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102 lines
3.6 KiB
ReStructuredText
102 lines
3.6 KiB
ReStructuredText
Reproducibility, provenance.
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=================================
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Scientific numerical calculations are ... scientific calculations.
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Hence, like any other kind of calculations, according to the basic principles of science,
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everyone should be able to reproduce them, reuse or modify them.
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Therefore, the detailed instructions leading to results or figures
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should be published along with them.
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To achieve these goals, in practice we need to be able to do simply the following things :
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* Store along with the data the version of the code used to produced them (or even the code itself !),
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and the configuration options of this code.
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* Keep with the figures all the instructions (i.e. the script) that have produced it.
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* We want to do that **easily, at no cost in human time**, hence
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without adding a new layer of tools (which means new things to learn, which takes time, etc...).
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Indeed this task is important but admittedly extremely boring for physicists...
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Fortunately, python helps solving these issues easily and efficiently.
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TRIQS adds very little to the standard python tools here.
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So this page should be viewed more as a wiki page of examples.
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TRIQS does not impose any framework on you, it just provides tools
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and lets you organize your work as you wish.
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TRIQS code version
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----------------------
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The tiny module `pytriqs.version`
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contains various pieces of information configured automatically at compile time ::
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import pytriqs.version as V
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V.revision # The branch and the git_hash commit number or version number
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V.git_hash # The commit number if under git, or ""
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V.branch # the branch on which the code was compiled
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# publishing these informations may lead to a security issue....
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V.cmake_options() # returns the contents of the CMakeCache.txt file
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V.machine_info # Hostname and login of the compilation
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V.all_info # a print of most info (except Cache)
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.. warning::
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While the informations of the configurations, the machine, and login may be useful for *you* to
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reproduce the computation, publishing them is not recommended since it contains informations on your machine, your login.
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For public data, just use V.revision.
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Saving the script in the data archive
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-------------------------------------------
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It is actually very simple to save the script
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(hence the parameters) along with the data,
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simply by putting it in the HDFArchive, e.g. ::
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# ... computation ...
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Results = HDFArchive("solution.h5",'w')
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Results["G"] = S.G # save the results
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import sys, pytriqs.version as version
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Results.create_group("log")
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log = Results["log"]
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log["code_version"] = version.revision
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log["script"] = open(sys.argv[0]).read() # read myself !
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The script that is currently being executed will be copied into the file `solution.h5`, under the subgroup `/log/script`.
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In a more complex situation, you may decompose your computation in several scripts, e.g.
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* A script common.py, with some common functions, classes...
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* A little one, computation1.py for each computations.
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In such situation, one can simply use the `inspect` module of the python standard library e.g. ::
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import common
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# set parameters
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# run...
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# save...
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# Ok, I need to save common too !
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import inspect,sys, pytriqs.version as version
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log = Results.create_group("log")
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log["code_version"] = version.revision()
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log["script"] = open(sys.argv[0]).read()
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log["common"] = inspect.getsource(common) # This retrieves the source of the module in a string
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From the data to the figures
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-------------------------------------------
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[TO BE WRITTEN]
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