FIX: fixing dockerfile for lighter images

miguelcarcamov · miguelcarcamov · commit 75755664479b · 2025-12-24T14:14:00.000-03:00
diff --git a/README.md b/README.md
@@ -2,105 +2,260 @@
 
 ## 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.
+**SNOW** is an object-oriented Python framework for radio-interferometric self-calibration that simplifies the process of writing self-calibration scripts. Instead of rewriting calibration workflows for each dataset, SNOW provides a unified interface to run imagers (tclean, wsclean, gpuvmem, rascil, etc.) and self-calibration algorithms (phase, amplitude, amplitude-phase) in a single, streamlined workflow.
+
+### Features
+
+- **Object-oriented design**: Clean, modular architecture for easy customization
+- **Multiple imagers supported**: CASA tclean, wsclean, GPUvmem, and more
+- **Flexible self-calibration**: Phase-only, amplitude-only, or combined amplitude-phase calibration
+- **CASA integration**: Built on CASA (Common Astronomy Software Applications) for radio astronomy data processing
+- **Reproducible workflows**: Version-controlled calibration pipelines
+- **Docker support**: Pre-built container images for easy deployment
 
 ## Requirements
 
-1. `Python == 3.8`
-2. Check [CASA pip package requirements](https://casadocs.readthedocs.io/en/stable/notebooks/introduction.html#Modular-Packages) for current version requirements.
-3. Check the `requirements.txt` file.
+- **Python**: `>=3.10, <3.11` (Python 3.10 only, due to CASA compatibility requirements)
+- **CASA packages**: See [CASA pip package requirements](https://casadocs.readthedocs.io/en/stable/notebooks/introduction.html#Modular-Packages) for current version requirements
+- **Dependencies**: See `requirements.txt` for a complete list of required packages
+
+### Key Dependencies
+
+- **CASA packages**: casatasks, casatools, casashell, and related CASA modules
+- **Scientific Python**: numpy (1.26.0), scipy (1.15.0), astropy (6.1.0)
+- **Astronomy tools**: reproject (0.14.1), python-casacore (3.7.1)
 
 ## Installation
 
-### From PYPI repository
+### Option 1: From PyPI (Recommended)
+
+```bash
+pip install snow
+```
+
+### Option 2: From GitHub
+
+```bash
+pip install -U git+https://github.com/miguelcarcamov/snow.git
+```
 
-- `pip install snow`
+### Option 3: Using Conda
 
-### From Github
+Create a conda environment from the provided environment file:
+
+```bash
+conda env create -f environment.yml
+conda activate snow-env
+```
 
-- `pip install -U git+https://github.com/miguelcarcamov/snow`
+The environment file includes all system dependencies and will automatically install Python packages from `requirements.txt`.
 
-### From source
+### Option 4: From Source
 
 ```bash
 git clone https://github.com/miguelcarcamov/snow
 cd snow
 pip install .
 ```
 
-### From source as developer
+### Option 5: Development Installation
+
+For development work with editable installation:
 
 ```bash
 git clone https://github.com/miguelcarcamov/snow
 cd snow
 pip install -e .
 ```
 
-## Using docker container
+### Option 6: Docker Container
+
+Pull the pre-built Docker image:
 
 ```bash
 docker pull ghcr.io/miguelcarcamov/snow:latest
 ```
 
-## Run snow
+The Docker image includes all dependencies and is ready to use. See the [Docker documentation](https://docs.docker.com/) for usage instructions.
+
+## Quick Start
+
+### Basic Usage Example
 
 ```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)
+    # Get input parameters (adjust as needed for your use case)
+    visfile = sys.argv[1]  # Input measurement set
+    output = sys.argv[2]   # Output prefix
+    want_plot = eval(sys.argv[3])  # Whether to generate plots
+
+    # Create imager for phase calibration
+    # See CASA automasking guide: https://casaguides.nrao.edu/index.php/Automasking_Guide
+    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
+    )
+
+    # Create imager for amplitude-phase calibration (with lower threshold)
+    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
+    )
+
+    # Shared parameters for self-calibration objects
+    shared_vars_dict = {
+        'visfile': visfile,
+        'minblperant': 6,
+        'refant': "DA51",
+        'spwmap': [0, 0, 0, 0],
+        'gaintype': 'T',
+        'want_plot': want_plot
+    }
+
+    # Solution intervals for phase calibration
+    solint_phs = ['inf', '600s']
+
+    # Solution intervals for amplitude-phase calibration
+    solint_ap = ['inf']
+
+    # Phase-only self-calibration
+    phscal = Phasecal(
+        minsnr=3.0,
+        solint=solint_phs,
+        combine="spw",
+        imager=clean_imager_phs,
+        **shared_vars_dict
+    )
+    phscal.run()
+
+    # Amplitude-phase self-calibration (optional, only if phase calibration is successful)
+    apcal = AmpPhasecal(
+        minsnr=3.0,
+        solint=solint_ap,
+        combine="",
+        previous_selfcal=phscal,
+        imager=clean_imager_ampphs,
+        **shared_vars_dict
+    )
+    apcal.run()
+
+    # Output the final calibrated measurement set
+    apcal.selfcal_output(overwrite=True)
 ```
 
-Then you can simply run the main script using `python yourscript.py <arguments>`
+Run the script:
+
+```bash
+python yourscript.py <visfile> <output_prefix> True
+```
+
+## API Overview
+
+### Imaging Classes
+
+SNOW supports multiple imaging backends:
+
+- **`Tclean`**: CASA tclean task wrapper for standard imaging
+- **`WSClean`**: WSClean imager interface
+- **`GPUvmem`**: GPU-accelerated imaging with GPUvmem
+- **`Imager`**: Base class for custom imagers
+
+### Self-Calibration Classes
+
+- **`Phasecal`**: Phase-only self-calibration
+- **`Ampcal`**: Amplitude-only self-calibration
+- **`AmpPhasecal`**: Combined amplitude-phase self-calibration
+- **`Selfcal`**: Base class for self-calibration algorithms
+
+### Utility Modules
+
+- **`snow.utils.image_utils`**: Image processing utilities
+- **`snow.utils.selfcal_utils`**: Self-calibration helper functions
+
+## Project Structure
+
+```
+snow/
+├── src/snow/
+│   ├── imaging/          # Imaging backends (tclean, wsclean, gpuvmem)
+│   ├── selfcalibration/  # Self-calibration algorithms
+│   └── utils/            # Utility functions
+├── main_files/           # Example scripts for different telescopes
+├── requirements.txt      # Python dependencies
+├── environment.yml       # Conda environment specification
+└── pyproject.toml       # Project metadata and build configuration
+```
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This project is licensed under the terms specified in the `LICENSE` file.
+
+## Citation
+
+If you use SNOW in your research, please cite:
+
+```bibtex
+@software{snow,
+  author = {Cárcamo, Miguel},
+  title = {SNOW: CASA Python Self-calibration Framework},
+  url = {https://github.com/miguelcarcamov/snow},
+  version = {<version>},
+  year = {<year>}
+}
+```
+
+## Support
+
+- **Issues**: Report bugs or request features on [GitHub Issues](https://github.com/miguelcarcamov/snow/issues)
+- **Documentation**: See the [CASA documentation](https://casadocs.readthedocs.io/) for CASA-specific questions
+- **Contact**: <miguel.carcamo@usach.cl>
+
+## Acknowledgments
+
+SNOW is built on top of [CASA](https://casa.nrao.edu/) (Common Astronomy Software Applications), developed by the National Radio Astronomy Observatory (NRAO).
