Python tools to interact with darshan log records of HPC applications.
Project description
Python utilities to interact with Darshan log records of HPC applications. pydarshan requires darshan-utils (3.2.2+) to be installed.
Features
Darshan Report Object Wrapper
CFFI bindings to access darshan log files
Plots typically found in the darshan reports (matplotlib)
Auto-discover darshan-util.so (via darshan-parser in $PATH)
Usage
For examples and Jupyter notebooks to get started with pydarshan make sure to check out the examples subdirectory.
A brief examples showing some of the basic functionality is the following:
import darshan # Open darshan log report = darshan.DarshanReport('example.darshan') # Load some report data report.mod_read_all_records('POSIX') report.mod_read_all_records('MPI-IO') # or fetch all report.read_all_generic_records() # ... # Generate summaries for currently loaded data # Note: aggregations are still experimental and have to be activated: darshan.enable_experimental() report.summarize()
Installation
To install in most cases the following will work:
pip install darshan
For alternative installation instructions and installation from source refer to <docs/install.rst>
Testing
Targets for various tests are included in the makefile. To run the normal test suite use:
make test
Or to test against different version of Python using Tox:
make test-all
Coverage tests can be performed using:
make coverage
Conformance to PEPs can be tested using flake8 via:
make lint
Documentation
Documentation for the python bindings is generated seperatedly from the darshan-utils C library in the interest of using Sphinx. After installing the developement requirements using pip install -r requirements_dev.txt the documentation can be build using make as follows:
pip install -r requirements_dev.txt make docs
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distributions
Hashes for darshan-0.0.7-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 89e1bd17ebe464370a547bdbbae2f99760edce423da39fd00dfd2e29731d9d00 |
|
MD5 | 54ab2d6fbe9c5f5174ac1db50b6716bd |
|
BLAKE2b-256 | 5f88aa1ff8d54745053eba0042508f7673f91ced321622487183a2ffb5b55268 |
Hashes for darshan-0.0.7-cp39-cp39-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 93a0c08b89871bc94d5e4d7a833295f7e2866dbe4387fc04ffab13df2a52bc5f |
|
MD5 | a0e3a516ddd718de945ee62854f4ba36 |
|
BLAKE2b-256 | 849c4a399a4d6e3e81d60e31bf80e4dcdac7ce96ce89a5234dd59433cfc0cfb8 |
Hashes for darshan-0.0.7-cp39-cp39-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5245698719bb3ef9c06232fdc1551e07b02f6c246b451c934851366cf5ddcb76 |
|
MD5 | d0c90d7fbd6413d67d336616bd6983d6 |
|
BLAKE2b-256 | 72da972ce69a42067b9ea413252fb31077ff8d8631f1a650ca6263fa0d2cbc28 |
Hashes for darshan-0.0.7-cp39-cp39-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e67301855f4f22ed720e3db47c52fec2d611ddca56ca31155f023594ba3832b7 |
|
MD5 | ddbe7726665ff29350aa6b7e5dc11337 |
|
BLAKE2b-256 | 507a6cdb351b6d722c72ecd4e82535aff648ba3c94844977f58c4b222a25cd86 |
Hashes for darshan-0.0.7-cp38-cp38-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1440c0224ec988d446766899b0a300e817983abf4cc7fb02b16fdeb5c1fb3f5e |
|
MD5 | c5d9d7c85040afdce399f60ecef73c28 |
|
BLAKE2b-256 | ba822a154f207d49a57d9c62ca0800ad255d2f3bcf5b5870a94808779ef98996 |
Hashes for darshan-0.0.7-cp38-cp38-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a1f9fda963669a558695b15c07a672c1100c62f6ea95787adaec336a27839517 |
|
MD5 | ff91b269f9dadccf086f6c9203aa4b5b |
|
BLAKE2b-256 | 0d95f5a573ce8fad6f6bf73cefeb8ddd69eac36e846c48344769d1e4343aa87f |
Hashes for darshan-0.0.7-cp38-cp38-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0b80178905205eac703b6a9c6320b916b228b27f5110faff60e3407461552d55 |
|
MD5 | 244e6bed6a27156d1155475118f0e3f0 |
|
BLAKE2b-256 | cdf18d98a1c1933eb525bf4fb91a341a5bf581ae5b0913248608064fc4fd0187 |
Hashes for darshan-0.0.7-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c6ad7b1a77a1e3e3c292ab7e961f2f791cbba5134cab5ec99bdd5642bd940bbd |
|
MD5 | c5efc62229f09c81cf69ee940156d6ad |
|
BLAKE2b-256 | ec17b9b763bf05a31213510156da2b8ca0a807649b07a61c4d21ebd3970859cf |
Hashes for darshan-0.0.7-cp37-cp37m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 68abf1f1838ed5f7deea0b37210c8acc9be64194243714fa4da3b6ac291c8bb1 |
|
MD5 | e0802c682bd36e6e9ca7648755775f5d |
|
BLAKE2b-256 | 125a27113fada5b0c1b96b4e22e8a3242ed23767f57b687527f573fa9738ab48 |
Hashes for darshan-0.0.7-cp37-cp37m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 62754eba649dae2c0bdcc48b0eda2440cdcd895568eb69585a3707f490403d08 |
|
MD5 | 64040b87504b5d4408e49fc3d654021e |
|
BLAKE2b-256 | 40aeaa21d67b57fa4a0091340f54a8c504860c2739b8d87c8c3bf5e3a1d3ec4b |
Hashes for darshan-0.0.7-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 64c2e2e700da97b500d013483b24da54b2aac76a83bf6a6142418cc7435eacb3 |
|
MD5 | a4c3eda53f38e707d3be8ac2e443a496 |
|
BLAKE2b-256 | 429f88ec1bbbca82d1fd9a0c2858205935bb75832d0a954a38529faf94361d3b |
Hashes for darshan-0.0.7-cp36-cp36m-manylinux1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | beca229c861075b1345223f7ea0aed2a25d9c0fbca0392f6b488a476a0a08d0c |
|
MD5 | 3371803e29ca81f2d0f78fa337200b99 |
|
BLAKE2b-256 | 016a6026e0e0b9f6871785d95cd083a851c2f3e2cd35b04de98d13db4451d7a8 |
Hashes for darshan-0.0.7-cp36-cp36m-manylinux1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9e6d149928a79efae5fbef0dbaee35dc40b486de66a588d1659d086f20aeb10e |
|
MD5 | 904d609ef00bf53418060f0fbeca6807 |
|
BLAKE2b-256 | b528a921fee7364cf90d2d37ced4569af56c3369c25ccfab62e9cdb54b1286ab |