| Name | jiveplot JSON |
| Version |
2.1.0
JSON |
| download |
| home_page | None |
| Summary | Python based visualization tool for AIPS++/CASA MeasurementSet version 2 data |
| upload_time | 2025-08-08 15:34:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | GPL-3.0 |
| keywords |
astronomy
astrophysics
vlbi
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://singularity-hub.org/collections/1847)
# jplotter / jiveplot
Python based visualization tool for AIPS++/CASA MeasurementSet data
The jplotter command line tool allows the user to quickly visualize the
radio-astronomical data contained in a MeasurementSet (`ms`).
## Python support
Starting from 20 Jul 2023 the `master` branch has been updated to support
Py2 and Py3 based systems simultaneously. This version is tagged [v2.0](https://github.com/haavee/jiveplot/releases/tag/v2.0). The
previous latest Py2-only master was tagged as
[v1.1](https://github.com/haavee/jiveplot/releases/tag/v1.1) and
[last-python2-only-master](https://github.com/haavee/jiveplot/releases/tag/last-python2-only-master).
Since Aug 2025 the module can be installed using `pip` - the project was restructured to adhere to modern Python packaging.
After cloning the repository it is possible to do
```bash
$ cd /path/to/clone
$ pip install [-e] .
```
Starting from *v2.1.0* the package is now also published on PyPI.
## 5 second workflow
After downloading and having the
[dependencies](https://github.com/haavee/jiveplot#dependencies) installed
(as of 30 Oct 2018 you can run from a [singularity or Docker](https://github.com/haavee/jiveplot#singularity-and-docker-container-images) image) type:
```bash
$ /path/to/jiveplot/jplotter
+++++++++++++++++++++ Welcome to cli +++++++++++++++++++
$Id: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $
'exit' exits, 'list' lists, 'help' helps
jcli>
```
and you're in the command line environment. Then open a MS, select data,
select what to plot and go.
This README will not explain any further because there is a colourful [PDF cookbook/tutorial/explanation](jplotter-cookbook-draft-v2.pdf) with far more detail.
## What can be visualized?
Quantities that can be visualized are, e.g., amplitude-versus-time,
phase-versus-frequency, amplitude-versus-uv-distance, weight-versus-time, to
name but a few.
Some key features:
- the package focuses on powerful selection syntax
- has built-in help for all commands
- the ability to difference whole sets of plots, e.g. to visualize before-after changes or to
compare output of different correlators
- time- or frequency averaging of the data before plotting
- plots can be saved to file (postscript).
- plots/data sets can be organized at will
- the data can be indexed (`> indexr`) to create a scan list, after which powerful
scan-based selection can be used
- plotting can be scripted/play back stored commands from text file
- open/visualize multiple data sets at the same time or the same data set
from different 'angles'
- the current selection can be written out as a new *reference* `ms`; data is not copied but the newly created `ms` references rows of data in the parent `ms`. It can be treated as a real `ms`.
## Data selection
`ms`'s can contain several GBs of binary data. Therefore, data selection is
desirable, preferably in a fairly natural way, even without knowing the
exact details of the experiment's data.
The jplotter selection commands take a stab at suiting the needs of a radio
astronomer:
```sh
# select a time range near the end of the experiment
jcli> time $end-1h to +2m20s
# select IF 0,1,2 with parallel hand polarizations
jcli> fq 0-2/p
# equivalent, but would not work for XX, YY whereas the former would
jcli> fq 0-2/rr,ll
# select sources whose name matches this
jcli> src j(19|30)*
# select all cross baselines, remove those to stations xx and yy, but add xx-ef
jcli> bl cross -(xx|yy)* +xx(ef)
# select 80% of the band, irrespective of how many channels
# the correlator produced
jcli> ch 0.1*last:0.9*last
# after running indexr, extract a bit of data (trimming 1 minute from either
# end) from scans on sources matching 042* and who are longer than three minutes
jcli> scan start+1m to end-1m where length>3m and field ~ '042*'
```
# Dependencies
The package uses the [pyrap, python casacore](https://github.com/casacore/python-casacore)
Python binding to access data.
It uses pgplot to visualize (it was faster and easier than matplotlib):
[Python binding to pgplot](https://github.com/haavee/ppgplot) (the github version is preferred over this old link: http://www.jive.eu/~verkout/ppgplot-1.4.tar.gz)
The github version became online during the course of 2018 and has a `setup.py` which has support for Python2 and 3, where the `ppgplot-1.4.tar.gz` lacks this.
Note: if the original `PGPLOT` is giving too many headaches, the [Giza](https://github.com/danieljprice/giza) library can be used as drop-in replacement for `ppgplot` to link against for its `libpgplot.so`. My [ppgplot fork](https://github.com/haavee/ppgplot)'s `setup.py` has support for having both FORTRAN PGPLOT and Giza installed and allows for compile-time selection of which *actual* pgplot backend to use.
# Singularity and Docker container images
As of 30 October 2018 [Singularity](https://www.sylabs.io/) and [Docker](https://www.docker.com/) images are available. In fact, the Singularity image just runs the Docker image. The [jiveplot Docker image](https://hub.docker.com/r/haavee/jiveplot/) contains `jiveplot` and all its dependencies and is built on top of the excellent [kernsuite/kern-4](http://kernsuite.info) project.
Even though all functionality is in the Docker image, we advise to run/install Singularity (if you have a choice) for the following reasons:
- X11 forwarding works out of the box with Singularity, which is convenient if you wish to actually *see* the plots on your screen. According to the interwebs X forwarding can be done through Docker as well but it didn't for me (see below)
- Your `${HOME}` directory is visible by default inside the Singularity container. This has the nice effect that your `jiveplot` command history and aliases are persisted between runs of the image (`~/.jcli.history` for the history). This in turn means that `^r` (reverse-search-history) is actually useful
- I'm not even going to mention the security issues of Docker which has to run as root
### Running the Singularity image
*UPDATE* November 2019 - because of [Singularity security
changes](https://singularityhub.github.io/singularityhub-docs/2019/security-release/#api-access)
it is now recommended to use the following method of running the jiveplot
container:
```bash
$ singularity pull shub://haavee/jiveplot:latest
# this will give you a local `path/to/*.simg` file
$ singularity run --bind <local dir>:<container dir> path/to/*.simg
```
where `<local dir>` is the/a directory on your host where your CASA
MeasurementSet(s) live and `<container dir>` is the desired mount point
_inside_ the container.
### Running the Docker image
Allegedly, running Docker like this:
```bash
$ docker run -it --init --network=host -v /tmp/.X11-unix:/tmp/.X11-unix:ro -e DISPLAY="$DISPLAY" -v <local dir>:<container dir> haavee/jiveplot
```
does X11 forwarding but yours truly has seen it also *not* work. YMMV.
Both commands should drop you immediately into the `jiveplot` command line interface:
```bash
+++++++++++++++++++++ Welcome to cli +++++++++++++++++++
$Id: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $
'exit' exits, 'list' lists, 'help' helps
jcli> ms <container dir>/path/to/my_data.ms
MS my_data.ms opened &cet
jcli>
```
Raw data
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"description": "[](https://singularity-hub.org/collections/1847)\n\n# jplotter / jiveplot\nPython based visualization tool for AIPS++/CASA MeasurementSet data\n\nThe jplotter command line tool allows the user to quickly visualize the\nradio-astronomical data contained in a MeasurementSet (`ms`).\n\n## Python support\n\nStarting from 20 Jul 2023 the `master` branch has been updated to support\nPy2 and Py3 based systems simultaneously. This version is tagged [v2.0](https://github.com/haavee/jiveplot/releases/tag/v2.0). The\nprevious latest Py2-only master was tagged as\n[v1.1](https://github.com/haavee/jiveplot/releases/tag/v1.1) and\n[last-python2-only-master](https://github.com/haavee/jiveplot/releases/tag/last-python2-only-master).\n\nSince Aug 2025 the module can be installed using `pip` - the project was restructured to adhere to modern Python packaging.\nAfter cloning the repository it is possible to do\n```bash\n$ cd /path/to/clone\n$ pip install [-e] .\n```\nStarting from *v2.1.0* the package is now also published on PyPI.\n\n\n## 5 second workflow\n\nAfter downloading and having the\n[dependencies](https://github.com/haavee/jiveplot#dependencies) installed\n(as of 30 Oct 2018 you can run from a [singularity or Docker](https://github.com/haavee/jiveplot#singularity-and-docker-container-images) image) type:\n\n```bash\n$ /path/to/jiveplot/jplotter\n+++++++++++++++++++++ Welcome to cli +++++++++++++++++++\n$Id: command.py,v 1.16 2015-11-04 13:30:10 jive_cc Exp $\n 'exit' exits, 'list' lists, 'help' helps\njcli>\n```\nand you're in the command line environment. Then open a MS, select data,\nselect what to plot and go.\n\nThis README will not explain any further because there is a colourful [PDF cookbook/tutorial/explanation](jplotter-cookbook-draft-v2.pdf) with far more detail.\n\n## What can be visualized?\n\nQuantities that can be visualized are, e.g., amplitude-versus-time,\nphase-versus-frequency, amplitude-versus-uv-distance, weight-versus-time, to\nname but a few.\n\nSome key features:\n- the package focuses on powerful selection syntax\n- has built-in help for all commands\n- the ability to difference whole sets of plots, e.g. to visualize before-after changes or to\ncompare output of different correlators\n- time- or frequency averaging of the data before plotting\n- plots can be saved to file (postscript).\n- plots/data sets can be organized at will\n- the data can be indexed (`> indexr`) to create a scan list, after which powerful\n scan-based selection can be used\n- plotting can be scripted/play back stored commands from text file\n- open/visualize multiple data sets at the same time or the same data set\n from different 'angles'\n- the current selection can be written out as a new *reference* `ms`; data is not copied but the newly created `ms` references rows of data in the parent `ms`. It can be treated as a real `ms`.\n\n## Data selection\n`ms`'s can contain several GBs of binary data. Therefore, data selection is\ndesirable, preferably in a fairly natural way, even without knowing the\nexact details of the experiment's data.\n\nThe jplotter selection commands take a stab at suiting the needs of a radio\nastronomer:\n\n```sh\n# select a time range near the end of the experiment\njcli> time $end-1h to +2m20s\n\n# select IF 0,1,2 with parallel hand polarizations\njcli> fq 0-2/p\n# equivalent, but would not work for XX, YY whereas the former would\njcli> fq 0-2/rr,ll\n\n# select sources whose name matches this\njcli> src j(19|30)*\n\n# select all cross baselines, remove those to stations xx and yy, but add xx-ef\njcli> bl cross -(xx|yy)* +xx(ef)\n\n# select 80% of the band, irrespective of how many channels\n# the correlator produced\njcli> ch 0.1*last:0.9*last\n\n# after running indexr, extract a bit of data (trimming 1 minute from either\n# end) from scans on sources matching 042* and who are longer than three minutes\njcli> scan start+1m to end-1m where length>3m and field ~ '042*'\n```\n\n# Dependencies\n\nThe package uses the [pyrap, python casacore](https://github.com/casacore/python-casacore)\nPython binding to access data.\n\nIt uses pgplot to visualize (it was faster and easier than matplotlib):\n[Python binding to pgplot](https://github.com/haavee/ppgplot) (the github version is preferred over this old link: http://www.jive.eu/~verkout/ppgplot-1.4.tar.gz)\n\nThe github version became online during the course of 2018 and has a `setup.py` which has support for Python2 and 3, where the `ppgplot-1.4.tar.gz` lacks this.\n\nNote: if the original `PGPLOT` is giving too many headaches, the [Giza](https://github.com/danieljprice/giza) library can be used as drop-in replacement for `ppgplot` to link against for its `libpgplot.so`. My [ppgplot fork](https://github.com/haavee/ppgplot)'s `setup.py` has support for having both FORTRAN PGPLOT and Giza installed and allows for compile-time selection of which *actual* pgplot backend to use.\n\n\n# Singularity and Docker container images\n\nAs of 30 October 2018 [Singularity](https://www.sylabs.io/) and [Docker](https://www.docker.com/) images are available. In fact, the Singularity image just runs the Docker image. The [jiveplot Docker image](https://hub.docker.com/r/haavee/jiveplot/) contains `jiveplot` and all its dependencies and is built on top of the excellent [kernsuite/kern-4](http://kernsuite.info) project.\n\nEven though all functionality is in the Docker image, we advise to run/install Singularity (if you have a choice) for the following reasons:\n\n- X11 forwarding works out of the box with Singularity, which is convenient if you wish to actually *see* the plots on your screen. According to the interwebs X forwarding can be done through Docker as well but it didn't for me (see below)\n\n- Your `${HOME}` directory is visible by default inside the Singularity container. This has the nice effect that your `jiveplot` command history and aliases are persisted between runs of the image (`~/.jcli.history` for the history). This in turn means that `^r` (reverse-search-history) is actually useful\n\n- I'm not even going to mention the security issues of Docker which has to run as root\n\n### Running the Singularity image\n\n*UPDATE* November 2019 - because of [Singularity security\nchanges](https://singularityhub.github.io/singularityhub-docs/2019/security-release/#api-access)\nit is now recommended to use the following method of running the jiveplot\ncontainer:\n\n```bash\n$ singularity pull shub://haavee/jiveplot:latest\n# this will give you a local `path/to/*.simg` file\n$ singularity run --bind <local dir>:<container dir> path/to/*.simg\n```\n\nwhere `<local dir>` is the/a directory on your host where your CASA\nMeasurementSet(s) live and `<container dir>` is the desired mount point\n_inside_ the container.\n\n### Running the Docker image\n\nAllegedly, running Docker like this:\n```bash\n$ docker run -it --init --network=host -v /tmp/.X11-unix:/tmp/.X11-unix:ro -e DISPLAY=\"$DISPLAY\" -v <local dir>:<container dir> haavee/jiveplot\n```\ndoes X11 forwarding but yours truly has seen it also *not* work. 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