%global _empty_manifest_terminate_build 0
Name: python-perfplot
Version: 0.10.2
Release: 1
Summary: Performance plots for Python code snippets
License: GNU General Public License v3 or later (GPLv3+)
URL: https://pypi.org/project/perfplot/
Source0: https://mirrors.aliyun.com/pypi/web/packages/97/41/51d8b9caa150a050de16a229f627e4b37515dbff0075259e4e75aff7218b/perfplot-0.10.2.tar.gz
BuildArch: noarch
Requires: python3-matplotlib
Requires: python3-matplotx
Requires: python3-numpy
Requires: python3-rich
Requires: python3-typing_extensions
%description
[](https://pypi.org/project/perfplot)
[](https://pypi.org/pypi/perfplot/)
[](https://github.com/nschloe/perfplot)
[](https://pepy.tech/project/perfplot)
[](https://discord.gg/hnTJ5MRX2Y)
[](https://github.com/nschloe/perfplot/actions?query=workflow%3Aci)
[](https://codecov.io/gh/nschloe/perfplot)
[](https://lgtm.com/projects/g/nschloe/perfplot)
[](https://github.com/psf/black)
perfplot extends Python's [timeit](https://docs.python.org/3/library/timeit.html) by
testing snippets with input parameters (e.g., the size of an array) and plotting the
results.
For example, to compare different NumPy array concatenation methods, the script
```python
import numpy as np
import perfplot
perfplot.show(
setup=lambda n: np.random.rand(n), # or setup=np.random.rand
kernels=[
lambda a: np.c_[a, a],
lambda a: np.stack([a, a]).T,
lambda a: np.vstack([a, a]).T,
lambda a: np.column_stack([a, a]),
lambda a: np.concatenate([a[:, None], a[:, None]], axis=1),
],
labels=["c_", "stack", "vstack", "column_stack", "concat"],
n_range=[2**k for k in range(25)],
xlabel="len(a)",
# More optional arguments with their default values:
# logx="auto", # set to True or False to force scaling
# logy="auto",
# equality_check=np.allclose, # set to None to disable "correctness" assertion
# show_progress=True,
# target_time_per_measurement=1.0,
# max_time=None, # maximum time per measurement
# time_unit="s", # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
# relative_to=1, # plot the timings relative to one of the measurements
# flops=lambda n: 3*n, # FLOPS plots
)
```
produces
|  |  |
| -------------------------------------------------- | ---------------------------------------------------- |
Clearly, `stack` and `vstack` are the best options for large arrays.
(By default, perfplot asserts the equality of the output of all snippets, too.)
If your plot takes a while to generate, you can also use
```python
perfplot.live(
# ...
)
```
with the same arguments as above. It will plot the updates live.
Benchmarking and plotting can be separated. This allows multiple plots of the same data,
for example:
```python
out = perfplot.bench(
# same arguments as above (except the plot-related ones, like time_unit or log*)
)
out.show()
out.save("perf.png", transparent=True, bbox_inches="tight")
```
Other examples:
- [Making a flat list out of list of lists in Python](https://stackoverflow.com/a/45323085/353337)
- [Most efficient way to map function over numpy array](https://stackoverflow.com/a/46470401/353337)
- [numpy: most efficient frequency counts for unique values in an array](https://stackoverflow.com/a/43096495/353337)
- [Most efficient way to reverse a numpy array](https://stackoverflow.com/a/44921013/353337)
- [How to add an extra column to an numpy array](https://stackoverflow.com/a/40218298/353337)
- [Initializing numpy matrix to something other than zero or one](https://stackoverflow.com/a/45006691/353337)
### Installation
perfplot is [available from the Python Package
Index](https://pypi.org/project/perfplot/), so simply do
```
pip install perfplot
```
to install.
### Testing
To run the perfplot unit tests, check out this repository and type
```
tox
```
### License
This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html).
%package -n python3-perfplot
Summary: Performance plots for Python code snippets
Provides: python-perfplot
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-perfplot
[](https://pypi.org/project/perfplot)
[](https://pypi.org/pypi/perfplot/)
[](https://github.com/nschloe/perfplot)
[](https://pepy.tech/project/perfplot)
[](https://discord.gg/hnTJ5MRX2Y)
[](https://github.com/nschloe/perfplot/actions?query=workflow%3Aci)
[](https://codecov.io/gh/nschloe/perfplot)
[](https://lgtm.com/projects/g/nschloe/perfplot)
[](https://github.com/psf/black)
perfplot extends Python's [timeit](https://docs.python.org/3/library/timeit.html) by
testing snippets with input parameters (e.g., the size of an array) and plotting the
results.
For example, to compare different NumPy array concatenation methods, the script
```python
import numpy as np
import perfplot
perfplot.show(
setup=lambda n: np.random.rand(n), # or setup=np.random.rand
kernels=[
lambda a: np.c_[a, a],
lambda a: np.stack([a, a]).T,
lambda a: np.vstack([a, a]).T,
lambda a: np.column_stack([a, a]),
lambda a: np.concatenate([a[:, None], a[:, None]], axis=1),
],
labels=["c_", "stack", "vstack", "column_stack", "concat"],
n_range=[2**k for k in range(25)],
xlabel="len(a)",
# More optional arguments with their default values:
# logx="auto", # set to True or False to force scaling
# logy="auto",
# equality_check=np.allclose, # set to None to disable "correctness" assertion
# show_progress=True,
# target_time_per_measurement=1.0,
# max_time=None, # maximum time per measurement
# time_unit="s", # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
# relative_to=1, # plot the timings relative to one of the measurements
# flops=lambda n: 3*n, # FLOPS plots
)
```
produces
|  |  |
| -------------------------------------------------- | ---------------------------------------------------- |
Clearly, `stack` and `vstack` are the best options for large arrays.
(By default, perfplot asserts the equality of the output of all snippets, too.)
If your plot takes a while to generate, you can also use
```python
perfplot.live(
# ...
)
```
with the same arguments as above. It will plot the updates live.
Benchmarking and plotting can be separated. This allows multiple plots of the same data,
for example:
```python
out = perfplot.bench(
# same arguments as above (except the plot-related ones, like time_unit or log*)
)
out.show()
out.save("perf.png", transparent=True, bbox_inches="tight")
```
Other examples:
- [Making a flat list out of list of lists in Python](https://stackoverflow.com/a/45323085/353337)
- [Most efficient way to map function over numpy array](https://stackoverflow.com/a/46470401/353337)
- [numpy: most efficient frequency counts for unique values in an array](https://stackoverflow.com/a/43096495/353337)
- [Most efficient way to reverse a numpy array](https://stackoverflow.com/a/44921013/353337)
- [How to add an extra column to an numpy array](https://stackoverflow.com/a/40218298/353337)
- [Initializing numpy matrix to something other than zero or one](https://stackoverflow.com/a/45006691/353337)
### Installation
perfplot is [available from the Python Package
Index](https://pypi.org/project/perfplot/), so simply do
```
pip install perfplot
```
to install.
### Testing
To run the perfplot unit tests, check out this repository and type
```
tox
```
### 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 perfplot
Provides: python3-perfplot-doc
%description help
[](https://pypi.org/project/perfplot)
[](https://pypi.org/pypi/perfplot/)
[](https://github.com/nschloe/perfplot)
[](https://pepy.tech/project/perfplot)
[](https://discord.gg/hnTJ5MRX2Y)
[](https://github.com/nschloe/perfplot/actions?query=workflow%3Aci)
[](https://codecov.io/gh/nschloe/perfplot)
[](https://lgtm.com/projects/g/nschloe/perfplot)
[](https://github.com/psf/black)
perfplot extends Python's [timeit](https://docs.python.org/3/library/timeit.html) by
testing snippets with input parameters (e.g., the size of an array) and plotting the
results.
For example, to compare different NumPy array concatenation methods, the script
```python
import numpy as np
import perfplot
perfplot.show(
setup=lambda n: np.random.rand(n), # or setup=np.random.rand
kernels=[
lambda a: np.c_[a, a],
lambda a: np.stack([a, a]).T,
lambda a: np.vstack([a, a]).T,
lambda a: np.column_stack([a, a]),
lambda a: np.concatenate([a[:, None], a[:, None]], axis=1),
],
labels=["c_", "stack", "vstack", "column_stack", "concat"],
n_range=[2**k for k in range(25)],
xlabel="len(a)",
# More optional arguments with their default values:
# logx="auto", # set to True or False to force scaling
# logy="auto",
# equality_check=np.allclose, # set to None to disable "correctness" assertion
# show_progress=True,
# target_time_per_measurement=1.0,
# max_time=None, # maximum time per measurement
# time_unit="s", # set to one of ("auto", "s", "ms", "us", or "ns") to force plot units
# relative_to=1, # plot the timings relative to one of the measurements
# flops=lambda n: 3*n, # FLOPS plots
)
```
produces
|  |  |
| -------------------------------------------------- | ---------------------------------------------------- |
Clearly, `stack` and `vstack` are the best options for large arrays.
(By default, perfplot asserts the equality of the output of all snippets, too.)
If your plot takes a while to generate, you can also use
```python
perfplot.live(
# ...
)
```
with the same arguments as above. It will plot the updates live.
Benchmarking and plotting can be separated. This allows multiple plots of the same data,
for example:
```python
out = perfplot.bench(
# same arguments as above (except the plot-related ones, like time_unit or log*)
)
out.show()
out.save("perf.png", transparent=True, bbox_inches="tight")
```
Other examples:
- [Making a flat list out of list of lists in Python](https://stackoverflow.com/a/45323085/353337)
- [Most efficient way to map function over numpy array](https://stackoverflow.com/a/46470401/353337)
- [numpy: most efficient frequency counts for unique values in an array](https://stackoverflow.com/a/43096495/353337)
- [Most efficient way to reverse a numpy array](https://stackoverflow.com/a/44921013/353337)
- [How to add an extra column to an numpy array](https://stackoverflow.com/a/40218298/353337)
- [Initializing numpy matrix to something other than zero or one](https://stackoverflow.com/a/45006691/353337)
### Installation
perfplot is [available from the Python Package
Index](https://pypi.org/project/perfplot/), so simply do
```
pip install perfplot
```
to install.
### Testing
To run the perfplot unit tests, check out this repository and type
```
tox
```
### License
This software is published under the [GPLv3 license](https://www.gnu.org/licenses/gpl-3.0.en.html).
%prep
%autosetup -n perfplot-0.10.2
%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-perfplot -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot - 0.10.2-1
- Package Spec generated