Integrating Forced Photometry Service Queries
---------------------------------------------
The base TOM Toolkit comes with `ATLAS `__,
`PanSTARRS `__,
and (coming soon) ZTF query services. These services are optional and require additional configuration
integrate into your TOM.
Additional services can be added by extending the base ``ForcedPhotometryService`` implementation
(:ref:`see below`).
Integrating existing Forced Photometry Services
###############################################
You must add certain configuration to your TOM's ``settings.py`` to setup the existing forced
photometry services. This configuration will go in the ``FORCED_PHOTOMETRY_SERVICES`` section
shown below:
.. code:: python
FORCED_PHOTOMETRY_SERVICES = {
'ATLAS': {
'class': 'tom_dataproducts.forced_photometry.atlas.AtlasForcedPhotometryService',
'url': "https://fallingstar-data.com/forcedphot",
'api_key': os.getenv('ATLAS_FORCED_PHOTOMETRY_API_KEY', 'your atlas account api token')
},
'PANSTARRS': {
'class': 'tom_dataproducts.forced_photometry.panstarrs_service.panstarrs.PanstarrsForcedPhotometryService',
'url': 'https://catalogs.mast.stsci.edu/api/v0.1/panstarrs', # MAST Base URL
# MAST_API_TOKEN is not required for public data
'api_key': os.getenv('MAST_API_TOKEN', 'MAST_API_TOKEN not set')
},
# TODO: coming soon...
# # 'ZTF': {
# }
}
DATA_PRODUCT_TYPES = {
...
'atlas_photometry': ('atlas_photometry', 'Atlas Photometry'),
'panstarrs_photometry': ('panstarrs_photometry', 'PanSTARRS Photometry'),
...
}
DATA_PROCESSORS = {
...
'atlas_photometry': 'tom_dataproducts.processors.atlas_processor.AtlasProcessor',
'panstarrs_photometry': 'tom_dataproducts.processors.panstarrs_processor.PanstarrsProcessor',
...
}
As you can see in the ``FORCED_PHOTOMETRY_SERVICES`` configuration dictionary above, some services require an API key.
Information on how to obtain an API key is available for both for `ATLAS `_
and for `PanSTARRS `_. (PanSTARRS Forced Photometry is accessed via `Catalogs.MAST `_).
Configuring your TOM to serve tasks asynchronously:
***************************************************
Several of the services are best suited to be queried asynchronously, especially if you plan to make large
queries that would take a long time. The TOM Toolkit can be setup to use `dramatiq `_
as an asynchronous task manager, and doing so requires you to run either a `redis `_
or `rabbitmq `_ server to act as the task queue. To use dramatiq with
a redis server, you would add the following to your ``settings.py``:
.. code:: python
INSTALLED_APPS = [
...
'django_dramatiq',
...
]
DRAMATIQ_BROKER = {
"BROKER": "dramatiq.brokers.redis.RedisBroker",
"OPTIONS": {
"url": "redis://your-redis-service-url:your-redis-port"
},
"MIDDLEWARE": [
"dramatiq.middleware.AgeLimit",
"dramatiq.middleware.TimeLimit",
"dramatiq.middleware.Callbacks",
"dramatiq.middleware.Retries",
"django_dramatiq.middleware.DbConnectionsMiddleware",
]
}
After adding the ``django_dramatiq`` installed app, you will need to run ``./manage.py migrate`` once to setup
its DB tables. If this configuration is set in your TOM, the existing services which support asynchronous queries,
Atlas and ZTF, should start querying asynchronously. (Note: You must also start the dramatiq workers:
``./manage.py rundramatic``. If you do not add these settings, those services will still function but will fall
back to synchronous queries.
Adding a new Forced Photometry Service
######################################
The Forced Photometry services fulfill an interface defined in
`BaseForcedPhotometryService `_.
To implement your own Forced Photometry service, you need to do three things:
#. Subclass ``BaseForcedPhotometryService``
#. Subclass ``BaseForcedPhotometryQueryForm``
#. Subclass ``DataProcessor``
Once those subclasses are implemented, don't forget to update your settings for ``FORCED_PHOTOMETRY_SERVICES``,
``DATA_PRODUCT_TYPES``, and ``DATA_PROCESSORS`` for your new service and its associated data product type.
Subclass BaseForcedPhotometryService:
*************************************
The most important method here is the ``query_service`` method which is where you put your service's business logic
for making the query, given the form parameters and target. This method is expected to create a DataProduct in the database
at the end of the query, storing the result file or files. If queries to your service are expected to take a long time and
you would like to make them asynchronously (not blocking the UI while calling), then follow the example in the
`atlas implementation `_ and place your
actual asynchronous query method in your module's ``tasks.py`` file so it can be found by dramatiq. Like in the atlas implementation,
your code should check to see if ``django_dramatiq`` is in the settings ``INSTALLED_APPS`` before trying to enqueue it with dramatiq.
The ``get_data_product_type`` method should return the name of your new data product type you are going to define a
DataProcessor for. This must match the name you add to ``DATA_PROCESSORS`` and ``DATA_PRODUCT_TYPES`` in your ``settings.py``.
You will also need to define a
`DataProcessor `_
for this data type.
Subclass BaseForcedPhotometryQueryForm:
***************************************
This class defines the form users will need to fill out to query the service. It uses
`django-crispy-forms `_ to define the layout
programmatically. You first will add whatever form fields you need to the base of your
subclass, and then just fill in the ``layout()`` method with a django-crispy-forms layout
for your fields, and optionally the ``clean()`` method if you want to perform any field validation.
The values of the fields from this form will be available to you in your service class in the
``query_service`` method.
Subclass DataProcessor:
***********************
You must create a custom DataProcessor that knows how to convert data returned from your service into
a series of either photometry or spectroscopy datums. Without defining this step, your queries will still
result in a DataProduct file being stored from the service's ``query_service`` method, but those files will
not be parsed into photometry or spectroscopy datums. You can read more about how to implement a custom
DataProcessor `here <./customizing_data_processing.html>`_.