass
filename = Plotting2.plotCols(x=data_x,y=data_y,yerr=data_err,\
filename=fn_plt,\
figsize=(20,10),number_subplots=1,\
plot_title=plot_title,fontsize_axis=20,\
keytags=keytags,fontsize_title=24,\
linewidth=3,key_location=(0.0,0.75),\
xlogscale=1,transparent=0,cfg=cfg_dict,\
line_types=line_types,ylogscale=0,\
fontsize_ticklabels=20,fontsize_key=18,\
xmin=2,xmax=200,extension='.pdf',\
**extra_pars)
print '** Your SED plots can be found at:'
print filename
print '***********************************'
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 plotTemp(self, star_grid=[], models=[], power=[1], fn_plt='', cfg=''):
"""
Plotting the temperature stratification of the dust.
All models are shown in one plot.
@keyword star_grid: parameter sets, if [], the parameter
sets are determined from the model ids
(default: [])
@type star_grid: list[Star()]
@keyword models: The model_ids, if [], the parameter sets are expected
in star_grid
(default: [])
@type models: list[string]
@keyword power: A list of values for s in below formula. If [] no power
law is included. Power law parameters are taken from
star_grid[0].
See Thesis p32, where power is s in
T(r) = T_eff*(2*r/R_STAR)**(-2/(4+s)).
(default: [1])
@type power: list
@keyword fn_plt: A plot filename for the tiled plot.
(default: '')
@type fn_plt: string
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
"""
print '***********************************'
print '** Starting to plot dust temperature stratification.'
if not star_grid and not models:
print 'Input is undefined. Aborting.'
return
elif not star_grid and models:
star_grid = self.makeMCMaxStars(models=models)
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('power'):
power = cfg_dict['power']
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = os.path.join(self.pplot, 'Td_avg')
rads = []
temps = []
keytags = []
for star in star_grid:
rad = star.getDustRad()
temp, key = star.getDustTemperature(add_key=1)
rads.append(rad)
temps.append(temp)
keytags.append(key)
#-- Add power laws if requested
for s in power:
rad = star_grid[0].getDustRad(unit='rstar')
tstar = star_grid[0]['T_STAR']
temp,key = Profiler.dustTemperaturePowerLaw(rad=rad,add_key=1,\
tstar=tstar,s=s)
rads.append(rad)
temps.append(temp)
keytags.append(key)
title = 'Average Dust Temperature Stratification for %s'\
%(self.star_name_plots)
filename = Plotting2.plotCols(x=rads,y=temps,filename=fn_plt,\
yaxis='$T_\mathrm{d}$ (K)',\
plot_title=title,xaxis='$R$ (cm)',\
key_location=(0.05,0.05),cfg=cfg_dict,\
xlogscale=1,ylogscale=1,fontsize_key=20,\
keytags=keytags,fontsize_axis=26,\
figsize=(12.5,8),linewidth=3,\
fontsize_ticklabels=26,)
print '** Your plots can be found at:'
print filename
print '***********************************'
def plotVelocity(self,star_grid=[],models=[],fn_plt='',force_plot=0,cfg=''):
'''
Plot velocity versus radius for every model in star_grid.
@keyword star_grid: List of Star() instances. If default, model ids
have to be given.
(default: [])
@type star_grid: list[Star()]
@keyword models: The model ids, only required if star_grid is []
(default: [])
@type models: list[string]
@keyword fn_plt: A base plot filename. Includes folder. If not, a
default is added
(default: '')
@type fn_plt: string
@keyword force_plot: force a plotting if more than models are requested
(default: 0)
@type force_plot: bool
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
'''
print '***********************************'
print '** Plotting Gas Velocity Profiles'
if not star_grid and models:
star_grid = self.makeStars(models=models)
elif (not models and not star_grid) or (models and star_grid):
print '** Input is undefined or doubly defined. Aborting.'
return
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict.pop('filename')
if len(star_grid) < 20 or force_plot:
valid_sg = [star for star in star_grid
if star['LAST_CHEMISTRY_MODEL']]
folders = [os.path.join(cc.path.cout,'models',\
star['LAST_CHEMISTRY_MODEL'])+'/' for star in valid_sg]
radii = [CodeIO.getChemistryPhysPar(folder+'csphyspar.out', 'RADIUS') \
for folder in folders]
temps = [CodeIO.getChemistryPhysPar(folder+'csphyspar.out', 'VELOCITY') \
for folder in folders]
if temps:
keytags = [star['LAST_CHEMISTRY_MODEL'].replace('_','\_')
for star in valid_sg]
#-- Set filenames
pfn = fn_plt if fn_plt else 'velocity_profiles'
pfn = self.setFnPlt(pfn)
#-- Run the two plots
keys_cm = ['Model %i'%(i+1)
for i in xrange(len(star_grid))]
pfn = Plotting2.plotCols(x=radii,y=temps,cfg=cfg_dict,\
filename=pfn,xaxis='$r$ (cm)',\
yaxis=r'$v$ (km s$^{-1}$)',\
figsize=(12.5,8),fontsize_ticklabels=26,\
key_location=(0.05,0.05),xlogscale=1,ylogscale=1,\
keytags=keys_cm,fontsize_axis=26,fontsize_key=26)
print '** Plots can be found at:'
print pfn
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 plotOpacities(self,star_grid=[],scaling=1,species=['AMC'],\
cfg='',index=0,*args,**kwargs):
"""
Plotting wavelength dependent mass extinction coefficients
(ie opacities).
If based on star_grid or modelslist, they are scaled with abundances if
wanted.
If no model info is given, the input opacities are plotted.
Args and kwargs can be given straight to the plot command.
@keyword star_grid: The input Star() models. If default, the MCMax
input opacities are plotted.
(default: [])
@type star_grid: list(Star())
@keyword scaling: allow species abundance scaling of opacities
(default: 1)
@type scaling: bool
@keyword species: If no star_grid or model list are given, this gives
the species requested to be plotted from Dust.dat
(default: ['AMC'])
@type species: list(string)
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword index: The index of the kappas in the .opacity/.particle file.
0: extinction, 1: absorption, 2: scattering
(default: 0)
@type index: int
"""
print '***********************************'
print '** Starting to plot dust opacities.'
#-- Set the filename
cfg_dict = Plotting2.readCfg(cfg)
ppars = dict()
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
elif kwargs.has_key('filename'):
fn_plt = kwargs['filename']
del kwargs['filename']
elif not star_grid:
fn_plt = os.path.join(cc.path.mopac,\
'dust_opacities_%s'%'_'.join(species))
else:
fn_plt = os.path.join(self.pplot,'opacities_species')
#-- Set some plot parameters
ppars['xaxis'] = '$\lambda$ ($\mu \mathrm{m}$)'
ppars['yaxis'] = '$\kappa_\lambda$ ($\mathrm{cm}^2\mathrm{/g}$)'
ppars['fontsize_key'] = 20
ppars['xlogscale'] = 1
ppars['ylogscale'] = 1
ppars['key_location'] = (0.05,0.05)
ppars.update(kwargs)
ppars.update(cfg_dict)
#-- Check if raw opacities or modeling results are requested
if not star_grid:
kr = KappaReader.KappaReader()
wl_list = [kr.getKappas(sp)[0] for sp in species]
q_list = [kr.getKappas(sp)[1] for sp in species]
fn_plt = Plotting2.plotCols(x=wl_list,y=q_list,filename=fn_plt,\
plot_title = 'Dust Opacities',\
keytags=species,*args,**ppars)
print '** Your plot can be found at:'
print fn_plt
else:
fns = []
for star in star_grid:
try:
wave,opacities = star.readKappas()
except IOError:
continue
opacities = [(opacities[i]+opacities[i+len(star.getDustList())])
for i,species in enumerate(star.getDustList())]
if scaling:
opacities = [opa*star['A_%s'%sp]
for opa,sp in zip(opacities,species)]
fn_mplt = '_'.join(fn_plt,star['LAST_MCMAX_MODEL'])
title = 'Dust Opacities in %s (%s)' \
%(self.star_name_plots,\
star['LAST_MCMAX_MODEL'].replace('_','\_'))
keys = ['%s with $A$ = %s and $T_{des} = %i$ K'\
%(sp,str(star['A_%s'%sp]),int(star['T_DES_%s'%sp]))
for sp in star.getDustList()]
fns.append(Plotting2.plotCols(x=wave,y=opacities,keytags=keys,\
plot_title=title,\
filename=fn_mplt,*args,**ppars))
if len(fns) != len(star_grid):
print 'At least one of the models requested does not yet ' + \
'have a MCMax model.'
print '** Your plots can be found at:'
if fns[-1][-4] == '.pdf':
fn_plt = fn_plt+'.pdf'
DataIO.joinPdf(old=fns,new=fn_plt)
print fn_plt
else:
print '\n'.join(fns)
print '***********************************'
def plotAbundanceProfiles(self,star_grid=[],models=[],force_plot=0,cfg='',\
fn_plt='',molecules=[],per_molecule=0,frac=1):
'''
Plot abundance profiles for all molecules in every model.
@keyword star_grid: List of Star() instances. If default, model ids
have to be given.
(default: [])
@type star_grid: list[Star()]
@keyword models: The model ids, only required if star_grid is []
(default: [])
@type models: list[string]
@keyword force_plot: force a plotting if more than models are requested
(default: 0)
@type force_plot: bool
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
@keyword fn_plt: A base plot filename. Includes folder. If not, a
default is added
(default: '')
@type fn_plt: string
@keyword molecules: Molecules to be plotted.
(default: [])
@type molecules: bool
@keyword per_molecule: Plot one molecule for all models in one figure.
(default: 0)
@type per_molecule: bool
@keyword per_model: Plot all molecules for one model in one figure.
(default: 0)
@type per_model: bool
@keyword frac: Plot the fractional abundances. If not frac, plot number
densities.
(default: 1)
'''
print '***********************************'
print '** Plotting Abundance Profiles'
if not star_grid and models:
star_grid = self.makeStars(models=models)
elif (not models and not star_grid) or (models and star_grid):
print '** Input is undefined or doubly defined. Aborting.'
return
pfns = []
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict.pop('filename')
if cfg_dict.has_key('molecules'):
molecules = cfg_dict.pop('molecules')
if cfg_dict.has_key('per_molecule'):
per_molecule = cfg_dict['per_molecule']
if cfg_dict.has_key('per_model'):
per_model = cfg_dict['per_model']
#-- Some general plot settings
extra_pars = dict()
extra_pars['ymin'] = 1e-9
extra_pars['ymax'] = 1e-3
extra_pars['ylogscale'] = 1
extra_pars['xlogscale'] = 1
extra_pars['figsize'] = (12.5,8.5)
extra_pars['xaxis'] = 'cm'
#-- Dict to keep track of all data
ddata = dict()
for istar,star in enumerate(star_grid):
if not star['LAST_CHEMISTRY_MODEL']: continue
ddata[istar] = dict()
folder = os.path.join(cc.path.cout,'models',\
star['LAST_CHEMISTRY_MODEL'])+'/'
ddata[istar]['rad'] = CodeIO.getChemistryPhysPar(folder+\
'csphyspar.out', 'RADIUS')
ddata[istar]['id'] = star['LAST_CHEMISTRY_MODEL']
if frac:
species = CodeIO.getChemistryAbundances(folder+'csfrac.out')
else:
species = CodeIO.getChemistryAbundances(folder+'csnum.out')
for molec in molecules:
ddata[istar][molec] = species[molec]
if not per_molecule:
#-- Collect all data
radii = [ddata[istar]['rad']]*len(molecules)
abuns = [ddata[istar][molec] for molec in molecules]
keytags = molecules
#ids = star['LAST_CHEMISTRY_MODEL']
ids = ddata[istar]['id']
#-- Set the yaxis tag
yaxis = '$n_\mathrm{molec}/n_{\mathrm{H}_2}$'
#-- Set filename
pfn = fn_plt if fn_plt else 'abundance_profiles'
suff = '_'.join(list(set(ids)))
pfn = self.setFnPlt(pfn,fn_suffix=suff)
pfns.append(Plotting2.plotCols(x=radii,y=abuns,cfg=cfg_dict,\
filename=pfn,keytags=keytags,\
plot_title=ids.replace('_','\_'),\
yaxis=yaxis,**extra_pars))
if per_molecule:
#-- Collect all data
#molecs = list(set([molec for istar in ddata.keys()
#for molec in ddata[istar].keys()]))
for molec in molecules:
#-- Collect data
radii = [dstar['rad']
for istar,dstar in ddata.items()]
abuns = [dstar[molec]
for istar,dstar in ddata.items()]
keytags = [dstar['id'].replace('_','\_')
for istar,dstar in ddata.items()]
#-- Set the y axis tag
#strmolec = ddata[0][molec]['key']
yaxis = '$n_\mathrm{%s}/n_{\mathrm{H}_2}$'%str(molec)
#-- Make filename
pfn = fn_plt if fn_plt else 'abundance_profiles'
pfn = self.setFnPlt(pfn,fn_suffix=molec)
pfns.append(Plotting2.plotCols(x=radii,y=abuns,yaxis=yaxis,\
filename=pfn,keytags=keytags,\
cfg=cfg_dict,**extra_pars))
if not per_molecule and pfns and pfns[0][-4:] == '.pdf':
pfn = fn_plt if fn_plt else 'abundance_profiles'
pfn = self.setFnPlt(pfn) + '.pdf'
DataIO.joinPdf(old=pfns,new=pfn)
print '** Plots can be found at:'
print pfn
print '***********************************'
else:
print '** Plots can be found at:'
print '\n'.join(pfns)
print '***********************************'
def plotSed(self,star_grid=[],cfg='',iterative=0,no_models=0,\
fn_add_star=0):
"""
Creating an SED 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 iterative: add an extra suffix to the filename for each
iteratively calculated model, with this number
giving the model muber (index in star_grid),
0 if not used.
(default: 0)
@type iterative: int
@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 self.sed is None:
print 'No dsed given in Path.dat. Cannot plot SED. Aborting...'
return
print '***********************************'
print '** Creating SED 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 = ''
data_labels = dict([(dt,(n,ls))
for n,dt,ls in zip(DataIO.getInputData(path=cc.path.usr,\
keyword='PLOT_NAMES',\
filename='Sed.dat',\
remove_underscore=1),\
DataIO.getInputData(path=cc.path.usr,\
keyword='DATA_TYPES',\
filename='Sed.dat'),\
DataIO.getInputData(path=cc.path.usr,\
keyword='LINE_TYPES',\
filename='Sed.dat'))])
#- filename settings and copying inputfiles to plot output folder
if not fn_plt:
fn_plt = os.path.join(self.pplot,'SED_%s'%self.star_name)
if fn_add_star:
fn_plt = '_'.join([fn_plt,self.star_name])
if iterative:
fn_plt = fn_plt + '_iterative_%i'%iterative
if self.inputfilename <> None:
ipfn = os.path.split(self.inputfilename)[1]
subprocess.call(['cp ' + self.inputfilename + ' ' + \
os.path.join(self.pplot,ipfn)],\
shell=True)
plot_title='SED %s'%self.star_name_plots
#- prepare and collect data, keytags and line types
keytags = []
data_x = []
data_y = []
data_err = []
line_types = []
for (dt,fn),tdata in sorted([dset
for dset in self.sed.data.items()
if 'PHOT' not in dset[0][0].upper()]):
keytags.append(data_labels[dt][0])
data_x.append(tdata[0])
data_y.append(tdata[1])
#data_err.append(tdata[2])
#-- For now, no error bars for spectra.
data_err.append(None)
line_types.append(data_labels[dt][1])
for (dt,fn),(w,f,err) in sorted([dset
for dset in self.sed.data.items()
if 'PHOT' in dset[0][0].upper()]):
keytags.append(data_labels[dt][0])
data_x.append(w)
data_y.append(f)
data_err.append(err)
line_types.append(data_labels[dt][1])
#- Collect model data as well as keytags and set line types
model_ids_mcm = [s['LAST_MCMAX_MODEL']
for s in star_grid
if s['LAST_MCMAX_MODEL']]
#- Only if the model_ids list is not empty, MCMax models are available
#- Otherwise the ray tracing keyword is unnecessary.
if no_models:
model_ids_mcm = []
if model_ids_mcm:
rt_sed = star_grid[0]['RT_SED']
for model_id in model_ids_mcm:
dpath = os.path.join(cc.path.mout,'models',model_id)
w,f = MCMax.readModelSpectrum(dpath,rt_sed)
data_x.append(w)
data_y.append(f)
data_err.append(None)
keytags.append(model_id.replace('_','\_'))
line_types += [0]*len(star_grid)
keytags = [tag.replace('#','') for tag in keytags]
extra_pars = dict()
try:
extra_pars['ymax'] = 1.3*max([max(dy) for dy in data_y])
except ValueError:
pass
try:
extra_pars['ymin'] = 0.5*min([min(dy) for dy in data_y])
except ValueError:
pass
filename = Plotting2.plotCols(x=data_x,y=data_y,yerr=data_err,\
filename=fn_plt,\
figsize=(20,10),number_subplots=1,\
plot_title=plot_title,fontsize_axis=20,\
keytags=keytags,fontsize_title=24,\
linewidth=3,key_location=(0.0,0.75),\
xlogscale=1,transparent=0,cfg=cfg_dict,\
line_types=line_types,ylogscale=0,\
fontsize_ticklabels=20,fontsize_key=18,\
xmin=2,xmax=200,extension='.pdf',\
**extra_pars)
print '** Your SED plots can be found at:'
print filename
print '***********************************'
def plotTempSpecies(self,star_grid=[],models=[],include_total=1,\
power=[1],fn_plt='',cfg=''):
"""
Plotting the temperature stratification of the dust for the species
separately, per model.
@keyword star_grid: parameter sets, if [], the parameter
sets are determined from the model ids
(default: [])
@type star_grid: list[Star()]
@keyword models: The model_ids, if [], the parameter sets are expected
in star_grid
(default: [])
@type models: list[string]
@keyword include_total: Include the sum of all temperature profiles as
well for comparison.
(default: 0)
@type include_total: bool
@keyword power: A list of values for s in below formula. If [] no power
law is included. Power law parameters are taken from
star_grid[0].
See Thesis p32, where power is s in
T(r) = T_eff*(2*r/R_STAR)**(-2/(4+s)).
(default: [1])
@type power: list
@keyword fn_plt: A plot filename for the tiled plot.
(default: '')
@type fn_plt: string
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
"""
print '***********************************'
print '** Starting to plot dust temperature for separate species.'
if not star_grid and not models:
print 'Input is undefined. Aborting.'
return
elif not star_grid and models:
star_grid = self.makeMCMaxStars(models=models,id_type='MCMax')
raise IOError('Reading dust species temperatures from a model id'+\
' list only, not yet implemented.')
#- Requires star.dust_list and T_CONTACT to be taken from the log file.
#- It's possible, but needs some programming
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('power'):
power = cfg_dict['power']
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = os.path.join(self.pplot,'Td_species')
plot_filenames = []
for star in star_grid:
if not int(star['T_CONTACT']):
rads = [star.getDustRad(species=species)
for species in star.getDustList()]
temps = [star.getDustTemperature(species=species)
for species in star.getDustList()]
rads = [r[t<=star['T_DES_%s'%sp]]
for r,t,sp in zip(rads,temps,star.getDustList())]
temps = [t[t<=star['T_DES_%s'%sp]]
for t,sp in zip(temps,star.getDustList())]
keytags = list(star.getDustList())
else:
include_total = 1
print 'Thermal contact is on. All dust species share the ' + \
'same temperature profile. Vertical lines indicate ' + \
'inner radii of dust species.'
rads, temps, keytags = [], [], []
if include_total:
rad = star.getDustRad()
temp, key = star.getDustTemperature(add_key=1)
rads.append(rad[rad>star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']])
temps.append(temp[rad>star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']])
keytags.append(key)
#-- Add power laws if requested
for s in power:
rad = star_grid[0].getDustRad(unit='rstar')
tstar = star_grid[0]['T_STAR']
temp,key = Profiler.dustTemperaturePowerLaw(rad=rad,add_key=1,\
tstar=tstar,s=s)
rads.append(rad)
temps.append(temp)
keytags.append(key)
filename = '_'.join([fn_plt,star['LAST_MCMAX_MODEL']])
plot_filenames.append(Plotting2.plotCols(x=rads,y=temps,\
cfg=cfg_dict,filename=filename,xaxis='$r$ (cm)',\
yaxis='$T_\mathrm{d}$ (K)',keytags=keytags,\
xmax=star['R_OUTER_DUST']*star.Rsun*star['R_STAR'],\
xmin=star['R_STAR']*star.Rsun,fontsize_axis=26,\
xlogscale=1,ylogscale=1,fontsize_key=16,\
figsize=(12.5,8),transparent=0,linewidth=3,\
fontsize_ticklabels=26,\
vert_lines=[star['R_INNER_DUST']\
*star.Rsun*star['R_STAR']]))
if len(plot_filenames) != len(star_grid):
print 'At least one of the models does not yet have a MCMax model.'
if plot_filenames[0][-4:] == '.pdf':
new_filename = fn_plt + '.pdf'
DataIO.joinPdf(old=plot_filenames,new=new_filename)
print '** Your plots can be found at:'
print new_filename
print '***********************************'
else:
print '** Plots can be found at:'
print '\n'.join(plot_filenames)
print '***********************************'
def plotTemp(self,star_grid=[],models=[],power=[1],fn_plt='',cfg=''):
"""
Plotting the temperature stratification of the dust.
All models are shown in one plot.
@keyword star_grid: parameter sets, if [], the parameter
sets are determined from the model ids
(default: [])
@type star_grid: list[Star()]
@keyword models: The model_ids, if [], the parameter sets are expected
in star_grid
(default: [])
@type models: list[string]
@keyword power: A list of values for s in below formula. If [] no power
law is included. Power law parameters are taken from
star_grid[0].
See Thesis p32, where power is s in
T(r) = T_eff*(2*r/R_STAR)**(-2/(4+s)).
(default: [1])
@type power: list
@keyword fn_plt: A plot filename for the tiled plot.
(default: '')
@type fn_plt: string
@keyword cfg: path to the Plotting2.plotCols config file. If default,
the hard-coded default plotting options are used.
(default: '')
@type cfg: string
"""
print '***********************************'
print '** Starting to plot dust temperature stratification.'
if not star_grid and not models:
print 'Input is undefined. Aborting.'
return
elif not star_grid and models:
star_grid = self.makeMCMaxStars(models=models)
cfg_dict = Plotting2.readCfg(cfg)
if cfg_dict.has_key('power'):
power = cfg_dict['power']
if cfg_dict.has_key('filename'):
fn_plt = cfg_dict['filename']
del cfg_dict['filename']
else:
fn_plt = os.path.join(self.pplot,'Td_avg')
rads = []
temps = []
keytags = []
for star in star_grid:
rad = star.getDustRad()
temp,key = star.getDustTemperature(add_key=1)
rads.append(rad)
temps.append(temp)
keytags.append(key)
#-- Add power laws if requested
for s in power:
rad = star_grid[0].getDustRad(unit='rstar')
tstar = star_grid[0]['T_STAR']
temp,key = Profiler.dustTemperaturePowerLaw(rad=rad,add_key=1,\
tstar=tstar,s=s)
rads.append(rad)
temps.append(temp)
keytags.append(key)
title = 'Average Dust Temperature Stratification for %s'\
%(self.star_name_plots)
filename = Plotting2.plotCols(x=rads,y=temps,filename=fn_plt,\
yaxis='$T_\mathrm{d}$ (K)',\
plot_title=title,xaxis='$R$ (cm)',\
key_location=(0.05,0.05),cfg=cfg_dict,\
xlogscale=1,ylogscale=1,fontsize_key=20,\
keytags=keytags,fontsize_axis=26,\
figsize=(12.5,8),linewidth=3,\
fontsize_ticklabels=26,)
print '** Your plots can be found at:'
print filename
print '***********************************'
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 '***********************************'
