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README.md
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README.md
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# SAGECAL
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##Features
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## Features
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- Levenberg-Marquardt, LBFGS, Riemannian Trust Region, Nesterov's accelerated gradient descent algorithms
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- GPU acceleration using CUDA
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## Step by Step Introduction:
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##1) Input Data
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### 1) Input Data
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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.
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##2) Sky Model:
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###2a)Make an image of your MS (using ExCon/casapy).
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### 2) Sky Model:
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#### 2a)Make an image of your MS (using ExCon/casapy).
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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.
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Special options to buildsky: -o 1 (NOTE: not -o 2)
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Alternatively, create these files by hand according to the following formats.
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###2b)Cluster file format:
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#### 2b)Cluster file format:
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cluster_id chunk_size source1 source2 ...
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e.g.
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```
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chunk_size: find hybrid solutions during one solve run. Eg. if -t 120 is used
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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.
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###2c)Sky model format:
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#### 2c)Sky model format:
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```
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#name h m s d m s I Q U V spectral_index RM extent_X(rad) extent_Y(rad) pos_angle(rad) freq0
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```
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...
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##3)Run sagecal
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### 3)Run sagecal
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Optionally: Make sure your machine has (1/2 working NVIDIA GPU cards or Intel Xeon Phi MICs) to use sagecal.
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Recommended usage: (with GPUs)
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and use it as the 'ignore_file'.
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##4)Distributed calibration
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### 4)Distributed calibration
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Use mpirun to run sagecal-mpi, example:
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mpirun -np 11 -hostfile ./machines --map-by node --cpus-per-proc 8
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The rest of the options are similar to sagecal.
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##5)Solution format
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### 5)Solution format
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All SAGECal solutions are stored as text files. Lines starting with '#' are comments.
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The first non-comment line includes some general information, i.e.
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freq(MHz) bandwidth(MHz) time_interval(min) stations clusters effective_clusters
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