# Output Files¶

Different Output files are written, depending on the values in the param.dat file

## Info_<name>.dat¶

Contains all used parameters, and timing information

## Out_<name>.dat¶

It contains nu and K(nu), where nu are the wavenumbers and K(nu) is the full opacity function. When the PFile option is used, then the files contain also the values of T and P.

This files are written by using the option:
• doStoreFullK = 1

## Out_<name>.bin¶

Binary output file, it contains K(nu), where nu are the wavenumbers and K(nu) is the full opacity function. The wavenumbers are not contained in the output files, and have to be calculated manually by using the wavenumber resolution, and file line index. The opacities are stored in a single precision floating point binary format.

This files are written by using the option:
• doStoreFullK = 2

## Convert Out_<name>.dat to Out_<name>.bin files¶

Output files in the text format *.dat can be converted into binary *.bin files with the script DATtoBIN.py in the /tools directory.

Use for example python3 DATtoBIN.py -n Out_i to convert the file Out_i.dat to Out_i.bin.

## Out_<name>_bin.dat¶

It contains the values of y and K(y) per bin. y goes from 0 to 1. K(y) is the per bin sorted opacity function. The bins are separated by two blank lines, starting with the bin with the lowest wavenumber and ending with the bin with the highest wavenumber.

When doResampling is set to one, then this file contains the sorted opacity functions, recomputed from the Chebyshev coefficients. When the PFile option is used, then the files contain also the values of T, P and point index.

When the OutputEdgesFile option is used, then the file contains not all points in y, but the averaged values between the edges, given in the OutputEdgesFile.

When doStoreSK is set to 2, then the bins are stored in different files with names Out_<name>_bin< bin index>.dat .

## Out_<name>_cbin.dat¶

It contains the Chebyshev coefficients of the per bins sorted natural logarithm of the opacity functions in the format

kmin_i ystart_i C0_i C1_i ... C(nC - 1)_i, where i refers to the bin index, and C are the Chebyshev coefficients.

kmin is the minimal value of K(y), used e.g. in holes in the opacity funtion.

ystart is the position in y when the value ofK(y) starts to be larger than kmin.

K(y) can be recomputed as

K(y) = sum_(0 <= j < nC) (C[j] * T[j](yy)),

where T(y) are the Chebyshev polynomials and yy = (2.0 * y - 1.0 - ystart) / (1.0 - ystart), for y in the range [ystart, 1]. The bins are separated with a blank line, starting with the bin with the lowest wavenumber and ending with the bin with the highest wavenumber. When the PFile option is used, then the files contains also the values of T and P. When doResampling is set to 2, then the bins are stored in different files with names Out_<name>_cbin< bin index>.dat .

The following python script can be used to reconstruct the per bin sorted opacity function from the Chebyshev coefficients:

import numpy as np
from numpy.polynomial.chebyshev import chebval

#change here the name of the file

#change here the bin index and the bin size:
binIndex = 0
binSize = 300

#extract Chebyshev coefficients
c = data_c[binIndex,2:]
#extract starting point in x of opacity function
xs = data_c[binIndex,1]

#rescale x to the standard Chebychev polynomial range [-1:1]
x1 = x * 2.0 - 1.0
k_res = chebval(x1,c,tensor=False)
x2 = x * (1.0 - xs) + xs

#result is in k_res for x values in x2
k_res = np.exp(k_res)


## Out_<name>_tr.dat¶

It contains m and T. m is the column mass, m_i = exp((i - nTr/2) * dTr) T is the Transmission function Int_0^1 exp(-K(y)m) dy When the PFile is used then the files contains also the values of T, P and point index. When doTransmission is set to 2, then the bins are stored in different files with names Out_<name>_tr< bin index>.dat

## Out_<name>_mean.dat¶

When the argument doMean is set to one, this file contains the Planck and Rosseland means. They are computed over the entire range in wavenumbers from numin to numax with spacing dnu.
The first line is the Planck mean: kappa_P = Int_0^infty (kappa * B_nu * dnu) / Int_0^infty (B_nu * dnu).
The second line is the Rosseland mean: kappa_R = (Int_0^infty (kappa^-1 * del(B_nu)/del(T) * dnu) / Int_0^infty ( del(B)/del(T)_nu * dnu))^-1
The third line is the numerical integral Int_0^infty (B_nu * dnu)
The fourth line is the analytic integral Int_0^infty (B_nu * dnu) = sigma * T^4 / pi
The fifth line is the numerical integral Int_0^infty ( del(B)/del(T)_nu * dnu)
The sixth line is the analytic integral Int_0^infty ( del(B)/del(T)_nu * dnu) = 4 * sigma * T^3 / pi

The value of the numerical integrals should converge to the analytic expressions for high resolutions dnu, numin -> 0 and numax -> infinity.