Requirements
exomol.py and exomol2.py
bs4
requests
sys
os
subprocess
numpy
argparse
math
Kurucz2.py
numpy
math
struct
os
argparse
nist_ELevels2.py and nist_Lines3.py
sys
argparse
csv
requests
nist_partition.py
numpy
sys
argparse
nist_Lines2.py
numpy
struct
math
csv
pandas
argparse
re
vald_request.py
selenium
argparse
vald_download.py
numpy
math
struct
os
argparse
vald.py
numpy
math
struct
os
argparse
Note, when using a computing cluster, all these libraries can be installed locally in the home directory with:
pip3 install --user <package name>
Compilation
HELIOS-K can be compiled with the provided Makefile by typing
make SM=xx
into the terminal, where xx
corresponds to the compute
capability of the installed GPU. For example use make SM=20
for compute capability 2.0, or make SM=35
for 3.5. A table with all compute capabilities
can be found here.
On a computing cluster, eventually the CUDA module must be loaded before compiling the code.
On Windows machines
If using Cygwin on Windows, then HELIOS-K can be compiled the same way
with make SM=xx
. If using the Windows Command Prompt, type
nmake -f MakefileW SM=xx
. Note, that the Windows C++ compiler cl
must be installed, and the compiler path must be loaded in the shell. If
this is not the case, it can be loaded similar to this command:
call "C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\Tools\vsvars32.bat"
, where the exact path and file name must eventually be changed.