Skip to main content

Composable histogram primitives for distributed data reduction.

Project description

Histogrammar is a suite of data aggregation primitives designed for use in parallel processing. In the simplest case, you can use this to compute histograms, but the generality of the primitives allows much more.

See http://histogrammar.org for a complete introduction.

This Python implementation of Histogrammar adheres to version 1.0 of the specification and has been tested to guarantee compatibility with the Scala implementation. The test suite includes empty datasets, NaN/infinity handling, associativity tests, and numerical agreement at the level of one part in a trillion (double precision). Several common histogram types can be plotted in Matplotlib, PyROOT, and Bokeh with a single method call.

If Numpy or Pandas is available, histograms and other aggregators can be filled from arrays ten to a hundred times more quickly via Numpy commands, rather than Python for loops.

If PyROOT is available, histograms and other aggregators can be filled from ROOT TTrees hundreds of times more quickly by JIT-compiling a specialized C++ filler.

Histograms and other aggregators may also be converted into CUDA code for inclusion in a GPU workflow. And if PyCUDA is available, they can also be filled from Numpy arrays by JIT-compiling the CUDA.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

Histogrammar-1.0.0.zip (214.9 kB view details)

Uploaded Source

Histogrammar-1.0.0.tar.gz (169.7 kB view details)

Uploaded Source

Built Distribution

Histogrammar-1.0.0-py2.7.egg (493.0 kB view details)

Uploaded Source

File details

Details for the file Histogrammar-1.0.0.zip.

File metadata

  • Download URL: Histogrammar-1.0.0.zip
  • Upload date:
  • Size: 214.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for Histogrammar-1.0.0.zip
Algorithm Hash digest
SHA256 5dbc6d2fcedfa657a262b7b4b92a6196df72362676229c7abb5cea2c534a038b
MD5 1de57d136dd195b5d72115e6e1eb40c9
BLAKE2b-256 14e56f6e5073d530403c9293348cea080ae5d7a39013d2b4d469b5b5ed26a8a7

See more details on using hashes here.

File details

Details for the file Histogrammar-1.0.0.tar.gz.

File metadata

File hashes

Hashes for Histogrammar-1.0.0.tar.gz
Algorithm Hash digest
SHA256 f367402034be3395564a307fb37c8eafc67004f7d1d4db40b29d8d8fdf914019
MD5 e8f048c650840eeb34ab2718a0102593
BLAKE2b-256 12a294377bbd4e7f1fe34176e0c7079a7bff9e6295bc26dc964ae2b60764ce00

See more details on using hashes here.

File details

Details for the file Histogrammar-1.0.0-py2.7.egg.

File metadata

File hashes

Hashes for Histogrammar-1.0.0-py2.7.egg
Algorithm Hash digest
SHA256 c99f9c609090b728fb174f5f98aceb6a4e6dcaaf8e03efea7fa8e8a9bba5414c
MD5 765d9cf13bc9f0a9272591b1b69bfea0
BLAKE2b-256 acc90254ddf02c01a6216da82efd8974e383a8adcaeec30c05266b03e1f17b3b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page