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 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 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 '***********************************'
