# Python Notebooks

There is a separate Lasair repo for jupyter notebooks. To begin, lets the repo with:
```
git clone https://github.com/lsst-uk/lasair-examples.git
```
One branch is a set of notebooks showing how the Lasair client works (the API), and the other is a "Marshall" to enable viewing, vetoing and favouriting objects that pass through a kafka-enabled filter.

## Clone the repo and get your token
* You need a Lasair login.  There is a video [How to get a Lasair account](https://www.youtube.com/watch?v=ekjl5DpLV_Q) that explains how to do this, or just go [here]({%lasairurl%}/register). Then log in to the Lasair website.
* Click on your username at the top right and select "My Profile", then copy the token.
* You can go to `notebooks/API_ztf` or `notebooks/marshall`.
* Copy the `settings_template.py to `settings.py` then change the `API_TOKEN` to your own token, that you can find in your "profile" at the top-left of the Lasair web page.
* Install the lasair client with `pip3 install lasair`.

## API_examples

The notebooks are:
### Using the query and lightcurve methods
#### Cone_Search.ipynb
Uses the Lasair cone_search method to find objcts near a given point in the sky.

#### BrightSNe.ipynb
Runs a query on the Lasair objects table and the Sherlock table to extract possible supernovae, then fetches lightcurves and plots them.

#### Cone_Search.ipynb
Shows the behaviour of the cone search method of the Lasair API.

#### TDE_candidates.ipynb
A sophisticated gathering of information from Lasair, the Transient Naming Service, and PanStarrs cutout images.

#### Get_Watchlist_and_Area_Hits.ipynb
Shows how to use the Lasair API query method to search the objects, a watchlist, and a watchmap jointly.

#### Query_Watchlist.ipynb
A simple notebook to jointly query objects and a watchlist.

Other notebooks cover the use of the Kafka consumers.

Usage of the Lasair API is throttled by default to 10 requests per hour. If you get an error message about this, you can [email Lasair-help](mailto:lasair-help@mlist.is.ed.ac.uk?subject=throttling problem), explain what you are doing, and you will be put into the “Power Users” category. 

Please also [Contact us](mailto:lasair-help@mlist.is.ed.ac.uk?subject=Notebooks) with any notebooks that you would like to share.

## Marshall Notebook

This Jupyter notebook allows you to view the output from a Lasair filter, to link to more information, and to either make it a favourite or veto it so it won't be shown again.
The brief instructions for using the Marshall are [at the github page](https://github.com/lsst-uk/lasair-examples/tree/main/notebooks/marshall), and 
[there is a video](https://youtu.be/sgH5cQk-TDU) about how to use it.