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GRAph Made Statistics for Cosmological Information A higher-order correlation function code driven by a custom graph database and a powerful kd-tree implementation. Running in serial, OpenMP, MPI and hybrid parallel schemes. Developers: Cristiano Sabiu, Ben Hoyle, Xiao-Dong Li, Juhan Kim If you use this code please cite: https://arxiv.org/abs/19…

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----------------------------------------------------------------------
      GRAMSCI: GRAph Made Statistics for Cosmological Information
----------------------------------------------------------------------
----- Cristiano Sabiu, [email protected] ------
----------------------------------------------------------------------
----- Graph Database Solution for Higher Order Spatial Statistics in the Era of Big Data
----- Cristiano Sabiu, Ben Hoyle, Juhan Kim, XiaoDong Li 
----- arXiv: https://arxiv.org/abs/1901.00296
----- cite:  The Astrophysical Journal Supplement Series, Volume 242, Issue 2, article id. 29, 8 pp. (2019).
----------------------------------------------------------------------

 -----------------
 **   gramsci   **
 -----------------

PURPOSE: Code for calculating the N-point Spatial Correlation Functions of a 3D point set.
      
      
 CALLING SEQUENCE:
       gramsci [-gal galaxy file][-ran ranfile (optional)]
               [-rmin Rmin] [-rmax Rmax] [-nbins Nbins] 
               [-nmu Nmu] [-out out_file] 
  
       eg: ./gramsci -rmin 10.0 -rmax 12.0 -nbins 10 [choose one: -2pcf -3pcf -4pcf]
  
 INPUTS:
    
        out_file = filename for output correlation func. 
    
        Rmin = Min. radial seperation
    
        Rmax = Max. radial seperation
  
 OPTIONAL:
        Nbins = Number of radial bins to calculate
  
        Nmu = Number of angular (mu) bins - linear in range -1<mu<+1
  


 --------------------------------
 **    domain_decompose.sh     **
 --------------------------------

PURPOSE: 

        Code for performing domain decomposition of the input catalogues
        This code is useful if the catalogues are large or the Rmax value is too large.
        Decomposing into more regions will reduce the RAM required of the graph database.
        However, the correlation code must be ran on each region sepratley add the results 
        collected later.

CALLING SEQUENCE:

      domain_decompose.sh [data_file] [random_file] [Nregion] [Rmax] 
 
      eg: domain_decompose.sh test.gal test.ran 4 100.0 

INPUTS:
       data_file = input catalogue 3-4 columns [x y z (weight)]

       random_file = input random catalogue 3-4 columns [x y z (weight)] - matching above file

       Nregion = integer controlling the number of domain decomposed regions N^3. 
                 i.e. N=3 -> 27 regions
   
       Rmax = Max. radial seperation

BUILDING
--------
A Fortran compiler such as `gfortran` is required. To build the executables run:

```
cd src
make all
```

This will compile the code and place the `gramsci` and `domain_decomposition`
executables in the `bin/` directory.

TESTING
-------
Run the unit tests with:

```
make test
```

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GRAph Made Statistics for Cosmological Information A higher-order correlation function code driven by a custom graph database and a powerful kd-tree implementation. Running in serial, OpenMP, MPI and hybrid parallel schemes. Developers: Cristiano Sabiu, Ben Hoyle, Xiao-Dong Li, Juhan Kim If you use this code please cite: https://arxiv.org/abs/19…

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