schedview

Tools for visualizing Rubin Observatory scheduler behaviour and LSST survey status

Development state

The current code is in a early stage of development. The architecture will be documented by RTN-037, currently little more than an outline.

Applications

There are presently two different applications in this project:

• metric_maps, a tool for visualizing metric output of MAF that takes the form of a sky map.
• sched_maps, a tool for examing some elements of the state of objects used by the scheduler, particularly those that take the form of a sky map, although some indications of other elements are also present.
• prenight, a pre-night briefing dashboard.

The project contains example data for each. At present, to use the example data, different versions of dependencies are required, so the installation instructions are slightly different in each case. (The reason for this is that the pickles containing the sample objects to be viewed with sched_maps were created with an old version of rubin_sim, and this older version needs to be installed for these to be loaded.)

Installation

First, get the code by cloning the github project:

$ git clone git@github.com:ehneilsen/schedview.git

Go to the development directory, and download and decompress a data file used by the automated tests.

$ cd schedview
$ wget -O schedview/data/bsc5.dat.gz http://tdc-www.harvard.edu/catalogs/bsc5.dat.gz
$ gunzip schedview/data/bsc5.dat.gz

Create a conda environment with the appropriate dependencies, and activate it. If you are running the metric_maps application, use the conda environment file that includes a recent version of rubin_sim:

$ # ONLY IF RUNNING metric_maps
$ conda env create -f environment.yaml
$ conda activate schedview

If you are running sched_maps, you may need to modify the environment to one compatible with whatever instance of the scheduler you will be viewing.

Install the (development) schedview in your new environment:

$ pip install -e .

Run the tests:

$ pytest

Running metric_maps

Activate the environment, and start the bokeh app. If SCHEDVIEW_DIR is the directory into which you cloned the schedview github repository, then:

$ conda activate schedview
$ bokeh serve ${SCHEDVIEW_DIR}/schedview/app/metric_maps

The app will then give you the URL at which you can find the app. The data displayed with the above instructions will be the sample metric map in the project itself.

If you want to use a different data file, you can set the METRIC_FNAME to its path before running the bokeh app. This is only a very short term solution, and will be replaced soon.

Running sched_maps

Activate the environment, and start the bokeh app. If SCHEDVIEW_DIR is the directory into which you cloned the schedview github repository, then:

$ conda activate schedview
$ bokeh serve ${SCHEDVIEW_DIR}/schedview/app/sched_maps

The app will then give you the URL at which you can find the app.

Running prenight

Activate the environment, and start the bokeh app. If SCHEDVIEW_DIR is the directory into which you cloned the schedview github repository, then:

$ conda activate schedview
$ python ${SCHEDVIEW_DIR}/schedview/app/prenight/prenight.py