From 51ad307072458941912bc0da1290ef21f3867d8b Mon Sep 17 00:00:00 2001 From: eduardo-rodrigues Date: Thu, 22 Nov 2018 09:43:59 +0100 Subject: [PATCH] Added info on ostap --- README.md | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 50c9f56..c237af3 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,7 @@ Some more advanced talks of interested: * David Beazley, "Discovering python" https://www.youtube.com/watch?v=RZ4Sn-Y7AP8. * Thomas Ballinger, "Finding closure with closures" https://www.youtube.com/watch?v=E9wS6LdXM8Y + ## Getting Python | Name | Use | @@ -81,24 +82,18 @@ General information through talks tthat maybe useful on PyData (various confere * Enthought: https://www.youtube.com/user/EnthoughtMedia * Continuum Analytics: https://www.youtube.com/channel/UCND4vKhJssAtK8p1Blfj14Q + ## Particle Physics packages | Name | Use | | ------------ | --------------- | | [numpythia](https://github.com/scikit-hep/numpythia) | Interface between FastJet and NumPy. | | [pyjet](https://github.com/scikit-hep/pyjet) | Interface between PYTHIA and NumPy. | -## Jupyter extensions - -Jupyter has a wide ecosystem of extensions that can be used to extend the functionality. Some useful extensions for HEP data analysis are summarised here. - -| Name | Use | -| ------------ | --------------- | -| nbdime | Simplifies diffing and merging of jupyter notebooks that are stored in version control. | -| jupytext | Splits notebooks into a `.ipynb` and `.py` file for easier version control and to allow them to be run as scripts idependently of jupyter. | ## Tutorials -See tutorials here and other resources collected by [IML HEP-ML Resources](https://github.com/iml-wg/HEP-ML-Resources#tutorials) +See tutorials here and other resources collected by [IML HEP-ML Resources](https://github.com/iml-wg/HEP-ML-Resources#tutorials). + ## ROOT and interoperability with ROOT @@ -106,6 +101,7 @@ For many particle physics experiments, a lot of data is stored within ROOT files | Package name | Use | Pro | Con | Further information | | ------------ | --- | --- | --- | ------------------- | +| [ostap](https://github.com/OstapHEP/ostap) | User-friendly & more intuitive interface to(Py)ROOT | Many decorations to ROOT classes | Requires C++ code compilation | | | [uproot](https://github.com/scikit-hep/uproot) | Native Python ROOT I/O | Easy to install, fast, no dependence on C++ ROOT | Although can read all ROOT files, can only write ROOT files with specific objects. | | | root_numpy, root_pandas | ROOT to/from Numpy and Pandas, like uproot | full ROOT functionality, like TFormula | slower than uproot, binary incompatibilities with different versions of ROOT | | | [ROOT conda](https://nlesc.gitbooks.io/cern-root-conda-recipes/content/installing_root_via_anaconda.html) | Using ROOT within Anaconda | Easy to get ROOT installed with PyROOT support | Not all features of ROOT and getting dated (6.04 Py2.7/3.4 since XENON1T uses that) | [Recipes](https://github.com/NLeSC/root-conda-recipes) | @@ -115,6 +111,16 @@ For many particle physics experiments, a lot of data is stored within ROOT files | [pyhf](https://github.com/diana-hep/pyhf) | statistical analysis / fitting | pure python implementation of HistFactory specification with auto-diff enabled backends in tensorflow, pytorch, and MXNet | not yet interoperable with ROOT-based RooFit models | [pyhf](https://github.com/diana-hep/pyhf) | +## Jupyter extensions + +Jupyter has a wide ecosystem of extensions that can be used to extend the functionality. Some useful extensions for HEP data analysis are summarised here. + +| Name | Use | +| ------------ | --------------- | +| nbdime | Simplifies diffing and merging of jupyter notebooks that are stored in version control. | +| jupytext | Splits notebooks into a `.ipynb` and `.py` file for easier version control and to allow them to be run as scripts idependently of jupyter. | + + ## Speeding up code Often, it is not needed anymore to write C++/C routines that get wrapped since there are other ways to speed up your Python code. Namely: @@ -126,6 +132,7 @@ Often, it is not needed anymore to write C++/C routines that get wrapped since t | [numpy](http://www.numpy.org) | Expressing your code as array options means you get native-C speeds. | | NumExpr | single pass "mapper" operations (one input → one output). | + ## Binding C/C++ to Python *Before you read this, realize that this is for existing C++ code. If you want to write new C/C++ code for speed, see section above.* @@ -145,6 +152,7 @@ At present, the best summary of how to bind code in HEP applications comes from | swig | Wrapping C++ code | Widely used. | Have to write wrapper file and feels dated. | | | Boost | Wrapping C++ code | Widely used. | Giant dependency since Boost does many other things.| | + ## Experimental codes Stealing code from other physicists is its own sign of flattery. Codes that are abandoned more than two years will get struck through.: