Kālī is a software library to model time series data using various stochastic processes such as Continuous-time ARMA (C-ARMA) processes. The name of the library is taken from the Hindu goddess Kālī, who is the goddess of time and change. It also stands for KArma (C-ARMA) LIbrary because the library began as a tool to model C-ARMA light curves. Kālī is written in c++ and is exposed to python using cython and cffi (deprecated).

Version: 1.0.0

Install instructions are provided for Linux & Mac OSX machines. The following OSs have been tested

1. Ubuntu 14.04 LTS Trusty Tahr

2. Red Hat Enterprise Linux Server release 6.5 (Santiago)

3. Mac OS X 10.10.5 Yosemite

4. Fedora 23

5. Mac OS X 10.11.2 El Capitan

If you are working on Mac OSX, please be sure to install the latest XCode. You will need to have Anaconda Python, the Intel C++ Compiler XE, Intel MKL, NLOpt, cython , the future package, py.test, cffi (optional), & Brandon Kelly's carma_pack (optional) installed. At the moment, Anaconda Python, the Intel C++ Compiler XE, Intel MKL, cython & NLOpt are required though the plan is to eventually allow the use of g++ etc... Brandon Kelly's carma_pack is not required but is recommended. cffi is only required if you wish to use the older deprecated cffi interface to the c++ code.

You may encounter the following error when running bash-prompt$ python setup.py build_ext - icpc: error #10001: could not find directory in which g++-x.x resides. This error occurs when the g++ compiler is installed in a non-standard location and can be resolved by setting the environment variable GXX_ROOT to the installation location of the g++ compiler. To determine the installation location of the g++ compiler, execute the command

bash-prompt$ g++ --print-search-dirs

and set GXX_ROOT to the location indicated by the install field from the output of the above command.

1. Anaconda Python

Anaconda Python is a free suite of Python development tools including a Python2/3 interpreter along with most of the common Python scientific computing packages such as numpy, scipy, and matplotlib. Anaconda Python may be obtained from

Download Anaconda Python

This package is written for Python2, though it may eventually get updated to Python3. Download the 64-bit Python2 installer for your platform and follow the provided install instructions. Keep your installation up to date by periodically doing

bash-prompt$ conda update conda to update the package manager.

bash-prompt$ conda update --all to update all the installed packages.

This software has been tested with

1. Python 2.7.11 |Anaconda 2.4.1 (64-bit)

2. Python 2.7.11 |Anaconda 4.0.0 (64-bit)

3. Intel C++ Compiler XE

The Intel C++ compiler is included in the Intel® Parallel Studio XE Composer Edition (and higher editions).

Intel® C++ Compiler Overview

Try & Buy Intel C++ Compiler

Students are eligible for a free license. Greatly discounted pricing is available for academic users. Use of the Intel compiler results in the largest performance gains on Intel CPUs (but not on AMD machines!) To install the Intel compiler, download the Parallel Studio XE edition of your choice by following the instructions at

Student + free Compiler & MKL

Academic Resercher + free MKL

Unpack the tarball as usual and then change to the top-level directory of the un-tarred folder. In the terminal type

<Intel C & C++ Compiler dir>$ sudo install_GUI.sh

to start the install. Add the following line to your .bashrc to setup the necessary environment variables required by the compiler.

source /opt/intel/compilers_and_libraries/linux/bin/compilervars.sh intel64

This software has been tested with

1. Intel® Parallel Studio XE 2016 Cluster Edition Initial Release 16.0.1.111 (icpc version 16.0.0) (gcc version 4.9.0 compatibility)

2. Intel® Parallel Studio XE 2016 Cluster Edition Update 1 16.0.1.150 (icpc version 16.0.1) (gcc version 4.8.0 compatibility)

3. Intel® Parallel Studio XE 2016 Cluster Edition Update 2 16.0.2.180 (icpc version 16.0.2) (gcc version 4.8.0 compatibility)

4. Intel® Parallel Studio XE 2016 Cluster Edition Update 3 16.0.3.210 (icpc version 16.0.3) (gcc version 4.8.0 compatibility)

5. Intel MKL Library

The Intel MKL library is a high performance math library that is used extensively in this package. Since the library can be obtained free of cost by both students as well as academic researchers, there is no plan to replace it with an alternative. Intel MKL may be obtained from

Student + free Compiler & MKL

Academic Resercher + free MKL

This software has been tested with

1. Intel® Math Kernel Library 11.3 11.3

2. Intel® Math Kernel Library 11.3 Update 1 11.3.1

3. Intel® Math Kernel Library 11.3 Update 2 11.3.2

4. Intel® Math Kernel Library 11.3 Update 3 11.3.3

5. NLOpt

NLOpt is a free/open-source non-liner optimization library written by the AbInitio Group at MIT.

Steven G. Johnson, The NLopt nonlinear-optimization package

NLOpt can be downloaded from

Download NLOpt

After un-tarring the tar file, NLOpt should be installed as follows

<nlopt dir>$ ./configure --enable-shared

<nlopt dir>$ make

<nlopt dir>$ sudo make install

Lastly, one must update the list of installed shared libraries by running

bash-prompt$ sudo ldconfig

This software has been tested with

1. NLOpt Version 2.4.2

2. cython

cython is used to wrap the c++ parts of Kālī in Python. Make sure that you have the latest cython build. You can get the most recent version using

bash-prompt$ conda update conda

bash-prompt$ conda update --all

bash-prompt$ conda install cython

This software has been tested with

1. Cython Version 0.23.4

2. Cython Version 0.24

3. future

future is a Python 2 package that makes a number of useful Python 3 modules useable in Python 2. You can get future using

bash-prompt$ conda update conda

bash-prompt$ conda update --all

bash-prompt$ conda install future

This software has been tested with

1. future Version 0.15.2

2. py.test

py.test is used for testing purposes. py.test can be installed into Anaconda using

bash-prompt$ conda update conda

bash-prompt$ conda update --all

bash-prompt$ conda install pytest

This software has been tested with

1. py.test Version 2.9.2

2. cffi

The C Foreign Function Interface cffi is used to make the libcarma.so library calls from Python. Use of cffi is now deprecated. For posterity sake, cffi can be installed into Anaconda using

bash-prompt$ sudo apt-get install libffi6 (On linux only)

bash-prompt$ sudo apt-get install libffi6:i386 (On linux only)

bash-prompt$ sudo apt-get install libffi-dev (On linux only)

bash-prompt$ conda update conda

bash-prompt$ conda update --all

bash-prompt$ conda install cffi

10. carma_pack

Brandon Kelly's carma_pack is a C-ARMA analysis package written in C++ and Python. It may be obtained at

carma_pack

Please install carma_pack using the instructions provided with the package. This library includes a Python script cffi/python/KellyAnalysis.py to use carma_pack with the same interface as the rest of this package. Please read

bash-prompt$ KellyAnalysis --help and the python docstring for usage instructions.

To make Kālī after cloning the repository, simply run

bash-prompt$ source ./bin/setup.sh

followed by

bash-prompt$ python setup.py build_ext

This will compile all the c++ source files in the folder src/ with the headers in include/ and put the built object files in the directory build/. Then it will link the object files together into the library which will be located in lib/. Python __init__.py files make the libary visible to the Python interface files located in python/. You must re-run

bash-prompt$ source ./bin/setup.sh

in every new terminal that you use Kālī in. You may consider adding

source <path to kali>/bin/setup.sh

to your .bashrc. To clean the library, just delete the build/ directory and any files inside lib/ except, of course, the __init__.py file. You may consider adding

source <path to kali>/bin/setup.sh

to your .bashrc. This covers installation. Please feel free to try the package out by running

bash-prompt$ source <path to kali>/bin/setup.sh

and following through the user guide available at <path to kali>/guide/Introduction.ipynb. More example code can be found in the folders <path to kali>/examples and <path to kali>/tests.