Performance analysis for Python.
[](https://pypi.org/project/tuna) [](https://pypi.org/pypi/tuna/) [](https://github.com/nschloe/tuna) [](https://pepy.tech/project/tuna) [](https://discord.gg/hnTJ5MRX2Y) [](https://github.com/nschloe/tuna/actions?query=workflow%3Aci) [](https://lgtm.com/projects/g/nschloe/tuna) [](https://github.com/psf/black) [](https://github.com/prettier/prettier) tuna is a modern, lightweight Python profile viewer inspired by [SnakeViz](https://github.com/jiffyclub/snakeviz). It handles runtime and import profiles, has minimal dependencies, uses [d3](https://d3js.org/) and [bootstrap](https://getbootstrap.com/), and avoids [certain](https://github.com/jiffyclub/snakeviz/issues/111) [errors](https://github.com/jiffyclub/snakeviz/issues/112) present in SnakeViz (see below) and is faster, too. Create a runtime profile with ``` python -mcProfile -o program.prof yourfile.py ``` or an [import profile](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPROFILEIMPORTTIME) with ``` python -X importtime yourfile.py 2> import.log ``` and show it with ``` tuna program.prof ```  ### Why tuna doesn't show the whole call tree The whole timed call tree _cannot_ be retrieved from profile data. Python developers made the decision to only store _parent data_ in profiles because it can be computed with little overhead. To illustrate, consider the following program. ```python import time def a(t0, t1): c(t0) d(t1) def b(): a(1, 4) def c(t): time.sleep(t) def d(t): time.sleep(t) if __name__ == "__main__": a(4, 1) b() ``` The root process (`__main__`) calls `a()` which spends 4 seconds in `c()` and 1 second in `d()`. `__main__` also calls `b()` which calls `a()`, this time spending 1 second in `c()` and 4 seconds in `d()`. The profile, however, will only store that `c()` spent a total of 5 seconds when called from `a()`, and likewise `d()`. The information that the program spent more time in `c()` when called in `root -> a() -> c()` than when called in `root -> b() -> a() -> c()` is not present in the profile. tuna only displays the part of the timed call tree that can be deduced from the profile. SnakeViz, on the other hand, tries to construct the entire call tree, but ends up providing lots of _wrong_ timings. |  |  | | :------------------------------------------------------------: | :-------------------------------------------------------------: | | SnakeViz output. **Wrong.** | tuna output. Only shows what can be retrieved from the profile. | ### Installation tuna is [available from the Python Package Index](https://pypi.org/project/tuna/), so simply do ``` pip install tuna ``` to install. ### Testing To run the tuna unit tests, check out this repository and type ``` pytest ``` ### IPython magics tuna includes a `tuna` line / cell magic which can be used as a drop-in replacement for the `prun` magic. Simply run `%load_ext tuna` to load the magic and then call it like `%tuna sleep(3)` or ```python %%tuna sleep(3) ``` `prun` is still used to do the actual profiling and then the results are displayed in the notebook. ### Development After forking and cloning the repository, make sure to run `make dep` to install additional dependencies (bootstrap and d3) which aren't stored in the repo. ### License This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html). %package -n python3-tuna Summary: Visualize Python performance profiles Provides: python-tuna BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-tuna
Performance analysis for Python.
[](https://pypi.org/project/tuna) [](https://pypi.org/pypi/tuna/) [](https://github.com/nschloe/tuna) [](https://pepy.tech/project/tuna) [](https://discord.gg/hnTJ5MRX2Y) [](https://github.com/nschloe/tuna/actions?query=workflow%3Aci) [](https://lgtm.com/projects/g/nschloe/tuna) [](https://github.com/psf/black) [](https://github.com/prettier/prettier) tuna is a modern, lightweight Python profile viewer inspired by [SnakeViz](https://github.com/jiffyclub/snakeviz). It handles runtime and import profiles, has minimal dependencies, uses [d3](https://d3js.org/) and [bootstrap](https://getbootstrap.com/), and avoids [certain](https://github.com/jiffyclub/snakeviz/issues/111) [errors](https://github.com/jiffyclub/snakeviz/issues/112) present in SnakeViz (see below) and is faster, too. Create a runtime profile with ``` python -mcProfile -o program.prof yourfile.py ``` or an [import profile](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPROFILEIMPORTTIME) with ``` python -X importtime yourfile.py 2> import.log ``` and show it with ``` tuna program.prof ```  ### Why tuna doesn't show the whole call tree The whole timed call tree _cannot_ be retrieved from profile data. Python developers made the decision to only store _parent data_ in profiles because it can be computed with little overhead. To illustrate, consider the following program. ```python import time def a(t0, t1): c(t0) d(t1) def b(): a(1, 4) def c(t): time.sleep(t) def d(t): time.sleep(t) if __name__ == "__main__": a(4, 1) b() ``` The root process (`__main__`) calls `a()` which spends 4 seconds in `c()` and 1 second in `d()`. `__main__` also calls `b()` which calls `a()`, this time spending 1 second in `c()` and 4 seconds in `d()`. The profile, however, will only store that `c()` spent a total of 5 seconds when called from `a()`, and likewise `d()`. The information that the program spent more time in `c()` when called in `root -> a() -> c()` than when called in `root -> b() -> a() -> c()` is not present in the profile. tuna only displays the part of the timed call tree that can be deduced from the profile. SnakeViz, on the other hand, tries to construct the entire call tree, but ends up providing lots of _wrong_ timings. |  |  | | :------------------------------------------------------------: | :-------------------------------------------------------------: | | SnakeViz output. **Wrong.** | tuna output. Only shows what can be retrieved from the profile. | ### Installation tuna is [available from the Python Package Index](https://pypi.org/project/tuna/), so simply do ``` pip install tuna ``` to install. ### Testing To run the tuna unit tests, check out this repository and type ``` pytest ``` ### IPython magics tuna includes a `tuna` line / cell magic which can be used as a drop-in replacement for the `prun` magic. Simply run `%load_ext tuna` to load the magic and then call it like `%tuna sleep(3)` or ```python %%tuna sleep(3) ``` `prun` is still used to do the actual profiling and then the results are displayed in the notebook. ### Development After forking and cloning the repository, make sure to run `make dep` to install additional dependencies (bootstrap and d3) which aren't stored in the repo. ### License This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html). %package help Summary: Development documents and examples for tuna Provides: python3-tuna-doc %description help
Performance analysis for Python.
[](https://pypi.org/project/tuna) [](https://pypi.org/pypi/tuna/) [](https://github.com/nschloe/tuna) [](https://pepy.tech/project/tuna) [](https://discord.gg/hnTJ5MRX2Y) [](https://github.com/nschloe/tuna/actions?query=workflow%3Aci) [](https://lgtm.com/projects/g/nschloe/tuna) [](https://github.com/psf/black) [](https://github.com/prettier/prettier) tuna is a modern, lightweight Python profile viewer inspired by [SnakeViz](https://github.com/jiffyclub/snakeviz). It handles runtime and import profiles, has minimal dependencies, uses [d3](https://d3js.org/) and [bootstrap](https://getbootstrap.com/), and avoids [certain](https://github.com/jiffyclub/snakeviz/issues/111) [errors](https://github.com/jiffyclub/snakeviz/issues/112) present in SnakeViz (see below) and is faster, too. Create a runtime profile with ``` python -mcProfile -o program.prof yourfile.py ``` or an [import profile](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONPROFILEIMPORTTIME) with ``` python -X importtime yourfile.py 2> import.log ``` and show it with ``` tuna program.prof ```  ### Why tuna doesn't show the whole call tree The whole timed call tree _cannot_ be retrieved from profile data. Python developers made the decision to only store _parent data_ in profiles because it can be computed with little overhead. To illustrate, consider the following program. ```python import time def a(t0, t1): c(t0) d(t1) def b(): a(1, 4) def c(t): time.sleep(t) def d(t): time.sleep(t) if __name__ == "__main__": a(4, 1) b() ``` The root process (`__main__`) calls `a()` which spends 4 seconds in `c()` and 1 second in `d()`. `__main__` also calls `b()` which calls `a()`, this time spending 1 second in `c()` and 4 seconds in `d()`. The profile, however, will only store that `c()` spent a total of 5 seconds when called from `a()`, and likewise `d()`. The information that the program spent more time in `c()` when called in `root -> a() -> c()` than when called in `root -> b() -> a() -> c()` is not present in the profile. tuna only displays the part of the timed call tree that can be deduced from the profile. SnakeViz, on the other hand, tries to construct the entire call tree, but ends up providing lots of _wrong_ timings. |  |  | | :------------------------------------------------------------: | :-------------------------------------------------------------: | | SnakeViz output. **Wrong.** | tuna output. Only shows what can be retrieved from the profile. | ### Installation tuna is [available from the Python Package Index](https://pypi.org/project/tuna/), so simply do ``` pip install tuna ``` to install. ### Testing To run the tuna unit tests, check out this repository and type ``` pytest ``` ### IPython magics tuna includes a `tuna` line / cell magic which can be used as a drop-in replacement for the `prun` magic. Simply run `%load_ext tuna` to load the magic and then call it like `%tuna sleep(3)` or ```python %%tuna sleep(3) ``` `prun` is still used to do the actual profiling and then the results are displayed in the notebook. ### Development After forking and cloning the repository, make sure to run `make dep` to install additional dependencies (bootstrap and d3) which aren't stored in the repo. ### License This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html). %prep %autosetup -n tuna-0.5.11 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-tuna -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot