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Introduction to Object-Oriented Programming in Astro-WISE: Code Adaptation and Implementation

This tutorial from the Astro-WISE 2008 workshop in Leiden provides a comprehensive introduction to Object-Oriented Programming (OOP) concepts specifically tailored for astronomical data processing. Participants will learn about coding adaptations, how to create classes and objects that model real-world phenomena, and the advantages of employing OOP, such as code reuse and improved readability. The session covers essential OOP principles like inheritance and polymorphism, offering practical examples and insights into structuring code effectively for scientific applications.

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Introduction to Object-Oriented Programming in Astro-WISE: Code Adaptation and Implementation

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  1. Adapting Code • Introduction to OOP • Code, what code? What & where • Why adapt code anyway? • How? Astro-WISE Tutorial, Leiden 18-20 August 2008

  2. Introduction to OOP (1) • OOP is programming with classes • Programming in the problem domain • Describe a problem in one sentence, then the nouns are the classes/objects: • Let’s reduce a bias of ccd50 of the WFI instrument • Classes are blue-prints describing real world “things” • Can also map to abstract “things” Astro-WISE Tutorial, Leiden 18-20 August 2008

  3. Introduction to OOP (2) • Advantages of using OOP: • Reuse of code • Readability of code • Bug tracking • Important concepts: • Inheritance • Polymorphism Astro-WISE Tutorial, Leiden 18-20 August 2008

  4. Structure of a class, members class Cat: age = 3.0 weight = 5.2 def miaw(self): print ‘Miaw!’ def kill_other_cat(self, other): .... attribute method object awe> cat = Cat() awe> cat.age 3.0 awe> cat.age = 5.0 awe> cat.age 5.0 Astro-WISE Tutorial, Leiden 18-20 August 2008

  5. Inheritance class Mammal: weight = -1 def breathe(self): .... class Cat(Mammal): def miaw(self): print ‘Miaw!’ def kill_other_cat(self, other): .... inheritance cat = Cat() cat.breate() Astro-WISE Tutorial, Leiden 18-20 August 2008

  6. Polymorphism class Mammal: def swim(self): .... polymorphism class Cat(Mammal): def swim(self): # head up, move paws class Dolphin(Mammal): def swim(self): # head down, move fins class Toddler(Mammal): def yell(self, txt): print txt.upper() + ‘!!’ def swim(self): self.yell(‘I can’t swim’) Astro-WISE Tutorial, Leiden 18-20 August 2008

  7. Getting a checkout • CVS checkout = local copy of the code • Two versions: AWBASE and “most recent’’ • Must use “pserver” to log in anonymously • User anoncvs, password <ask representative, on request> >cvs –d cvs.astro-wise.org:/cvsroot co awe >cvs -d :pserver:anoncvs@cvs.astrowise.org:/cvsroot login ># base version >cvs -d :pserver:anoncvs@cvs.astro-wise.org:/cvsroot checkout -r AWBASE awe Astro-WISE Tutorial, Leiden 18-20 August 2008

  8. Using your own checkout • Set environment AWEPIPE to point to the checkout directory (“awe”) > setenv AWEPIPE /data/users/helmich/awe (add to .cshrc) Astro-WISE Tutorial, Leiden 18-20 August 2008

  9. Why adapt code? • Hopefully not often necessary • Make scripts to speed up process, and as a memory tool • Create objects with your own methods Astro-WISE Tutorial, Leiden 18-20 August 2008

  10. Directory structure • Top level: awe • common, astro, lofar, ai • Isolate specific code Astro-WISE Tutorial, Leiden 18-20 August 2008

  11. Directory structure: common config | Startup and environment/configuration filesdatabase | Persistency mechanism and implementations of its interfacelog         | Logging and messagingmath        | Mathematical Python routines such as statistics & least squaresnet         | General network-related python modulesservices    | Common (web-) servicestoolbox     | Scripts for e.g. maintenance of the database, installation, etc.util        | Routines for compression, checksumming, datetime manipulation. Astro-WISE Tutorial, Leiden 18-20 August 2008

  12. Directory structure: astro config | Startup and environment/configuration filesdatabase      | Astronomy specific database modulesexperimental | Experimental modulesexternal      | Python wrappers for tools such as LDAC, Sextractor and Swarpfilerecipes   | Data reduction recipes for use without databaseinstrument    | Instrument specific modulesmain          | Modules for the pipeline processing of imagesplot          | Various plotting modulesrecipes       | Data reduction recipes (to create classes in "main")services      | (Web-) services for astronomytest          | Unitteststoolbox       | Scripts for ingestion, installation, various maintenanceutil          | Utility modules usable throughout the code Astro-WISE Tutorial, Leiden 18-20 August 2008

  13. Making a script (1/2) • Based around dpu instance • Based around a task • As a sort of task • Write the script in a file with .py extension • Run with this command: > awe script.py Astro-WISE Tutorial, Leiden 18-20 August 2008

  14. Making a script (2/2) • Classes/models need to be imported • Dotted (directory) structure: • from astro.recipes.Reduce import ReduceTask Astro-WISE Tutorial, Leiden 18-20 August 2008

  15. Script based around dpu from astro.recipes.mods.dpu import Processor from astro.main.RawFrame import RawBiasFrame dpu = Processor('dpu.hpc.rug.astro-wise.org') ccd50 = [r.filename for r in RawBiasFrame.select(date='2000-04-28', instrument='WFI', chip='ccd50')] ccd51 = [r.filename for r in RawBiasFrame.select(date='2000-04-28', instrument='WFI', chip='ccd51')] dpu.run('Bias', instrument='WFI', raw_filenames=ccd50) dpu.run('Bias', instrument='WFI', raw_filenames=ccd51) Astro-WISE Tutorial, Leiden 18-20 August 2008

  16. Script based around Task • In particular to store configuration from astro.recipes.GalPhot import GalPhotTask from astro.util.Pars import Pars p=Pars(GalPhotTask) p.GalPhotModel.process_params.r1 = 10.0 p.GalPhotModel.process_params.dangmax=0.2 # etc. etc. task = GalPhotTask(instrument='WFI', slid=423431, sids=[16246], pars=p.get(), commit=0) task.execute() Astro-WISE Tutorial, Leiden 18-20 August 2008

  17. Simple eclipse script • Eclipse is used for image arithmetic import eclipse import glob filenames = glob.glob('*.red.fits') images = [eclipse.image.image(filename) for filename in filenames] cube = eclipse.cube.cube(images) median = cube.median() median.save('eclipse_med.fits') Astro-WISE Tutorial, Leiden 18-20 August 2008

  18. Adapting classes • ProcessTarget classes are located in astro.main • Lets look at BiasFrame: class BiasFrame(BaseFrame): .... def make_image(self) .... cube = eclipse.cube.cube([raw.image for raw in self.raw_bias_frames]) # current method does an average with sigma rejection self.image = cube.median() # use a median instead Astro-WISE Tutorial, Leiden 18-20 August 2008

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