pprofile 1.11.0-1 source package in Ubuntu
Changelog
pprofile (1.11.0-1) unstable; urgency=medium * New upstream release 1.11.0 * debian/control: Bumps Standards-Version to 4.0.1, no changes needed. * debian/control: Adds Suggests field with python{3}-ipython to pprofile{3} package -- Josue Ortega <email address hidden> Sat, 12 Aug 2017 11:55:11 -0400
Upload details
- Uploaded by:
- Josue Ortega
- Uploaded to:
- Sid
- Original maintainer:
- Josue Ortega
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Bionic | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
pprofile_1.11.0-1.dsc | 2.0 KiB | 1332ad6c53300d90dacb99fe554881afa41d62a74b40e19fc156068e8dab66bb |
pprofile_1.11.0.orig.tar.gz | 26.3 KiB | 41cbe4ca6722c0d2b189c4c7561af70139121ccf4017ad0f8f57ab2f970bc78c |
pprofile_1.11.0-1.debian.tar.xz | 3.2 KiB | 9960a57b05451072e35532da3a72db3827ea6a95b65faa7ee9eebae86541e0c5 |
Available diffs
- diff from 1.10.1-2 to 1.11.0-1 (3.5 KiB)
No changes file available.
Binary packages built by this source
- python-pprofile: Line-granularity, deterministic and statistic Python profiler
Line granularity allows locating precisely where time is spent in code.
Thread awareness automatically propagates profiling to all threads (all
threads in statistic mode, or threads spawned by profiled code in
deterministic mode).
.
Deterministic profiling gives precise measures, but at a large speed
cost (best used on minimal test scenario).
.
Statistic profiling gives rough measure, but has an extremely low
overhead (suitable for live code profiling).
.
Does not require marking methods to profile, allowing non-method
profiling (module imports, class & function declarations and other
module-level code).
.
This package installs the library for Python 2.
- python3-pprofile: Line-granularity, deterministic and statistic Python 3 profiler
Line granularity allows locating precisely where time is spent in code.
Thread awareness automatically propagates profiling to all threads (all
threads in statistic mode, or threads spawned by profiled code in
deterministic mode).
.
Deterministic profiling gives precise measures, but at a large speed
cost (best used on minimal test scenario).
.
Statistic profiling gives rough measure, but has an extremely low
overhead (suitable for live code profiling).
.
Does not require marking methods to profile, allowing non-method
profiling (module imports, class & function declarations and other
module-level code).
.
This package installs the library for Python 3.