anthrax3 / histogrammar-python

Python implementation of Histogrammar, with embedded front-ends and back-ends.

Python implementation of Histogrammar

See histogrammar.org for a complete introduction to Histogrammar.

This is a pure Python implementation for Python versions 2.6, 2.7, and 3.4.

Installation

Histogrammar has a standard Python install script. Run

python setup.py install

with sudo if superuser permissions are needed or --home=~ to install in a home directory. The latter option requires an appropriate PYTHONPATH.

This package has no explicit dependencies, though it can use back-ends like Numpy and front-ends like Matplotlib and Bokeh if they are on your system. If not, you'll get an ImportError at the time you try to call these methods.

Status

The tests are thorough.

• basic.py were written during development to test all features as they were added.
• testnumpy.py tests numerical agreement between the conventional implementation and the Numpy implementation, which are very different. Also tests much larger datasets and infinity/NaN handling.
  • contrary to its name, testnumpy.py also compares its implementation with the literal code given in the specification as well.
• testrootcling.py applies all of the Numpy tests to the ROOT/Cling implementation.
• testgpu.py applies all of the Numpy tests to the CUDA GPU implementation.

Primitive implementation is mature. CUDA implementation has begun.

Primitive	Pure Python	Numpy	ROOT JIT	CUDA GPU
Count	done	done	done	done
Sum	done	done	done	done
Average	done	done	done	done
Deviate	done	done	done	done
Minimize	done	done	done	done
Maximize	done	done	done	done
Bag	done	done	done	impossible
Bin	done	done	done	done
SparselyBin	done	done	done	impossible
CentrallyBin	done	done	done	done
IrregularlyBin	done	done	done	done
Categorize	done	done	done	impossible
Fraction	done	done	done	done
Stack	done	done	done	done
Select	done	done	done	done
Label	done	done	done	done
UntypedLabel	done	done	done	done
Index	done	done	done	done
Branch	done	done	done	done

• "impossible" for CUDA GPU means that the primitive requires a non-constant memory allocation (hashmaps in all three cases). There must be ways of doing this by preallocating more space than is needed, but I'm not going to get into that for this round.