Ceres and stack classes: Observational data handling¶
Ceres¶
To interface with an observational series, Dorado offers the Ceres class. The Ceres class contains a list
of Stack objects and the coresponding metadata including Target objects associated with
the campaign. As stated before, Astronomical data is collected and stored in many different ways and formats;
Dorado aims to be an open and community friendly pipeline tool, to do this, dorado needs a modular and
personalizable way to integrate data into the dorado system.
Ceres objects are by convention stored in the core dorado instance and referenced by key strings for ease of use.
The first step to working with a ceres object is to import dorado .. code:: python
## import import dorado
You may want to store a target object associated with the campaign in the ceres instance. Lets quickly initialize one.
Next up comes initializing the ceres object into the core dorado instance. We will hand it an observation
date of 2021-01-01+02 which points dorado to a date folder with the same name in the target data folder.
In this case that folder has the path home/.dorado/data/raw/2021-01-01+02 with home/.dorado/data/raw/
being the default data path. We will nickname the ceres instance as ceres_name so we can later reference it
by its key string. Lastly, we will append our target object into the ceres instance.
lastly, we wish to work with our new ceres instance. To calibrate the red filter data within our ceres instance we can use the following.
Note
The above syntax for calibration is assuming that aicoPhot is set as the
active dorado.dorphot instance.
Stack¶
The Stack hosts idividual stacks of data in dorado such as data specific to a target
in a single filter. The reader class conventionally handles Stack instances by reading them into ceres
instances and labelling them; conventionally, Stack key strings are generated from the filter name
found in the data. In the above example, the filter name was R so we told the calibration method to
calibrate the R stack in the ceres_name ceres instance.
In the future, more advanced documentation on the stack class will be available for those wishing to produce custom pipeline classes or work with data in a more fundamental way.