def plot_2comp(tem1=1e4, tem2=1e4, dens1=3e2, dens2=5e5, mass1=1, mass2=5e-4):
# List of diagnostics used to analyze the region
diags = pn.Diagnostics()
diags.addDiag([
'[NI] 5198/5200', '[NII] 5755/6548', '[OII] 3726/3729',
'[OII] 3727+/7325+', '[OIII] 4363/5007', '[ArIII] 5192/7136',
'[ArIII] 5192/7300+', '[ArIV] 4740/4711', '[ArIV] 7230+/4720+',
'[SII] 6731/6716', '[SII] 4072+/6720+', '[SIII] 6312/9069',
'[ClIII] 5538/5518'
])
"""
for diag in pn.diags_dict:
if diag[0:7] != '[FeIII]':
diags.addDiag(diag)
print('Adding', diag)
diags.addClabel('[SIII] 6312/9069', '[SIII]A')
diags.addClabel('[OIII] 4363/5007', '[OIII]A')
"""
# Define all the ions that are involved in the diagnostics
adict = diags.atomDict
pn.log_.message('Atoms built')
obs = pn.Observation(corrected=True)
for atom in adict:
# Computes all the intensities of all the lines of all the ions considered
for line in pn.LINE_LABEL_LIST[atom]:
if line[-1] == 'm':
wavelength = float(line[:-1]) * 1e4
else:
wavelength = float(line[:-1])
elem, spec = parseAtom(atom)
intens1 = adict[atom].getEmissivity(
tem1, dens1, wave=wavelength) * dens1 * mass1
intens2 = adict[atom].getEmissivity(
tem2, dens2, wave=wavelength) * dens2 * mass2
obs.addLine(
pn.EmissionLine(
elem,
spec,
wavelength,
obsIntens=[intens1, intens2, intens1 + intens2],
obsError=[0.0, 0.0, 0.0]))
pn.log_.message('Virtual observations computed')
emisgrids = pn.getEmisGridDict(atomDict=adict)
pn.log_.message('EmisGrids available')
# Produce a diagnostic plot for each of the two regions and another one for the
# (misanalyzed) overall region
f, axes = plt.subplots(2, 2)
diags.plot(emisgrids, obs, i_obs=0, ax=axes[0, 0])
diags.plot(emisgrids, obs, i_obs=1, ax=axes[0, 1])
diags.plot(emisgrids, obs, i_obs=2, ax=axes[1, 0])
def p1():
# Set restore to True if the emission maps have already been generated, False otherwise
### General settings
# Setting verbosity level. Enter pn.my_logging? for details
pn.log_.level = 3
# Adopt an extinction law
#extinction_law = 'CCM 89'
# Define the data file
obs_data = 'IC2165.dat'
### Read observational data
# define an Observation object and assign it to name 'obs'
obs = pn.Observation()
# fill obs with data read from file obs_data, with lines varying across rows and a default percent error on line intensities
obs.readData(obs_data,
fileFormat='lines_in_rows',
err_default=0.05,
corrected=True)
### Include the diagnostics of interest
# instantiate the Diagnostics class
diags = pn.Diagnostics()
# include in diags the relevant line ratios
diags.getAllDiagLabels()
diags.getAllDiags()
diags.addDiag([
'[NI] 5198/5200',
'[NII] 5755/6584',
'[OI] 5579/6302',
'[OII] 3726/3729',
'[OII] 3727+/7325+',
'[OIII] 4363/5007+',
'[SII] 6731/6716',
'[SII] 4072+/6720+',
'[SIII] 6312/9069',
'[ArIII] 5192/7136',
'[ArIV] 4740/4711',
'[ClIII] 5538/5518',
])
diags.addDiag('[ClIV] 5323/7531',
('Cl4', 'L(5323)/L(7531)', 'RMS([E(7531),E(5323)])'))
# Create the emission maps to be compared to the observation data
# To see the default parameters, do pn.getEmisGridDict? in ipython
# The files go to ~/.pypics
emisgrids = pn.getEmisGridDict(atomDict=diags.atomDict, den_max=1e6)
### Plot
plt.figure(1)
# Create the contour plot as the intersection of tem-den emission maps with dereddened line ratios
diags.plot(emisgrids, obs)
# Put the title
plt.title('IC 2165 Optical diagnostics')
# Display the plot
plt.show()
plt.savefig('IC2165-diag-opt.pdf')
def plot_2comp(tem1=1e4, tem2=1e4, dens1=3e2, dens2=5e5, mass1=1, mass2=5e-4):
# List of diagnostics used to analyze the region
diags = pn.Diagnostics()
for diag in pn.diags_dict:
if diag[0:7] != '[FeIII]':
diags.addDiag(diag)
diags.addClabel('[SIII] 6312/9069', '[SIII]A')
diags.addClabel('[OIII] 4363/5007', '[OIII]A')
# Define all the ions that are involved in the diagnostics
all_atoms = diags.atomDict
pn.log_.message('Atoms built')
obs = pn.Observation(corrected=True)
for atom in all_atoms:
# Computes all the intensities of all the lines of all the ions considered
for wavelength in all_atoms[atom].lineList:
elem, spec = parseAtom(atom)
intens1 = all_atoms[atom].getEmissivity(
tem1, dens1, wave=wavelength) * dens1 * mass1
intens2 = all_atoms[atom].getEmissivity(
tem2, dens2, wave=wavelength) * dens2 * mass2
obs.addLine(
pn.EmissionLine(
elem,
spec,
wavelength,
obsIntens=[intens1, intens2, intens1 + intens2],
obsError=[0.0, 0.0, 0.0]))
pn.log_.message('Virtual observations computed')
emisgrids = pn.getEmisGridDict(atomDict=all_atoms)
pn.log_.message('EmisGrids available')
# Produce a diagnostic plot for each of the two regions and another one for the (misanalyzed) overall region
plt.subplot(2, 2, 1)
diags.plot(emisgrids, obs, i_obs=0)
plt.subplot(2, 2, 2)
diags.plot(emisgrids, obs, i_obs=1)
plt.subplot(2, 1, 2)
pn.log_.level = 3
diags.plot(emisgrids, obs, i_obs=2)
# ionic abundance (intensity, temperature, density, transition)
O2.getIonAbundance(100, 1.5e4, 100., wave=3727)
# printout as in old nebular
O2.printIonic(
) # only prints transitions and corresponding wavelengths. Useful for a quick glance at the atom.
O2.printIonic(tem=10000, den=100) # also prints line emissivities
O2.printIonic(tem=10000, den=100, printA=True, printPop=True,
printCrit=True) # also prints populations and critical densities
# Compute Hb emissivity at T=10000K
pn.getRecEmissivity(10000, 1e2, 4, 2, atom='H1')
# simultaneously compute temperature and density from pairs of line ratios
# First of all, a Diagnostics object must be created and initialized with the relevant diagnostics.
diags = pn.Diagnostics() # this creates the object
diags.getAllDiags() # see what Diagnostics exist
tem, den = diags.getCrossTemDen('[NII] 5755/6548',
'[SII] 6731/6716',
50,
1.0,
guess_tem=10000,
tol_tem=1.,
tol_den=1.,
max_iter=5)
#TO BE CONTINUED FROM HERE
print(tem, den)
#######################################################################
# HANDLING OBSERVATIONS
import os
### General settings
# Setting verbosity level. Enter pn.log_? for details
pn.log_.level = 2
# Set to True if the emission maps have already been generated
restore = True
# Adopt IRAF atomic data set
pn.atomicData.setDataFileDict('IRAF_09')
# Define where emission maps are to be stored (restore = False) or read from (restore = True)
pypic_path = './pypics/'
# Adopt an extinction law
extinction_law = 'CCM 89'
# Instantiate the Diagnostics class
diags = pn.Diagnostics()
# Include in diags the relevant line ratios
diags.addDiag([
'[NI] 5198/5200',
'[NII] 5755/6548',
'[OIII] 4363/5007',
'[ArIII] 5192/7136',
'[ArIII] 5192/7300+',
'[ArIV] 4740/4711',
'[ArIV] 7230+/4720+',
'[SII] 6731/6716',
'[NeIII] 15.6m/36.0m',
'[NeV] 14.3m/24.2m'
])
# Create the emission maps to be compared to the observation data (some overkill here)
emisgrids = pn.getEmisGridDict(atom_list=diags.getUniqueAtoms(), den_max=1e6, pypic_path=pypic_path)
def p3(restore=True, fignum=3, pypic_path=pn.config.pypic_path):
# --- pregunta 3 Adding IR lines-------------------------------
# Define where emission maps are to be stored (restore = False) or read from (restore = True)
# Create the directory before running this program
obs_data = 'IC2165_IR.dat'
obs = pn.Observation()
# fill obs with data read from file obs_data, with lines varying across rows and a default percent error on line intensities
obs.readData(obs_data,
fileFormat='lines_in_rows',
err_default=0.05,
corrected=True)
temp = 14000
dens = 10**3.5
# Compute theoretical H5-4/Hbeta ratio from Hummer and Storey
IR2Opt_theo = pn.getRecEmissivity(temp, dens, 5, 4) / pn.getRecEmissivity(
temp, dens, 4, 2)
IR2Opt_obs = obs.getLine(label='H1_4.1m').corrIntens / obs.getLine(
label='H1_4861A').corrIntens
for line in obs.lines:
if line.label[-1] == 'm':
line.corrIntens *= IR2Opt_theo / IR2Opt_obs
diags = pn.Diagnostics()
diags.addDiag([
'[NI] 5198/5200',
'[NII] 5755/6584',
'[OI] 5579/6302',
'[OII] 3726/3729',
'[OII] 3727+/7325+',
'[OIII] 4363/5007',
'[SII] 6731/6716',
'[SII] 4072+/6720+',
'[SIII] 6312/9069',
'[ArIII] 5192/7136',
'[ArIV] 4740/4711',
'[ClIII] 5538/5518',
'[CIII] 1909/1907',
'[OIII] 1666/5007',
'[NeV] 1575/3426',
'[OIII] 51m/88m',
'[NeIII] 15.6m/36.0m',
'[NeV] 14.3m/24.2m',
'[SIII] 18.7m/33.6m',
'[NeIII] 3869/15.6m',
'[OIII] 5007/88m',
'[ArIII] 7136/9m',
'[SIII] 6312/18.7m',
])
diags.addDiag('[ClIV] 5323/7531',
('Cl4', 'L(5323)/L(7531)', 'RMS([E(7531),E(5323)])'))
# Create the emission maps to be compared to the observation data
# To see the default parameters, do pn.getEmisGridDict? in ipython
emisgrids = pn.getEmisGridDict(atomDict=diags.atomDict,
den_max=1e6,
pypic_path=pypic_path)
### Plot
plt.figure(fignum)
# Create the contour plot as the intersection of tem-den emission maps with dereddened line ratios
diags.plot(emisgrids, obs)
# Put the title
plt.title('IC 2165 Optical + UV + IR diagnostics')
# Display the plot
plt.show()
plt.savefig('IC6165-diag-opt+UV+IR.pdf')
def p1(obs, do_plot=True):
# Change the atomic data:
pn.atomicData.setDataFile('s_iii_coll_TG99.fits')
pn.atomicData.setDataFile('s_iii_atom_MZ82b-HSC95-LL93.fits')
#Instanciate the Diagnostic object
diags = pn.Diagnostics()
# We will use the following diagnostics
diags.addDiag([
'[NII] 5755/6584',
#'[OII] 3727+/7325+',
'[OIII] 4363/5007',
'[SII] 6731/6716',
'[SIII] 6312/9069',
# '[SII] 4072+/6720+',
])
# we can choose to do the diagnostic plots of the whole set of observations
if do_plot:
emisgrids = pn.getEmisGridDict(atomDict=diags.atomDict,
restore=False,
save=False,
n_tem=150,
n_den=150,
tem_min=5000.,
tem_max=20000.,
den_min=10.,
den_max=1e5)
plt.figure(figsize=(30, 30))
for i, obs_name in enumerate(obs.names):
plt.subplot(6, 5, i + 1)
diags.plot(emisgrids, obs, i_obs=i)
plt.title(obs_name)
plt.savefig('diags.pdf')
plt.close()
# Determination of Te and Ne by intersection of 2 diagnostics:
pn.log_.level = 3
temp_O3, dens_S2a = diags.getCrossTemDen('[OIII] 4363/5007',
'[SII] 6731/6716',
obs=obs)
temp_S3, dens_S2b = diags.getCrossTemDen('[SIII] 6312/9069',
'[SII] 6731/6716',
obs=obs)
temp_N2, dens_S2c = diags.getCrossTemDen('[NII] 5755/6584',
'[SII] 6731/6716',
obs=obs)
# temp_S2, dens_S2d = diags.getCrossTemDen('[SII] 4072+/6720+', '[SII] 6731/6716', obs=obs)
pn.log_.level = 1
#In the cases the density is not defined, we choose to set it to 10 and compute new Te
tt = np.isnan(dens_S2a)
dens_S2a[tt] = 10.
O3 = pn.Atom('O', 3)
temp_O3[tt] = O3.getTemDen((obs.getLine(label='O3_4363A').corrIntens /
obs.getLine(label='O3_5007A').corrIntens)[tt],
den=10,
wave1=4363,
wave2=5007)
tt = np.isnan(dens_S2b)
dens_S2b[tt] = 10.
S3 = pn.Atom('S', 3)
temp_S3[tt] = S3.getTemDen((obs.getLine(label='S3_6312A').corrIntens /
obs.getLine(label='S3_9069A').corrIntens)[tt],
den=10,
wave1=6312,
wave2=9069)
tt = np.isnan(dens_S2c)
dens_S2c[tt] = 10.
N2 = pn.Atom('N', 2)
temp_N2[tt] = N2.getTemDen((obs.getLine(label='N2_5755A').corrIntens /
obs.getLine(label='N2_6583A').corrIntens)[tt],
den=10,
wave1=5755,
wave2=6583)
# Here we adopt an average value of the density
mean_dens = (dens_S2a + dens_S2b + dens_S2c) / 3
O2 = pn.Atom('O', 2)
temp_O2 = O2.getTemDen(
(obs.getLine(label='O2_3727A+').corrIntens /
obs.getLine(label='O2_7325A+').corrIntens),
den=mean_dens,
to_eval='(L(3726)+L(3729))/(I(4,2)+I(5,2)+I(4,3)+I(5,3))')
S2 = pn.Atom('S', 2)
temp_S2 = S2.getTemDen((obs.getLine(label='S2_4072A+').corrIntens /
obs.getLine(label='S2_6716A').corrIntens),
den=mean_dens,
to_eval='(L(4076)+L(4069))/L(6716)')
return mean_dens, temp_O2, temp_S2, temp_N2, temp_O3, temp_S3
