Comparison with Boost.Histogram#
boost-histogram was based on the C++ library Boost.Histogram. In most ways,
it mimics the spirit of this library; if you learn to use one, you probably can use
the other. There are a few differences, however, mostly around adhering to Python
There are a few parts of the Boost.Histogram interface that are not bound. They are:
- The call operator
This is provided in C++ to allow single item filling, and was designed to mimic the accumulator syntax used elsewhere in Boost. It also works nicely with some STL algorithms. It was not provided in Python because using call to modify an object is not common in Python, using call makes duck-typing more dangerous, and single-item fills are not encouraged in Python due to poor performance. The
.fillmethod from Boost.Histogram 1.72 is bound, however - this provides fast fills without the drawbacks. If you want to fill with a single item, Python’s
.filldoes support single item fills.
- Histogram make functions
These functions, such as
make_weighted_histogram, are provided in Boost.Histogram to make the template syntax easier in C++14. In C++17, they are replaced by directly using the
histogramconstructor; the Python bindings are not limited by old templating syntax, and choose to only provide the newer spelling.
- Custom components
Many components in Boost.Histogram are configurable or replaceable at compile time; since Python code is precompiled, a comprehensive but static subset was selected for the Python bindings.
The bindings follow modern Python conventions, with CamelCase for classes, etc. The Boost.Histogram library follows Boost conventions.
The Python bindings use a pickle-based binary serialization, so cannot read files saved in C++ using Boost.Serialize.
Many methods in C++ are properties in Python.
.axis(i)is replaced with
The Python bindings use standard Python indexing for selection and setting. You can recover the functionality of
.at(i)at endpoints with
.rank()method is replaced by the
.ndimproperty to match the common NumPy spelling.
- Unified Histogram Indexing
The Python bindings support UHI, a proposal to unify and simplify histogram indexing in Python.
- Custom transforms
Custom transforms are possible using Numba or a C pointer. In Boost.Histogram, you can use templating to make arbitrary transforms, so a run time transform is not as necessary (but may be added).
- NumPy compatibility
The Python bindings do several things to simplify NumPy compatibility.