3. Definitions

When a reduction is launched with reduce (command line) or Reduce (Python class), the Recipe System will identify the nature of the inputs using the AstroData tags, and then start searching for the most appropriate, or the requested, recipe and primitives.

The Recipe System will search the active data reduction package (geminidr or as specified by the --drpkg option) for recipe libraries and primitive sets matching the inputs. The recipe library search is limited in scope by the mode option.

Once everything has been found, the default or specified recipe from the selected recipe library is given the primitive set as input. The recipe is run and the sequence of primitive calls is executed.

Below, we discuss each of the terms in bold italics from the execution summary above: “AstroData tags”, “mode”, “recipe”, “recipe library”, “primitive”, “primitive set”.

3.1. AstroData Tags

The AstroData Tags are data identification tags. When a file is opened with AstroData, the software loads the AstroData configuration modules and attempts to identify the data.

The tags associated with the dataset are compared to tags included in recipes and in primitive classes. The best match wins the selection process.

For Gemini instruments, the AstroData configurations are found in the gemini_instruments package. This is set as the default. Which configuration package to use can be configured on the reduce command line or in the Reduce class.

More information on AstroData tags can be found in the Astrodata User Manual.

3.2. Mode

The mode defines the type of reduction one wants to perform: science quality (“sq”), quick look reduction (“ql”), or quality assessment (“qa”). Each mode defines its own set of recipe libraries. The mode is switched through command line flags or the Reduce class mode attribute.

If not specified, the default is science quality, “sq”. Currently, only science quality, quick look, and quality assessment are supported. Users cannot select other modes.

Recipe libraries of the same name but assigned different mode are often very different from each other since the products are expected to be different.

The quality assessment mode, “qa”, is used mostly at the Observatory, at night to measure sky condition metrics and provide a visual assessment of the data. It does not require calibrations since we might not have all the calibrations needed at the time that the data was obtained.

The quick look mode, “ql”, is intended for quick, close to but not necessarily science quality reduction. The objective as the name entails being to do a quick and automatic reduction for quick scientific and technical evaluation of the data. This mode does not require calibrations either, but both QA and QL modes can use calibrations if they are found.

The science quality mode, “sq”, the default mode, is to be used in most cases. The recipes in “sq” mode contain all the steps required to fully reduce data without cutting corners. Some steps can be lengthy, some steps might offer an optional interactive interface for optimization. This mode requires all the calibrations and will return an error in case some are not found.

It is important to notice that a calibration processed with a lower quality mode cannot be used by a higher quality mode (sq > ql > qa). For example, a quicklook calibration cannot be used for science reduction, but a science quality calibration can be used for a quicklook reduction.

3.3. Recipe

A recipe is a sequence of data processing instructions. Technically, it is a Python function that calls a sequence of primitives, each primitive nominally designed to do one specific transformation or service request.

Below is what a recipe can look like. This recipe performs the standardization and corrections needed to convert the raw input science images into a stacked image. The argument, p, to the reduce recipe is the primitive set; the recipe can call any primitives from that set.

def reduce(p):

The guiding principle when building a recipe is to keep it human readable and scientifically oriented.

3.4. Recipe Library

A recipe library is a collection of recipes that applies to a specific type of data. The AstroData tags are used to match a recipe library to a dataset. A recipe library is implemented as Python module. There can be many recipes but only one is set as the default. It is however possible for the user to override the default and call any recipe within the library.

3.5. Primitive

A primitive is a data reduction step involving a transformation of the data or providing a service. By convention, the primitives are named to convey the scientific meaning of the transformation. For example biasCorrect will remove the bias signal from the input data.

A primitive is always a member of a primitive set. It is the primitive set that gets matched to the data by the Recipe System, not the individual primitives.

Technically, a primitive is a method of a primitive class. A primitive class gets associated with the input dataset by matching the AstroData tags. Once associated, all the primitives in that class, locally defined or inherited, are available to reduce that dataset. We refer to that collection of primitives as a “primitive set”.

3.6. Primitive Set

A primitive set is a collection of primitives that are applicable to the input dataset. The association of the primitive set to the data is done by matching AstroData tags. It is a primitive set that gets passed to the recipe. The recipe can use any primitive within that set.

Technically, a primitive set is a class that can have inherited from other more general classes. In geminidr, there is a large inheritance tree of primitive classes from very generic to very specific. For example, the primitive set for GMOS images defines a few of its own primitives and inherits many other primitives from other sets (classes) like the one for generic CCD processing, the one related to photometry, the one that applies to all Gemini data, etc.