Metadata-Version: 2.4
Name: jolly-roger
Version: 0.4.1
Summary: The pirate flagger
Project-URL: Homepage, https://github.com/flint-crew/jolly-roger
Project-URL: Bug Tracker, https://github.com/flint-crew/jolly-roger/issues
Project-URL: Discussions, https://github.com/flint-crew/jolly-roger/discussions
Project-URL: Changelog, https://github.com/flint-crew/jolly-roger/releases
Author-email: Alec Thomson <alec.thomson@csiro.au>
License-Expression: BSD-3-Clause
License-File: LICENSE
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: astropy
Requires-Dist: dask-ms
Requires-Dist: matplotlib
Requires-Dist: numpy>=2.0.0
Requires-Dist: python-casacore>=3.6.0
Requires-Dist: tqdm
Description-Content-Type: text/markdown

# jolly-roger

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The pirate flagger!

<img src="logo.png" alt="The Jolly Roger Flag" style="width:400px;"/>


# Installation

`pip install jolly-roger`

# About

This package attempts to flag or modify visibilities that are contaminated by the Sun (or potentially some other bright source). There are two main modes that are currently supported in `jolly-roger`.

The flags based on the projected baseline length should the array be tracking the Sun. The projected baseline length between some phased direction being tracked and the Sun can be significantly different. `jolly_roger` attempts to leverage this by only flagging data where the projected baseline length is between some nominal range that corresponds to angular scales associated with the Sun.

The second mode is the application of a notch filter in delay space, where the nulling is applied to the expected delay of the Sun for each timestep and baseline. This mode reads the data row-wise as it appears in the measurement set, so can be fast enough to apply to long-tracks ASKAP observations while also using a limited set of computing resources. A ten hour ASKAP continuum observation can be processed in ~2 minutes, with the process being mostly I/O bound.

`jolly_roger` makes no guarentess about removing all contaminated visibilities, nor does it attempt to peel/subtract the Sun from the visibility data.

# Projected baseline flagging
## How does it work?

`jolly_roger` will recompute the (u,v,w)-coordinates of a measurement set as if it were tracking the Sun, from which (u,v)-distances are derieved for each baseline and timestep. An updated `FLAG` column can then be inserted into the measurement set suppressing visibilities that would be sensitive to a nominated range of angular scales. This mode also allows for flagging based on the elevation of the target source.

## Example

`jolly_roger` has a CLI entry point for projected baseline flagging that can be called as:

```
jolly_flagger scienceData.EMU_1141-55.SB47138.EMU_1141-55.beam00_averaged_cal.leakage.ms --min-horizon-limit-deg '-2' --max-horizon-limit-deg 30 --min-scale-deg 0.075
```

Here we are flagging visibilities that correspond to instances where:
- the Sun has an elevation between -2 and 30 degrees, and
- they are sensitive to angular scales larger than 0.075 degrees.

# Delay nulling (notch filter)
## How it works

In this mode the frequency data of each timestep/baseline is Fourier transformed to form a delay spectrum. `jolly-roger` can null for delays away from zero (the tracked sky-position), or null the delays towards some nominated sky-direction. The expected delay of a bright source can be computed by examining the difference between the w-terms of the phased direction and the source direction.

## Example

The nulling approach can be accessed through `jolly_tractor`. Examples of its application are below. The left and right columns indicate the before and after of the nulling procedure (here nulling towards the Sun's sky position). The top row shows the dynamic sopectrum (time vs frequency) while the bottom highlight the time vs delay of the data.

Sunrise was approximately in the middle of this observation, as indicated by the sudden excess power seen in the top left figure.

The red dashed lines in the lower panel represents the delay of the Sun as derived from the geometry of the array, with the length of each dash represents the Nyquist zone (i.e. how aliased the source appears in delay space). Nulling can be deactivated if the Nyquist zone of the object is high enough to effectively mean no contribution (typically the case for longer baselines).

Should the source cross over a delay of 0 then that timestep will be flagged, as the intermixed components can not be separated, and nulling would have an adverse effect of the direction being observed.

### Baseline ak00 to ak02
![Example 1](docs/images/baseline_data_0_2_comparison.png)

### Baseline ak00 to ak06
![Example 1](docs/images/baseline_data_0_6_comparison.png)

The full set of command line options can be obtained with:

```
jolly_tractor --help
```
