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

```
fe14.popPlot()
```

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

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’].

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
```

```
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,

```
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

```
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.

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

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

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

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

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

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

so the specified ratio is then plotted

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

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

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.]])
```

```
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.

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'])
```

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

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.

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

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

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

```
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

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

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)
```

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: