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A first general restructuration of the doc according to the pattern [tour|tutorial|reference]. In the reference part, objects are documented per topic. In each topic, [definition|c++|python|hdf5] (not yet implemented)
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3.1 KiB
ReStructuredText
96 lines
3.1 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**, and hence
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without adding a new layer of tools (which means new things to learn,
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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 solve 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`` contains various pieces of information
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configured automatically at compile time ::
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from pytriqs.version import *
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version # The version of the TRIQS library
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release # The release number of the library
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git_hash # The git commit used at compilation
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# publishing this information may lead to a security issue....
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show_machine_info() # Hostname and login of the compilation
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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.release
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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.release
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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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