def plot_sed(self, axes=plt.gca(), nu_or_lambda='nu', color='black',
linewidth=0.5, x_range='normal'):
'''Plot a multi component greybody model.
Parameters
----------
nu_or_lambda :
plot against frequency ``'nu'`` or wavelenght ``'lambda'``
kappa :
The kappa to use. ``'easy'`` or ``'Kruegel'``. Please refer
to :py:func:`functions.astroFunctions.greyBody` for more
information.
xRange : PLEASE ADD DESCRIPTION
linewidth : float
The linewidth of the plotted lines. Default to 0.5.
color : matplotlib conform color
the color of the plotted lines. Default to ``'black'``.
'''
if not self.fit_done:
raise ValueError('Could not plot the data. The data has not been '
'fitted yet.')
sys.exit()
if self.p2 == None:
pass
if x_range == 'LTIR':
# Plot the SED in the range of the determination
# of the L_TIR: 3-1100 micron
xmin = 3e-6# micron
xmax = 1100e-6 # micron
# conversion to frequency in GHz
xmin = const.c/xmax/1e9
xmax = const.c/xmin/1e9
step = 0.1
if x_range == 'normal':
# arbitrary range definition
xmin = 1e-2
xmax = 3e5
step = 0.5
if type(x_range) == list:
xmin = x_range[0]
xmax = x_range[1]
if len(x_range) < 3:
step = 0.1
else:
step = x_range[2]
x = np.arange(xmin,xmax,step)
# multi_component_grey_body gives the summed 'model' and the components
# grey'. 'grey' is a List
if nu_or_lambda == 'nu':
model,grey = astro_functions.multi_component_grey_body(self.p2, x,
'nu',
self.kappa)
if nu_or_lambda=='lambda':
model,grey = astro_functions.multi_component_grey_body(self.p2, x,
'nu',
self.kappa)
y=x.copy()
modelLambda =model.copy()
greyLambda = []
for i in range(len(grey)):
greyLambda += [grey[i].copy()]
for i in range(len(x)):
y[i]=(const.c/(x[len(x)-i-1]*1e9))/1e-6
for i in range(len(model)):
modelLambda[i]=model[len(model)-i-1]
for j in range(len(greyLambda)):
greyLambda[j][i] = grey[j][len(grey[j])-i-1]
x=y
model =modelLambda
grey = greyLambda
plt.loglog(x, model, ls='-', color=color, label='_nolegend_', lw=0.5,
marker='')
linestyles = [':','-.','-']
j=0
for i in grey:
plt.loglog(x, i, color=color, ls=linestyles[j], lw=0.5, marker='')
j+=1
def plot_sed(p2, nu_or_lambda='nu', color='black', kappa='easy',
linewidth=0.5, xRange='normal'):
'''
Plot a multi component greybody model.
:param p2: formatted: p2 = [[t1,t2,t3,...], [N1,N2,N3,...], [beta]]
:param nu_or_lambda: plot against frequency ``'nu'`` or wavelenght
``'lambda'``
:param kappa: The kappa to use. ``'easy'`` or ``'Kruegel'``. Please refer
to :py:func:`functions.astroFunctions.greyBody` for more information.
:param xRange: PLEASE ADD DESCRIPTION
:param linewidth: The linewidth of the plotted lines. Default to 0.5.
:param linewidth: float
:param color: the color of the plotted lines. Default to ``'black'``
:type color: matplotlib conform color.
'''
if xRange == 'LTIR':
# Plot the SED in the range of the determination
# of the L_TIR: 3-1100 micron
xmin = 3e-6# micron
xmax = 4000e-6 # micron
# conversion to frequency in GHz
xmin = const.c/xmax/1e9
xmax = const.c/xmin/1e9
step = 0.1
if xRange == 'normal':
# arbitrary range definition
xmin = 1e-2
xmax = 3e5
step = 0.5
if type(xRange) == list:
xmin = xRange[0]
xmax = xRange[1]
if len(xRange) < 3:
step = 0.1
else:
step = xRange[2]
x = np.arange(xmin,xmax,step)
# multiComponent gives the summed 'model' and the components 'grey'.
# 'grey' is a List
if nu_or_lambda == 'nu':
model,grey = astFunc.multi_component_grey_body(p2,x,'nu',kappa)
if nu_or_lambda=='lambda':
model,grey = astFunc.multi_component_grey_body(p2,x,'nu',kappa)
y=copy(x)
modelLambda =copy(model)
greyLambda = []
for i in range(len(grey)):
greyLambda += [copy(grey[i])]
for i in range(len(x)):
y[i]=(const.c/(x[len(x)-i-1]*1e9))/1e-6
for i in range(len(model)):
modelLambda[i]=model[len(model)-i-1]
for j in range(len(greyLambda)):
greyLambda[j][i] = grey[j][len(grey[j])-i-1]
x=y
model =modelLambda
grey = greyLambda
plt.loglog(x,model,ls='-',color=color,label='_nolegend_',lw=0.5,marker='')
linestyles = [':','-.','-']
j=0
for i in grey:
plt.loglog(x,i,color=color,ls=linestyles[j],lw=0.5,marker='')
j+=1
