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https://github.com/miguelcarcamov/snow
SNOW: caSa pythoN self-calibratiOn frameWork
https://github.com/miguelcarcamov/snow
astronomy-astrophysics astrophysics image-synthesis imaging interferometry object-oriented-programming python radio-astronomy radio-imaging radioastro radioastronomy self-calibration selfcalibration
Last synced: 1 day ago
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SNOW: caSa pythoN self-calibratiOn frameWork
- Host: GitHub
- URL: https://github.com/miguelcarcamov/snow
- Owner: miguelcarcamov
- License: gpl-3.0
- Created: 2020-03-08T20:40:32.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-09-03T22:46:46.000Z (30 days ago)
- Last Synced: 2024-09-25T15:38:31.614Z (8 days ago)
- Topics: astronomy-astrophysics, astrophysics, image-synthesis, imaging, interferometry, object-oriented-programming, python, radio-astronomy, radio-imaging, radioastro, radioastronomy, self-calibration, selfcalibration
- Language: Python
- Homepage:
- Size: 1.93 MB
- Stars: 7
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **SNOW**
## ca**S**a pytho**N** self-calibrati**O**n frame**W**ork
Many radio-astronomers repeat the process of writing different scripts for self-calibration
depending on their datasets. This repository holds an object-oriented Framework for self-calibration
of radio-interferometric datasets that will help radio astronomers to minimize the tedious work of
writing self-calibration scripts once again. The idea is to call just one main Python script that
will run an imager (tclean, wsclean, gpuvmem, rascil, etc.) and one or multiple self-calibration
objects (phase, amplitude, amplitude-phase) having the self-calibrated dataset as a result.
## Requirements
1. `Python == 3.8`
2. Check CASA pip current version requirements [here](https://casadocs.readthedocs.io/en/stable/notebooks/introduction.html#Modular-Packages).
3. Check the `requirements.txt` file.
## Installation
### From PYPI repository
- `pip install snow`
### From Github
- `pip install -U git+https://github.com/miguelcarcamov/snow`
### From source
```bash
git clone https://github.com/miguelcarcamov/snow
cd snow
pip install .
```
### From source as developer
```bash
git clone https://github.com/miguelcarcamov/snow
cd snow
pip install -e .
```
## Using docker container
```bash
docker pull ghcr.io/miguelcarcamov/snow:latest
```
## Run snow
```python
# Import the modules that you want to use
import sys
from snow.selfcalibration import Phasecal, AmpPhasecal
from snow.imaging import Tclean
if __name__ == '__main__':
# This step is up to you, and option to capture your arguments from terminal is using sys.argv
visfile = sys.argv[3]
output = sys.argv[4]
want_plot = eval(sys.argv[5])
# Table for automasking on long or short baselines can be found here: https://casaguides.nrao.edu/index.php/Automasking_Guide
# The default clean object will use automasking values for short baselines
# In this case we will use automasking values for long baselines
# Create different imagers with different thresholds (this is optional, you can create just one)
clean_imager_phs = Tclean(inputvis=visfile, output=output, niter=100, M=1024, N=1024, cell="0.005arcsec",
stokes="I", datacolumn="corrected", robust=0.5,
specmode="mfs", deconvolver="hogbom", gridder="standard",
savemodel=True, usemask='auto-multithresh', threshold="0.1mJy", sidelobethreshold=3.0,
noisethreshold=5.0,
minbeamfrac=0.3, lownoisethreshold=1.5, negativethreshold=0.0, interactive=True)
clean_imager_ampphs = Tclean(inputvis=visfile, output=output, niter=100, M=1024, N=1024, cell="0.005arcsec",
stokes="I", datacolumn="corrected", robust=0.5,
specmode="mfs", deconvolver="hogbom", gridder="standard",
savemodel=True, usemask='auto-multithresh', threshold="0.025mJy",
sidelobethreshold=3.0,
noisethreshold=5.0,
minbeamfrac=0.3, lownoisethreshold=1.5, negativethreshold=0.0, interactive=True)
# This is a dictionary with shared variables between self-cal objects
shared_vars_dict = {'visfile': visfile, 'minblperant': 6, 'refant': "DA51", 'spwmap': [
0, 0, 0, 0], 'gaintype': 'T', 'want_plot': want_plot}
# Create your solution intervals
solint_phs = ['inf', '600s']
solint_ap = ['inf']
# Create your phasecal object
phscal = Phasecal(minsnr=3.0, solint=solint_phs, combine="spw", imager=clean_imager_phs, **shared_vars_dict)
# Run it!
phscal.run()
# If we are happy with the result of the only-phase self-cal we can end the code here, if not...
# Create the amplitude-phase self-cal object
apcal = AmpPhasecal(minsnr=3.0, solint=solint_ap, combine="", previous_selfcal=phscal, imager=clean_imager_ampphs,
**shared_vars_dict)
# Run it
apcal.run()
# Get your splitted final MS
apcal.selfcal_output(overwrite=True)
```
Then you can simply run the main script using `python yourscript.py `