"Python extension module that provides an efficient interface
between ROOT and NumPy.
root_numpy’s internals are compiled C++ and can therefore handle large amounts
of data much faster than equivalent pure Python implementations.
With your ROOT data in NumPy form, make use of NumPy’s broad library, including fancy indexing,
slicing, broadcasting, random sampling, sorting, shape transformations, linear
algebra operations, and more. See this tutorial to get started.
NumPy is the fundamental library of the scientific Python ecosystem. Using
NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert
your TTrees into NumPy arrays and use SciPy for
numerical integration and optimization, matplotlib
for plotting, pandas for data analysis,
statsmodels for statistical modelling,
scikit-learn for machine learning, and perform
quick exploratory analysis in a Jupyter notebook.
At the core of root_numpy are powerful and flexible functions for converting
ROOT TTrees into
structured NumPy arrays as well as converting
NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings
and basic types such as bool, int, float, double, etc. as well as
variable-length and fixed-length multidimensional arrays and 1D or 2D vectors
of basic types and strings. root_numpy can also create columns in the output
array that are expressions involving the TTree branches."
Where the chairs are arranged with exquisite precision, and the rosin bag is always full. Or perhaps (yet) another attempt to keep track of those things of which we think we need to keep track.
Tuesday, February 28, 2017
root_numpy
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