chiantipy

Quick Start

This short tutorial will demonstrate some of the capabilities of ChiantiPy and the CHIANTI database. It assumes that you know what the CHIANTI database provides and why you want to use it. The current implementation in Version 0.2 mainly provides access to methods concerned with single ions. An ion such as Fe XIV is specified by the string ‘fe_14’, in the usual CHIANTI notation.

Bring up a Python session (using > Python -i ), or better yet, an IPython session

import chianti.core as ch
from numpy import *

What we will really be interested in are various properties of the Fe XIV emissivities as a function of temperature and density. So, let’s define a numpy array of temperatures

t = 10.**(5.8 + 0.05*arange(21.))

In ChiantiPy, temperatures are currently given in degrees Kelvin and densities as the number electron density per cubic cm. Then, construct fe14 as would be typically done

fe14 = ch.ion('fe_14', temperature=t, eDensity=1.e+9)

note that eDensity is the new keyword for electron density

Level populations

fe14.popPlot()

produces a matplotlib plot window were the population of the top 10 (the default) levels are plotted as a function of temperature.

_images/fe14.popplot.png

If the level populations had not already been calculated, popPlot() would have invoked the populate() method which calculates the level populations and stores them in the Population dictionary, with keys = [‘protonDensity’, ‘population’, ‘temperature’, ‘density’].

A ChiantiPy Convention

Classes and function of ChiantiPy start with lower case letters. Data attached to the instantiation of a class will start with a capital letter. For example,

fe14.populate() creates fe14.Population containing the level population information

fe14.emiss() creates fe14.Emiss containing the line emissivity information

fe14.spectrum() creates fe14.Spectrum contain the line and continuum spectrum information

Emissivities

fe14.emissPlot(wvlRange=[210., 220.])

will plot the emissivities for the top (default = 10) lines in the specified wavelength range. Since there are 21 temperature involved, a single temperature is selected (21/2 = 10). Otherwise,

_images/fe14.emiss.png
fe14.emissPlot(index=2, wvlRange=[210., 220.])

plots the emissivities for a temperature = t[2] = 7.9e+5, in this case. And, by specifying relative = 1, the emissivities will be plotted relative to the strongest line.

fe14.emissList(wvlRange=[200,220], index=10, relative=1)

gives the following terminal output:

using index =    10 specifying temperature =    2.00e+06

------------------------------------------

lvl1  lvl2         lower                       upper                  Wvl(A)   Emissivity     A value  Obs
  1    11              3s2.3p 2P0.5 - 3s2.3d 2D1.5                   211.317    1.000e+00     3.81e+10 Y
  4    27              3s.3p2 4P1.5 - 3s.3p(3P).3d 4P1.5             212.125    5.236e-03     2.21e+10 Y
  3    24              3s.3p2 4P0.5 - 3s.3p(3P).3d 4D0.5             213.196    6.637e-03     4.26e+10 Y
137   261              3s2.4d 2D1.5 - 3s2.5p 2P0.5                   213.388    4.848e-03     1.87e+10 N
  3    23              3s.3p2 4P0.5 - 3s.3p(3P).3d 4D1.5             213.882    1.189e-02     2.97e+10 Y
  5    28              3s.3p2 4P2.5 - 3s.3p(3P).3d 4D2.5             216.579    8.566e-03     2.83e+10 Y
  5    25              3s.3p2 4P2.5 - 3s.3p(3P).3d 4D3.5             216.917    1.223e-02     4.29e+10 Y
  7    32              3s.3p2 2D2.5 - 3s.3p(3P).3d 2F3.5             218.177    2.306e-02     1.70e+10 Y
  4    22              3s.3p2 4P1.5 - 3s.3p(3P).3d 4P2.5             218.572    1.918e-02     2.65e+10 Y
  2    12              3s2.3p 2P1.5 - 3s2.3d 2D2.5                   219.131    2.488e-01     4.27e+10 Y

------------------------------------------

optionally, an output file could also be created by setting the keyword outFile to the name of the desired name

G(n,T) function

fe14.gofnt(wvlRange=[210., 220.],top=3)

brings up a matplotlib plot window which shows the emissivities of the top (strongest) 3 lines in the wavelength region from 210 to 220 Angstroms.

_images/fe14.rel.emiss.png

quickly followed by a dialog where the line(s) of interest can be specified

_images/fe14.gofnt.selector.png

and finally a plot of the G(n,T) function for the specified lines(s).

_images/fe14.gofnt.png

The G(n,T) calculation is stored in the Gofnt dictionary, with keys = [‘gofnt’, ‘temperature’, ‘density’]

Intensity Ratios

fe14.intensityRatio(wvlRange=[210., 225.])

this brings up a plot showing the relative emissivities on the Fe XIV lines

_images/fe14.int.vs.t.png

following by a dialog where you can selector the numerator(s) and denominator(s) of the desired intensity ratio

_images/2.selector.png

so the specified ratio is then plotted

_images/fe14.int.ratio.vs.t.png

if previously, we had done

d = 10.**(6. + 0.1*arange(61))
fe14 = ch.ion('fe_14', temperature = 2.e+6, density = d)

then the plot of relative intensities vs density would appear

_images/fe14.int.vs.d.png

the same numerator/denominator selector dialog would come up and when 2 or more lines are selected, the intensity ratio versus density appears.

_images/fe14.int.ratio.vs.d.png

to obtain ratios of lines widely separated in wavelength, the wvlRanges keyword can be used:

fe12 = ch.ion('fe_12', temperature=t, eDensity=1.e+9
fe12.intensityRatio(wvlRanges=[[190.,200.],[1240.,1250.]])
_images/fe_12_wvlranges_ratio.png

Spectra of a single ion

fe14 = ch.ion('fe_14', temperature = 2.e+6, density = 1.e+9)
wvl = wvl=200. + 0.125*arange(801)
fe14.spectrum(wvl)
plot(wvl, fe14.Spectrum['intensity'])

this will calculate the spectrum of fe_14 over the specified wavelength range and filter it with the default filter which is a gaussian (filters.gaussianR) with a ‘resolving power’ of 1000 which gives a gaussian width of wvl/1000.

_images/fe14.spectrum1.png

other filters available in chianti.filters include a boxcar filter and a gaussian filter where the width can be specified directly

import chianti.filters as chfilters
fe14.spectrum(wvl,filter=(chfilters.gaussian,.04))

calculates the spectrum of fe_14 for a gaussian filter with a width of 0.04 Angstroms.

plot(wvl,fe14.Spectrum['intensity'])
_images/fe14.spectrum2.png

Free-free and free-bound continuum

The module continuum provides the ability to calculate the free-free and free-bound spectrum for a large number of individual ions.

temperature = 2.e+7
c = c h.continuum('fe_25', temperature = temperature)
wvl = 1. + 0.002*arange(4501)
c.freeFree(wvl)
plot(wvl, c.FreeFree['rate'])
c.freeBound(wvl)
plot(wvl, c.FreeBound['rate'])

produces

_images/fe_25_ff_fb_2e7_1_10.png

In the continuum calculations, the specified ion, Fe XXV in this case, is the target ion for the free-free calculation. For the free-bound calculation, specified ion is also the target ion. In this case, the radiative recombination spectrum of Fe XXV recombining to form Fe XXIV is returned.

Spectra of multiple ions and continuum

the spectrum for all ions in the CHIANTI database can also be calculated

temperature = [1.e+6, 2.e+6]
density = 1.e+9
wvl = 200. + 0.05*arange(2001)
emeasure = [1.e+25 ,1.e+25]
s = ch.spectrum(temperature, density, wvl, filter = (chfilters.gaussian,.2), em = emeasure)
subplot(311)
plot(wvl, s.Spectrum['integrated'])
subplot(312)
plot(wvl, s.Spectrum['intensity'][0]*emeasure[0])
subplot(313)
plot(wvl, s.Spectrum['intensity'][1]*emeasure[1])

produces

_images/spectrum_200_300.png

the integrated spectrum is formed by multiplying each spectrum by the value of em (‘as in emission measure’) and summing them. Note that even though a value is specified for em, only the values of s.Spectrum[‘integrated’] have been multiplied by em. Also, the filter is not applied to the continuum. This spectrum was created with CHIANTI database version 7.1 and ChiantiPy version 0.5.2 using the following default values:

for akey in  chianti.data.Defaults:
   print(' %10s - %s'%(akey,chianti.data.Defaults[akey]))

wavelength - angstrom
       gui - False
 ioneqfile - chianti
 abundfile - sun_photospheric_1998_grevesse
      flux - energy

It is also possible to specify a selection of ions by means of the ionList keyword, for example, ionList=[‘fe_11’,’fe_12’,’fe_13’]

temperature = 1.e+7
wvl = 10. + 0.005*arange(2001)
s = ch.spectrum(temperature, density, wvl, filter = (chfilters.gaussian,.015))
plot(wvl, s.Spectrum['intensity'])

produces

_images/spectrum_10_20.png

this includes free-free, free-bound and line radiation. The continuum intensities are also available:

temperature = 2.e+7
density = 1.e+9
wvl = 1. + 0.002*arange(4501)
emeasure = 1.e+25
import chianti.filters as chfilters
s = ch.spectrum(temperature density, wvl, em = emeasure, filter = (chfilters.gaussian,.005))
plot(wvl,s.FreeFree['intensity'])
plot(wvl,s.FreeBound['intensity'])
plot(wvl,s.FreeBound['intensity']+s.FreeFree['intensity'])

produces

_images/continuum_2e7_1_10.png
temperature = 2.e+7
density = 1.e+9
wvl = 1.84 + 0.0001*arange(601)
import chianti.filters as chfilters
s = ch.spectrum(temperature, density ,wvl, filter = (chfilters.gaussian,.0003), doContinuum=0)

produces

_images/fe_25_2e7_1.84_1.90.png

Calculations with the Spectrum module can be time consuming. One way to control the length of time the calculations take is to limit the number of ions with the ionList keyword and to avoid the continuum calculations by setting the doContinuum keyword to 0 or False. Another way to control the length of time the calculations take is with the minAbund keyword. It sets the minimum elemental abundance that an element can have for its spectra to be calculated. The default value is set include all elements. Some usefull values of minAbund are:

minAbund = 1.e-4, will include H, He, C, O, Ne minAbund = 2.e-5 adds N, Mg, Si, S, Fe minAbund = 1.e-6 adds Na, Al, Ar, Ca, Ni

Calculations with multiple processors

Another way to speed up calculations is to use the mspectrum class which uses multiple cores on your local computer. It requires the Python multiprocessing module which is available with Python versions 2.6 and later. mspectrum is called in the same way as spectrum but you can specify the number of cores with the proc keyword. The default is 3 but it will not use more cores than are available on your machine. For example,

s = ch.mspectrum(temperature, density ,wvl, em=emeasure, filter = (chfilters.gaussian,.005), proc=2)

Radiative loss rate

the radiative loss rate can be calculated as a function of temperature and density:

::
temp = 10.**(4.+0.05*arange(81)) rl = ch.radLoss(temp, 1.e+4, minAbund=2.e-5) rl.radLossPlot()

produces:

_images/radloss.png
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