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()
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 get_OoH_3():
obs = pn.Observation('NGC300.dat', corrected=True, errIsRelative=False)
I = lambda label: obs.getLine(label=label).corrIntens
O3N2 = np.log10((I('O3_5007A') / 100.) / (I('N2_6584A') / (286.)))
OsH = 8.73 - 0.32 * O3N2
return OsH
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)
def get_OoH_2(error=None):
obs = pn.Observation('NGC300.dat', corrected=True, errIsRelative=False)
if error == '-':
for i in np.arange(obs.n_lines):
obs.lines[i].corrIntens *= 1. - obs.lines[i].corrError
if error == '+':
for i in np.arange(obs.n_lines):
obs.lines[i].corrIntens *= 1. + obs.lines[i].corrError
O3 = pn.Atom('O', 3)
r_O3 = O3.getEmissivity(1e4, 1e3, wave=4959) / O3.getEmissivity(
1e4, 1e3, wave=5007)
I = lambda label: obs.getLine(label=label).corrIntens
R23 = (I('O2_3727A+') + I('O3_5007A') * (1 + r_O3)) / 100.
x = np.log10(R23)
OsH = 9.265 - 0.33 * x - 0.202 * x**2 - 0.207 * x**3 - 0.333 * x**4
return OsH
def get_OoH_4(Tlow, Thigh, Ne):
obs = pn.Observation('NGC300.dat', corrected=True, errIsRelative=False)
O3 = pn.Atom('O', 3)
r_O3 = O3.getEmissivity(1e4, 1e3, wave=4959) / O3.getEmissivity(
1e4, 1e3, wave=5007)
t2 = Tlow / 1e4
t3 = Thigh / 1e4
I = lambda label: obs.getLine(label=label).corrIntens
R2 = I('O2_3727A+') / 100
R3 = I('O3_5007A') * (1 + r_O3) / 100.
R = I('O3_4363A') / 100
R23 = R2 + R3
X23 = np.log10(R23)
x2 = 1e-4 * Ne * t2**(-0.5)
Opp = np.log10(R3) + 6.174 + 1.251 / t3 - 0.55 * np.log10(t3)
Op = np.log10(R2) + 5.890 + 1.676 / t2 - 0.40 * np.log10(t2) + np.log10(
1 + 1.35 * x2)
OsH = np.log10(10**Op + 10**Opp)
return OsH
# Object to be analyzed (could be a list with more than one element) ngc_list = ['ngc650_R1'] # Loops over items in list (only one in this example) for ngc in ngc_list: # Define the data file obs_data = ngc +'.dat' # Define title title = ngc.upper() ### Read and deredden 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) # Deredden data with Cardelli's law obs.extinction.law = extinction_law # Apply correction obs.correctData() #Create file path for data if it does not already exist filepath = './'+str(ngc)+'/' if not os.path.exists(filepath): os.mkdir(filepath) ### Plot # Create the contour plot as the intersection of tem-den emission maps with dereddened line ratios
"""
Question 5
"""
pn.atomicData.setDataFileDict('IRAF_09')
ion_ab_dic_IRAF = ex3_1.getIonAb('IC2165_IR.dat', Ne_O2, Ne_Ar4, T_N2, T_O3, printIonAb = True)
elem_abun_IRAF = ex3_1.getElemAb(ion_ab_dic_IRAF, printAb = True)
pn.atomicData.resetDataFileDict()
for icf in elem_abun_UVIR:
print('{0}: {1:.3f} {2:.3f}'.format(icf, np.log10(elem_abun_UVIR[icf])+12, np.log10(elem_abun_IRAF[icf])+12))
#-------------------------------------- ex3_2 ----------------------------------------------
import ex3_2
import atpy
import asciitable
#Read the observations. Notice that in this file the errors are absolutes
obs = pn.Observation('NGC300.dat', corrected=True, errIsRelative=False)
# the galactocentric distance has been also read and the values are in "obsIntens"
Rgal = obs.getLine(label='DIST').obsIntens
# Compute the densities and temperatures
mean_dens, temp_O2, temp_S2, temp_N2, temp_O3, temp_S3 = ex3_2.p1(obs)
# Instantiate a table to write the results in anm ascii file
T = atpy.Table()
# Adding the columns with their names into T
T.add_column('NAME', obs.names)
T.add_column('T_O2', temp_O2)
T.add_column('T_S2', temp_S2)
T.add_column('T_N2', temp_N2)
T.add_column('T_O3', temp_O3)
# writing to a file
T.write('temperatures.ascii', type='ascii',
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')
# Sort lines of an observational dataset in alphabetical order
import pyneb as pn
# Read data
obs_data = 'pne.dat'
obs = pn.Observation(obs_data, fileFormat='lines_in_rows', corrected=True)
out_file = open('out.dat', 'w')
# getSortedLines returns lines in sorted order
for line in obs.getSortedLines():
row = line.label
for item in line.corrIntens:
row += ' {0:10.3e}'.format(item)
out_file.write(row + '\n')
out_file.close()
# plot_Fe3 example script
# Plots diagnostic diagrams from several Fe III lines, using two different sets of atomic data
import numpy as np
import pyneb as pn
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
pn.config.set_noExtrapol(True)
pn.log_.level = 3
# Read the observed intensities, reddening corrected, from a file
obs = pn.Observation('Fe3.dat',
fileFormat='lines_in_rows',
err_default=0.01,
corrected=True)
# Build an atom with default values from Quinet 1996, two kinds of interpolation
pn.atomicData.setDataFile('fe_iii_atom_Q96_J00.dat')
pn.atomicData.setDataFile('fe_iii_coll_Z96.dat')
Fe3_Z = pn.Atom('Fe', 3)
Fe3EM_Q_C = {
'Fe3':
pn.EmisGrid(n_tem=100,
n_den=100,
tem_min=5000.,
tem_max=15000.,
den_min=100.,
den_max=1.e5,
atomObj=Fe3_Z)
}
