def test_diag_list():
diags = [
'[ArIII] 5192/7136', '[ArIII] 9.0m/21.8m', '[ArIV] 4740/4711',
'[CIII] 1909/1907', '[ClIII] 5538/5518', '[NII] 121m/20.5m',
'[NII] 5755/6584', '[NeIII] 15.6m/36.0m', '[NeIII] 3343/3930+',
'[NeV] 14.3m/24.2m', '[NeV] 2973/3370+', '[OI] 5577/6300+',
'[OI] 63m/147m', '[OII] 3726/3729', '[OII] 3727+/7325+',
'[OIII] 1664+/5007', '[OIII] 5007/88m', '[OIII] 4363/5007',
'[OIV] 1400+/25.9m', '[OIV] 1401/1405', '[SII] 4072+/6720+',
'[SII] 4069/4076', '[SII] 6731/6716', '[SIII] 6312/9069',
'[SIII] 18.7m/33.5m'
]
# Build the 25 subplots
f, axes = plt.subplots(5, 5, figsize=(20, 20))
# loop on the diagnostics
for i, d in enumerate(diags):
# extract from the diagnostic dictionnary
# the expression to be evaluated
to_eval = pn.diags_dict[d][1]
atom = pn.diags_dict[d][0]
# split e.g. obtain 'O', 3 from 'O3'
elem, spec = pn.utils.misc.parseAtom(atom)
# instantiate the EmisGrid object for the given ion
EG = pn.EmisGrid(elem, spec)
# select the axis in which to plot
ax = axes.ravel()[i]
# make the plot
EG.plotContours(to_eval=to_eval, ax=ax)
plt.close()
def plot_all(save=False):
pn.log_.level = 1
AA = pn.getAtomDict(OmegaInterp='Linear')
for diag in pn.diags_dict:
atom, diag_eval, err = pn.diags_dict[diag]
if atom in AA:
plt.figure()
grid = pn.EmisGrid(atomObj=AA[atom])
grid.plotContours(to_eval=diag_eval)
if save:
plt.savefig('{0}_{1}.pdf'.format(atom,
diag_eval.replace('/', '_')))
def plot_all(save=False):
pn.log_.level = 1
AA = pn.getAtomDict(OmegaInterp='Linear')
# Loop over all the diags stored in pn.core.diags.diags_dict
for diag in diags_dict:
atom, diag_eval, err = diags_dict[diag]
# Skip Fe III as they are so many
if (atom in AA) and (atom != 'Fe3'):
print atom
plt.figure()
grid = pn.EmisGrid(atomObj=AA[atom])
grid.plotContours(to_eval=diag_eval)
if save:
plt.savefig('{0}_{1}.pdf'.format(atom,
diag_eval.replace('/', '_')))
def p2(diag):
# get the ion, and diagnostic description from the dictionary:
ion, diag_eval, err = pn.diags_dict[diag]
# split ion into elem and spec, e.g 'O3' into 'O' and 3
elem, spec = parseAtom(ion)
# prepare a new figure
plt.figure()
# create a grid of emissivities
#NB: one can use a pypic file containing all the emissivities, if already made
# in that case set restore_file to the name of the pypic file.
grid = pn.EmisGrid(elem, spec, restore_file=None, OmegaInterp='Linear')
# plot the contours
grid.plotContours(to_eval=diag_eval,
low_level=None,
high_level=None,
n_levels=20,
linestyles='-',
clabels=True,
log_levels=True,
title='{0} {1}'.format(ion, diag_eval))
# save the plot into pdf files
plt.savefig('{0}_{1}.pdf'.format(ion, diag_eval.replace('/', '_')))
# EmisGrid example script
# Shows how to produce, save, restore and plot emission maps
import pyneb as pn
import matplotlib.pyplot as plt
plt.figure()
O3map = pn.EmisGrid('O', 3, n_tem=30, n_den=30) # Instantiate an O III atom and computes the emissivity of all its lines in a 30x30 grid
print "Plot the 4363/5007 ratio map with a 30x30 Ne-Te grid"
O3map.plotImage(wave1=4363, wave2=5007) # Plot
plt.figure()
O3map = pn.EmisGrid('O', 3, n_tem=100, n_den=100) # Increase the grid resolution
print "Increase the grid resolution to 100x100"
O3map.plotImage(wave1=4363, wave2=5007) # Plot
plt.figure()
O3map = pn.EmisGrid('O', 3, n_tem=100, n_den=100, tem_max=1.5e4, den_max=1e5) # Change the axes limits
print "Change the axes limits"
O3map.plotImage(wave1=4363, wave2=5007) # Plot
O3map.save('O3_1k.pypic') # Save for later use
plt.figure()
O3map = pn.EmisGrid(restore_file='O3_1k.pypic') # Recover the map from a previous computation
print "Plot with a different color map"
O3map.plotImage(wave1=4363, wave2=5007, cmap=plt.cm.bone) # Plot with a different color map
plt.figure()
print "Plot the 4363/88m line ratio"
O3map.plotImage(wave1=4363, wave2=884000) # Plot a different line ratio
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)
}
Fe3EM_Q_L = {
'Fe3':
pn.EmisGrid(n_tem=100,
n_den=100,
tem_min=5000.,
tem_max=15000.,
den_min=100.,
den_max=1.e5,
OmegaInterp='Linear',
atomObj=Fe3_Z)
}
# Sample script
# Produces contour plots of OIII and SII line ratios
# Two versions of the SII plot are produced with two different sets of collision strengths
# A colormap and a plot of the ratio of the two SII determinations is also shown
import numpy as np
import matplotlib.pyplot as plt
import pyneb as pn
# Compute emission grids
EM_O3 = pn.EmisGrid('O', 3, n_den=100, n_tem=100)
EM_S2 = pn.EmisGrid('S', 2, n_den=100, n_tem=100)
pn.atomicData.setDataFile('s_ii_coll_RBS96.dat')
EM_S2_GR = pn.EmisGrid('S', 2, n_den=100, n_tem=100)
pn.atomicData.resetDataFileDict()
# Print populations a la ionic
EM_O3.atom.printIonic()
# Contour plots for OIII and the two versions of the SII ratios
EM_O3.plotContours('L(4363)/L(4959)')
plt.show()
EM_S2.plotContours('L(6731)/L(6716)', linestyles='--', low_level= -0.3, high_level=0.15)
plt.show()
EM_S2_GR.plotContours('L(6731)/L(6716)', linestyles=':', low_level= -0.3, high_level=0.15)
plt.show()
# Image of the ratio between the two SII computations
plt.imshow(EM_S2.getGrid(to_eval='L(6731) / L(6716)') /
EM_S2_GR.getGrid(to_eval='L(6731) / L(6716)'))
plt.colorbar()