SageCal
SageCal Documentation
master dev

# SAGECAL

## Features

• Levenberg-Marquardt, LBFGS, Riemannian Trust Region, Nesterov's accelerated gradient descent algorithms
• GPU acceleration using CUDA
• Fast and accurate interferometric calibration
• Gaussian and Student's t noise models
• Shapelet source models
• CASA MS data format supported
• Distributed calibration using MPI - consensus optimization with data multiplexing
• Tools to build sky models and restore sky models to images

Read INSTALL for installation. This file gives a brief guide to use SAGECal. Warning: this file may be obsolete. use sagecal -h to see up-to-date options.

## Code documentation

Code documentation can be found here.

## Step by Step Introduction:

### 1) Input Data

Input to sagecal must be in CASA MS format, make sure to create a column in the MS to write output data as well. The data can be in raw or averaged form, also initial calibration using other software can be also applied.

### 2) Sky Model:

#### 2a) Make an image of your MS (using ExCon/casapy).

Use Duchamp to create a mask for the image. Use buildsky to create a sky model. (see the README file on top level directory). Also create a proper cluster file. Special options to buildsky: -o 1 (NOTE: not -o 2)

Alternatively, create these files by hand according to the following formats.

#### 2b) Cluster file format:

cluster_id chunk_size source1 source2 ... e.g.

0 1 P0C1 P0C2
2 3 P11C2 P11C1 P13C1

Note: putting -ve values for cluster_id will not subtract them from data. chunk_size: find hybrid solutions during one solve run. Eg. if -t 120 is used to select 120 timeslots, cluster 0 will find a solution using the full 120 timeslots while cluster 2 will solve for every 120/3=40 timeslots.

#### 2c) Sky model format:

#name h m s d m s I Q U V spectral_index RM extent_X(rad) extent_Y(rad) pos_angle(rad) freq0

or

#name h m s d m s I Q U V spectral_index1 spectral_index2 spectral_index3 RM extent_X(rad) extent_Y(rad) pos_angle(rad) freq0

e.g.:

P1C1 0 12 42.996 85 43 21.514 0.030498 0 0 0 -5.713060 0 0 0 0 115039062.0
P5C1 1 18 5.864 85 58 39.755 0.041839 0 0 0 -6.672879 0 0 0 0 115039062.0
#Position Angle: "West from North (counter-clockwise)" (0 deg = North, 90 deg = West).
#Note: PyBDSM and BBS use "North from East (counter-clockwise)" (0 deg = East, 90 deg = North).
G0 5 34 31.75 22 00 52.86 100 0 0 0 0.00 0 0.0012 0.0008 -2.329615801 130.0e6
D01 23 23 25.67 58 48 58 80 0 0 0 0 0 0.000715 0.000715 0 130e6
R01 23 23 25.416 58 48 57 70 0 0 0 0 0 0.00052 0.00052 0 130e6
#A shapelet ('S3C61MD.fits.modes' file must be in the current directory)
S3C61MD 2 22 49.796414 86 18 55.913266 0.135 0 0 0 -6.6 0 1 1 0.0 115000000.0

Note: Comments starting with a '#' are allowed for both sky model and cluster files. Note: 3rd order spectral indices are also supported, use -F 1 option in sagecal. Note: Spectral indices use natural logarithm, exp(ln(I0) + p1 * ln(f/f0) + p2 * ln(f/f0)^2 + ..) so if you have a model with common logarithms like 10^(log(J0) + q1*log(f/f0) + q2*log(f/f0)^2 + ..) then, conversion is

ln(I0)+p1*ln(f/f0)+p2*ln(f/f0)^2+... = ln(10)*(log(J0)+q1*log(f/f0)+q2*log(f/f0))^2)+...)
=ln(10)*(ln(J0)/ln(10)+q1*ln(f/f0)/ln(10)+q2*ln(f/f0)^2/ln(10)^2+...)

so

I0=J0
p1=q1
p2=q2/ln(10)
p3=q3/ln(10)^2
...

### 3) Run sagecal

Optionally: Make sure your machine has (1/2 working NVIDIA GPU cards or Intel Xeon Phi MICs) to use sagecal. Recommended usage: (with GPUs)

sagecal -d my_data.MS -s my_skymodel -c my_clustering -n no.of.threads -t 60 -p my_solutions -e 3 -g 2 -l 10 -m 7 -w 1 -b 1

Use your solution interval (-t 60) so that its big enough to get a decent solution and not too big to make the parameters vary too much. (about 20 minutes per solution is reasonable).

Note: It is also possible to calibrate more than one MS together. See section 4 below. Note: To fully use GPU acceleration use -E 1 option.

Simulations: With -a 1, only a simulation of the sky model is done. With -a 1 and -p 'solutions_file', simulation is done with the sky model corrupted with solutions in 'solutions_file'. With -a 1 and -p 'solutions_file' and -z 'ignore_file', simulation is done with the solutions in the 'solutions_file', but ignoring the cluster ids in the 'ignore_file'. Eg. If you need to ignore cluster ids '-1', '10', '999', create a text file :

-1
10
999

and use it as the 'ignore_file'.

### 4) Distributed calibration

Use mpirun to run sagecal-mpi, example:

mpirun -np 11 -hostfile ./machines --map-by node --cpus-per-proc 8
--mca yield_when_idle 1 -mca orte_tmpdir_base /scratch/users/sarod
/full/path/to/sagecal-mpi -f 'MS*pattern' -A 30 -P 2 -r 5
-s sky.txt -c cluster.txt -n 16 -t 1 -e 3 -g 2 -l 10 -m 7 -x 10 -F 1 -j 5

Specific options : -np 11 : 11 processes : starts 10 slaves + 1 master

./machines : will list the host names of the 11 (or fewer) nodes used ( 1st name is the master ) : normally the node where you invoke mpirun

-f 'MS*pattern' : Search MS names that match this pattern and calibrate all of them together. The total number of MS being calibrated can be higher than the actual number of slaves (multiplexing).

-A 30 : 30 ADMM iterations.

-P 2 : polynomial in frequency has 2 terms.

-Q : can change the type of polynomial used (-Q 2 gives Bernstein polynomials).

-r 5 : regularization factor is 5.0.

-G textfile: each cluster can have a different regularization factor, instead of using -r option when the regularization is the same for all clusters.

MPI specific options:

/scratch/users/sarod : this is where MPI stores temp files (default is probably /tmp).

--mca*: various options to tune the networking and scheduling.

Note: the number of slaves (-np option) can be lower than the number of MS calibrated. The program will divide the workload among the number of available slaves.

The rest of the options are similar to sagecal.

### 5) Solution format

All SAGECal solutions are stored as text files. Lines starting with '#' are comments. The first non-comment line includes some general information, i.e. freq(MHz) bandwidth(MHz) time_interval(min) stations clusters effective_clusters

The remaining lines contain solutions for each cluster as a single column, the first column is just a counter. Let's say there are K effective clusters and N directions. Then there will be K+1 columns, the first column will start from 0 and increase to 8N-1, which can be used to count the row number. It will keep repeating this, for each time interval. The rows 0 to 7 belong to the solutions for the 1st station. The rows 8 to 15 for the 2nd station and so on. Each 8 rows of any given column represent the 8 values of a 2x2 Jones matrix. Lets say these are S0,S1,S2,S3,S4,S5,S6 and S7. Then the Jones matrix is [S0+j*S1, S4+j*S5; S2+j*S3, S6+j*S7] (the ';' denotes the 1st row of the 2x2 matrix).

When a cluster has a chunk size > 1, there will be more than 1 solution per given time interval. So for this cluster, there will be more than 1 column in the solution file, the exact number of columns being equal to the chunk size.