2. Overview

The Recipe System is a python package distributed as a component of the Gemini Observatory’s DRAGONS metapackage for data reduction. The Recipe System is a framework that supports configurable data processing pipelines and which can technically accommodate processing pipelines for arbitrary types of astronomical data.

The Recipe System provides infrastructure that will inspect DRAGONS-compatible configuration and data processing packages and map recipes and primitives to the input data. The infrastructure will then link the appropriate primitives to the appropriate recipe and run it. The Recipe System also includes tools and mechanisms to automatically handle and associate processed calibrations using the GeminiCalMgr python package, available separately.

The Recipe System matches and links recipes and primitives to the data, and execute the recipes. Any flow control and decisions are left to the recipes and primitives. The Recipe System only provides an automation infrastructure to run the reduction.

To match data to recipes and primitives, the Recipe System requires the data to be accessed with astrodata, the DRAGONS data access infrastructure. astrodata provides a common grammar to recognize and to access pixel and header data. The Recipe System depends critically on astrodata for recipe and primitive mapping which matches astrodata tags. (See the Astrodata documentation, Section 1.2 for more information.)

Gemini Observatory has developed a suite of recipes and primitives for the Observatory’s facility instruments. These can be found in the DRAGONS package geminidr. The Astrodata configuration package for the Gemini instruments is gemini_instruments. Both packages are included in DRAGONS.

At Gemini, DRAGONS and its Recipe System are used in an operational environment for data quality assessment at night. It provides sky condition metrics calculated from the data itself. DRAGONS and the Recipe System also are the data reduction platform that will replace Gemini’s legacy IRAF software.

Readers unfamiliar with terms and concepts presented in this manual can consult the Glossary for a definition of terms. For greater detail and depth, below are definitions of terms and components directly relevant to usage and development of the Recipe System.

2.1. Definitions

The following section provides definitions, discussion, and some examples about certain terms and components that are key to the functioning Recipe System.

Data Reduction Package (``drpkg``)

A data reduction package is a Python package in which data reduction software, formatted as recipes and primitives, is organized. Normally, recipes and primitives specific to an instrument are organized together in an “instrument package”. The primitives can be organized in any way that makes inheritance easy and practical. For example, in the Gemini data reduction package, geminidr, along with the instrument package, there is a gemini package for software that can apply to all or multiple instruments, and a core package for primitives that are generic, like for stacking or photometry. In the Recipe System, the default data reduction package is geminidr. This can be changed by setting the value of the drpkg attribute to the Reduce class or with the --drpkg options on the reduce command line.


Programmatically, the mode is a label – a string literal – by which recipe libraries are differentiated. The modes are represented in the data reduction package as subdirectory to the recipes directory in an “instrument package”. For example, mode sq is associated with the directory recipes/sq. Only the sq recipes will be found there. In principle, the mode names should indicate or hint at the recipes’ purpose or to the quality of the products.

The mode is an attribute of the Reduce class and can be set on the reduce command line with the --qa and --ql flags (“quality assessment” and “quicklook”, respectively) with the sq mode as the default. The mode specified for pipeline processing can be used as a flag in a primitive too, for example if a primitive is to behave differently depending on mode.

Recipes organized by modes can differ for whatever operational reasons you might have. The Gemini quality assessment recipes, mode qa, measure sky condition metrics at various stages of the reduction. That is not done in science quality reduction recipes, mode sq, but additional care is made to sky subtraction, for example. The Mode is therefore related to the desired product the recipe is to deliver.


A DRAGONS primitive is a method of a primitive class, also called a primitive set. Primitive sets can be associated to specific Astrodata tags that the Recipe System can match to data. A primitive is expected to be a meaningful data processing step and named appropriately, for example, biasCorrect will apply the bias correction. This is a guideline and the Recipe System has no technical requirements for this.

Primitive Class

The Recipe System matches the data to the most appropriate Primitive Class, also called Primitive Set. The association is made using the Astrodata tags of the input data, and the tagset attached to each primitive class. Each primitive class must have a tagset class attribute assigned with a Python set containing the relevant Astrodata tags, eg. tagset = set(["GEMINI", "GMOS"]). The class that matches the greatest number of tags wins the contest and gets selected.

The primitive classes can make use of inheritance and mix-ins to collect a complete set of primitives applicable to the data being reduced.


A recipe is a python function defined for specific instruments, instrument modes, and pipeline modes (see above, mode). A recipe function receives one parameter: an instance of a primitive class, also called a “primitive set”. The recipe can then use any primitives from that set.

The recipes are stored in a Recipe Library, a Python module, see below.

Recipe Library

A Recipe Library is a Python module that stores recipes. The Recipe Library is given a tagset for data for recipe mapping by the Recipe System. All the recipes in a Recipe Library must therefore apply to the same Astrodata tags. The Astrodata tagset is stored in the module variable recipe_tags.

Each library must have a recipe assigned as “default”. The module variable _default is set to the name of the default recipe.

The Recipe System finds the Recipe Library that best matches the data based on tagset and mode. Then it picks from it the default recipe, or the user-specified recipe from that library. The primitive set is selected and passed to the selected recipe, completing the mapping.


A Tagset is a Python set of Astrodata tags (see Astrodata documentation). A dataset opened with Astrodata will be recognized and assigned a set of tags. These tags are used by the Recipe System map the data to the most appropriate recipe library and the most appropriate primitive set.

A recipe library announces the tags it applies to with a tagset stored in the module recipe_tags variable. A primitive class uses the class attributr tagset to store the applicable tags. The Recipe System maps all the tagsets together to find the best mapping solution.

For example, a datasets is assigned the following tags:

>>> ad = astrodata.open('N20170609S0160.fits')
>>> ad.tags

The Recipe System will match that data to the recipe library and primitives with the following tags:

Recipe Library with: recipe_tags = set(['GMOS', 'IMAGE'])
Primitive Class with : tagset = set(["GEMINI", "GMOS", "IMAGE"])

Tagset matching by the Mapper classes are discussed in greater detail in subsequent chapters of this document, Chapter 3, The Mappers, and Chapter 4, Using The Mappers API.

2.2. Outline of the Recipe System

In this section we provide a functional overview of the Recipe System, and describe in more detail some of the key components of the complete reduction ecosystem.

2.2.1. Functional overview

The complete reduction ecosystem is represented in Figure 2.1 with emphasis on how the Recipe System automates and supports the data reduction. It illustrates the system’s relationship to instrument packages and the calibration manager.


Figure 2.1: Schematic Diagram of Recipe System Components and the supporting Calibration Request Service

Let us go through that diagram.

  1. The command line interface, reduce, provides users command access and execution from the terminal. (Reduce and Recipe System User Manual.)

  2. The Reduce class receives input datasets and parameters either from reduce or directly through the Reduce API (Reduce and Recipe System User Manual.). Reduce parses the input arguments and opens the input datasets. When run, it will send the first input to the Mappers.

  3. The Mappers, both RecipeMapper and PrimitiveMapper, conduct best matching tests on recipe libraries and primitive classes and return the best matched objects. The Astrodata tags and the recipe libraries and primitive classes tagsets are used to do the match.

  4. The instrument data reduction (DR) package is a collection of modules that provide data reduction classes (primitives) and recipe libraries, and any supporting software like lookup tables. The instrument DR packages are not part of the Recipe System, they are add-ons specific to the instruments being supported. The Recipe System probes and searches those packages for matching primitive sets and recipes. In DRAGONS, the instrument DR packages, and some generic primitive packages, are found under geminidr.

  5. The Calibration Request Service provides a functional interface between primitives requesting calibration files (biases, flats, etc.) and the designated calibration manager.

  6. The Calibration Manager is an independent component, not part of DRAGONS, that contains the calibration association rules and interacts with a database that stores the processed calibrations. It accepts calibration requests passed by the Calibration Request Service at the behest of the primitive calls. The Calibration Manager can be Local Calibration Manager distributed as GeminiCalMgr for use by individuals, or the Gemini internal facility calibration manager. The latter is for Gemini Operations needs only (for reference, Fitsstore). In either case, the data’s metadata are used, along with a set of rules, to determine a best available match for the requested calibration type and return a full path name (local) or a URL (internal Gemini manager) to the file.

It is worth noting that all components discussed here operate and communicate using the common grammar provided by the astrodata data abstraction.

2.2.2. reduce and Reduce

reduce is the easiest way to invoke the Recipe System. It passes command line options to the Reduce class, which then invokes the mappers. Those, in turn, use arguments to locate and identify the best applicable primitive classes and recipes. For most users, reduce will be the common way to process datasets with the Recipe System.

The Reduce class can be used directly for a programmatic invocation of the reduction rather than using the terminal.

Usage of both reduce and Reduce is documented in the Reduce and Recipe System User Manual.

The reduce script itself is really light weight and mostly just a wrapper around Reduce. It sets up a logger and then uses the same parser that Reduce also has access to, buildParser. Then it is off to Reduce to to run the show.

A Reduce instance can be created with or without arguments. The argument is a string representing the command line of reduce. When that argument is provided, Reduce will call buildParser on it. The instance attributes can also be set individually. When using the API, a logger must be set ahead of time, Reduce will not create one, yet it expects to be able to write to one. The main public method of Reduce is runr() which is responsible for applying the mapper-returned primitive instance to the mapper-returned recipe function, at which point, processing begins. Note that runr has logic to recognize the name of a primitive and to run that specific primitive rather than a recipe. Of course, the primitive will be coming from a tagset matching primitive set.

2.2.3. Mappers

The mapper classes are the core of the Recipe System and provide the means by which the Recipe System matches input datasets to processing routines. When applicable primitive classes and recipes are found, the mappers return objects of the appropriate kind to the caller; the PrimitiveMapper returns an instance of the applicable primitive class; the RecipeMapper returns the actual recipe function object from the applicable recipe library.

There are two functional mapper classes, RecipeMapper and PrimitiveMapper, which are subclassed on the base class, Mapper . These classes and their modules are located in recipe_system.mappers.

Mappers are discussed more fully in the next chapter.

2.2.4. Instrument Data Reduction Packages

The data reduction packages are not components of the Recipe System. They stand on their own. They provide the means, or instructions, for reducing data and, therefore, at least one such package is required for the Recipe System to function.

The data reduction packages provide some hooks that the Recipe System depends on to map recipes and primitives to the data.

Instructions on how to structure a data reduction package for use by the Recipe System are provided in appendix.

The primitive class signature must be able to accept this instantiation call:

primitive_actual(self.adinputs, mode=self.mode, ucals=self.usercals,
                 uparms=self.userparams, upload=self.upload)

 adinputs: Python list of AstroData objects
 mode:     One of 'sq', 'qa', or 'ql'
 ucals:    Python dict with format
              {(<data_label>, <type_of_calib>): <calib_filename>},
              one key-value pair for each input, with the type of
              calibration matching one from the keys in
 uparms:   Python dict with format ``{'<prim>:<param>': <value>}``
 upload:   Python list of any combination of 'calibs', 'metrics', or

In geminidr, the primitive classes use a decorator to process those inputs.

The recipes must be located in subdirectory named after the mode. For example:


While it is entirely possible to allow unrestricted naming of subpackages and modules within an instrument data reduction package, the Recipe System is optimized to search packages of a certain form. In particular, some optimization allows the mapping algorithms to bypass code defined in the lookups/ directory where Gemini puts static inputs like look-up tables and bad pixel masks. Because the Recipe System conducts depth-first searches, the optimization expedites mapping by known exclusion: bypassing subpackages and modules that are known not contain primitives or recipes.

Refer to the appendix for more a more complete discussion.

2.2.5. Calibration Request Service

The Calibration Request Service provides a functional interface to a local calibration manager (GeminiCalMgr), the Gemini Observatory facility calibration manager (fitsstore), or the Gemini Observatory Archive.

Primitives requiring processed calibration files (biases, flats, etc.) will use this functional interface to make calibration requests. These requests are served by the calibration manager in real time. This is a JIT (just in time) service. (See the Appendix for more information about why JIT calibration service is necessary.)

Calibration requests are built from the Astrodata descriptors and tags, and the requested calibration type (flat, dark, etc). The calibration request is processed by the calibration manager’s association rules to find the best match.

The structure of the calibration service is set up when the primitive class object is instantiated by reading the configuration file whose default location is ~/.dragons/dragonsrc. The service is constructed as a series of database instances, each of which is queried in turn via various get_processed_<caltype> methods, providing a suitable calibration for each requested file (and indicating which database successfully served the request) or passing the request onto the next database in the series.


The UserDB class handles the command-line parameters that define the user-defined calibrations, e.g., --user_cal processed_flat:myflat.fits. If a calibration has been defined for the type matching the request, then this calibration is returned.


The LocalDB class provides an interface to the LocalManager class which queries the sqlite database containing the locations of the calibration files that have been processed and stored by the user.


The RemoteDB class provides an interface to the Gemini internal fitsstore server by making an HTTP POST request. After determining the filename of a suitable calibration, it determines whether a file of that name and calibration type has already been cached on disk in a subdirectory of the calibrations directory created by DRAGONS, to avoid unnecessary repeated downloads.

Both the LocalDB and RemoteDB classes have the responsibility of verifying the md5 checksum of the file on disk with that stored in the database to ensure it hasn’t been corrupted. In the case of the local database, the file is not returned, while the remote database downloads the file again.

2.2.6. Calibration Manager

The Calibration Manager is an external component to the Recipe System and even DRAGONS itself. The Recipe System currently uses two types of calibration manager.

The original calibration manager is one used internally at Gemini. It is associated with a large database that stores the data too. For external users, a lightweight local calibration manager is available instead.

The local calibration manager uses a sqlite database to store the location information of the calibrations processed by the user. Since the data were processed locally, there is no need to store the data, just the name and the path to the data. The caldb facility is provided for the user to create and populate (or de-populate) the local database. At present, this limits the dragonsrc file to have only a single local database. This limitation may be relaxed in the future.

What both calibration managers share are the calibration associations rules, rules that will identify the best processed calibrations for a given Gemini observation. Those rules are the same as the rules used by the Gemini Observatory Archive. The internal database is in fact using exactly the same software as the GOA. The local calibration manager uses a subset of the code plus a couple extra routines.

The internal Gemini data manager is obviously very Gemini-centric, by necessity. The local calibration manager, distributed as GeminiCalMgr, is also, unfortunately still quite Gemini-centric. The ORMs are designed for Gemini data. It might be possible for a third-party to replace the ORMs and the calibration rules to match their data’s needs.