%global _empty_manifest_terminate_build 0
Name: python-distfit
Version: 1.6.10
Release: 1
Summary: distfit is a python library for probability density fitting.
License: MIT License
URL: https://erdogant.github.io/distfit
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/f9/cf5ea4b3912f22db6593254c0ef4a0e68eb20116502c112954afa3d6f539/distfit-1.6.10.tar.gz
BuildArch: noarch
Requires: python3-packaging
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-pandas
Requires: python3-tqdm
Requires: python3-statsmodels
Requires: python3-scipy
Requires: python3-pypickle
Requires: python3-colourmap
%description
[](https://img.shields.io/pypi/pyversions/distfit)
[](https://pypi.org/project/distfit/)
[](https://erdogant.github.io/distfit/)
[](https://github.com/erdogant/distfit/)
[](https://pepy.tech/project/distfit)
[](https://pepy.tech/project/distfit)
[](https://github.com/erdogant/distfit/blob/master/LICENSE)
[](https://github.com/erdogant/distfit/network)
[](https://github.com/erdogant/distfit/issues)
[](http://www.repostatus.org/#active)
[](https://zenodo.org/badge/latestdoi/231843440)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#)
#
### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
#
``distfit`` is a python package for probability density fitting of univariate distributions for random variables.
With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions.
* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions.
To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein,
Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale,
and arg parameters are returned, such as mean and standard deviation for normal distribution.
* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method.
Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method,
the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled.
* In case the dataset contains discrete values, the distift library contains the option for discrete fitting.
The best fit is then derived using the binomial distribution.
#
**⭐️ Star this repo if you like it ⭐️**
#
### [Documentation pages](https://erdogant.github.io/distfit/)
On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples.
#
### Installation
##### Install distfit from PyPI
```bash
pip install distfit
```
##### Install from github source (beta version)
```bash
install git+https://github.com/erdogant/distfit
```
##### Check version
```python
import distfit
print(distfit.__version__)
```
##### The following functions are available after installation:
```python
# Import library
from distfit import distfit
dfit = distfit() # Initialize
dfit.fit_transform(X) # Fit distributions on empirical data X
dfit.predict(y) # Predict the probability of the resonse variables
dfit.plot() # Plot the best fitted distribution (y is included if prediction is made)
```
### Examples
#
##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#)
```python
# [distfit] >INFO> fit
# [distfit] >INFO> transform
# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849]
# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000]
# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722]
# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979]
# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002]
# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530]
# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869]
# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437]
# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722]
# [distfit] >INFO> Compute confidence intervals [parametric]
# [distfit] >INFO> Compute significance for 9 samples.
# [distfit] >INFO> Multiple test correction method applied: [fdr_bh].
# [distfit] >INFO> Create PDF plot for the parametric method.
# [distfit] >INFO> Mark 5 significant regions
# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811]
```
#
##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss)
After we have a fitted model, we can make some predictions using the theoretical distributions.
After making some predictions, we can plot again but now the predictions are automatically included.
#
##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions)
#
##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html)
```python
from scipy.stats import binom
# Generate random numbers
# Set parameters for the test-case
n = 8
p = 0.5
# Generate 10000 samples of the distribution of (n, p)
X = binom(n, p).rvs(10000)
print(X)
# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5
# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7
# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...]
# Import distfit
from distfit import distfit
# Initialize for discrete distribution fitting
dfit = distfit(method='discrete')
# Run distfit to and determine whether we can find the parameters from the data.
dfit.fit_transform(X)
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >Fit using binomial distribution..
# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11]
# [distfit] >Compute confidence interval [discrete]
```
#
##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions)
#
##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html)
### Contributors
Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks:
### Citation
Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information.
### Maintainer
* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
* Contributions are welcome.
* If you wish to buy me a Coffee for this work, it is very appreciated :)
%package -n python3-distfit
Summary: distfit is a python library for probability density fitting.
Provides: python-distfit
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-distfit
[](https://img.shields.io/pypi/pyversions/distfit)
[](https://pypi.org/project/distfit/)
[](https://erdogant.github.io/distfit/)
[](https://github.com/erdogant/distfit/)
[](https://pepy.tech/project/distfit)
[](https://pepy.tech/project/distfit)
[](https://github.com/erdogant/distfit/blob/master/LICENSE)
[](https://github.com/erdogant/distfit/network)
[](https://github.com/erdogant/distfit/issues)
[](http://www.repostatus.org/#active)
[](https://zenodo.org/badge/latestdoi/231843440)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#)
#
### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
#
``distfit`` is a python package for probability density fitting of univariate distributions for random variables.
With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions.
* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions.
To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein,
Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale,
and arg parameters are returned, such as mean and standard deviation for normal distribution.
* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method.
Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method,
the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled.
* In case the dataset contains discrete values, the distift library contains the option for discrete fitting.
The best fit is then derived using the binomial distribution.
#
**⭐️ Star this repo if you like it ⭐️**
#
### [Documentation pages](https://erdogant.github.io/distfit/)
On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples.
#
### Installation
##### Install distfit from PyPI
```bash
pip install distfit
```
##### Install from github source (beta version)
```bash
install git+https://github.com/erdogant/distfit
```
##### Check version
```python
import distfit
print(distfit.__version__)
```
##### The following functions are available after installation:
```python
# Import library
from distfit import distfit
dfit = distfit() # Initialize
dfit.fit_transform(X) # Fit distributions on empirical data X
dfit.predict(y) # Predict the probability of the resonse variables
dfit.plot() # Plot the best fitted distribution (y is included if prediction is made)
```
### Examples
#
##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#)
```python
# [distfit] >INFO> fit
# [distfit] >INFO> transform
# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849]
# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000]
# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722]
# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979]
# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002]
# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530]
# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869]
# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437]
# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722]
# [distfit] >INFO> Compute confidence intervals [parametric]
# [distfit] >INFO> Compute significance for 9 samples.
# [distfit] >INFO> Multiple test correction method applied: [fdr_bh].
# [distfit] >INFO> Create PDF plot for the parametric method.
# [distfit] >INFO> Mark 5 significant regions
# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811]
```
#
##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss)
After we have a fitted model, we can make some predictions using the theoretical distributions.
After making some predictions, we can plot again but now the predictions are automatically included.
#
##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions)
#
##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html)
```python
from scipy.stats import binom
# Generate random numbers
# Set parameters for the test-case
n = 8
p = 0.5
# Generate 10000 samples of the distribution of (n, p)
X = binom(n, p).rvs(10000)
print(X)
# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5
# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7
# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...]
# Import distfit
from distfit import distfit
# Initialize for discrete distribution fitting
dfit = distfit(method='discrete')
# Run distfit to and determine whether we can find the parameters from the data.
dfit.fit_transform(X)
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >Fit using binomial distribution..
# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11]
# [distfit] >Compute confidence interval [discrete]
```
#
##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions)
#
##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html)
### Contributors
Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks:
### Citation
Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information.
### Maintainer
* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
* Contributions are welcome.
* If you wish to buy me a Coffee for this work, it is very appreciated :)
%package help
Summary: Development documents and examples for distfit
Provides: python3-distfit-doc
%description help
[](https://img.shields.io/pypi/pyversions/distfit)
[](https://pypi.org/project/distfit/)
[](https://erdogant.github.io/distfit/)
[](https://github.com/erdogant/distfit/)
[](https://pepy.tech/project/distfit)
[](https://pepy.tech/project/distfit)
[](https://github.com/erdogant/distfit/blob/master/LICENSE)
[](https://github.com/erdogant/distfit/network)
[](https://github.com/erdogant/distfit/issues)
[](http://www.repostatus.org/#active)
[](https://zenodo.org/badge/latestdoi/231843440)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#colab-notebook)
[](https://erdogant.github.io/distfit/pages/html/Documentation.html#)
#
### [Read the Medium Blog for more information](https://erdogant.github.io/distfit/pages/html/Documentation.html#medium-blog)
#
``distfit`` is a python package for probability density fitting of univariate distributions for random variables.
With the random variable as an input, distfit can find the best fit for parametric, non-parametric, and discrete distributions.
* For the parametric approach, the distfit library can determine the best fit across 89 theoretical distributions.
To score the fit, one of the scoring statistics for the good-of-fitness test can be used used, such as RSS/SSE, Wasserstein,
Kolmogorov-Smirnov (KS), or Energy. After finding the best-fitted theoretical distribution, the loc, scale,
and arg parameters are returned, such as mean and standard deviation for normal distribution.
* For the non-parametric approach, the distfit library contains two methods, the quantile and percentile method.
Both methods assume that the data does not follow a specific probability distribution. In the case of the quantile method,
the quantiles of the data are modeled whereas for the percentile method, the percentiles are modeled.
* In case the dataset contains discrete values, the distift library contains the option for discrete fitting.
The best fit is then derived using the binomial distribution.
#
**⭐️ Star this repo if you like it ⭐️**
#
### [Documentation pages](https://erdogant.github.io/distfit/)
On the [documentation pages](https://erdogant.github.io/distfit/) you can find detailed information about the ``distfit`` library with many examples.
#
### Installation
##### Install distfit from PyPI
```bash
pip install distfit
```
##### Install from github source (beta version)
```bash
install git+https://github.com/erdogant/distfit
```
##### Check version
```python
import distfit
print(distfit.__version__)
```
##### The following functions are available after installation:
```python
# Import library
from distfit import distfit
dfit = distfit() # Initialize
dfit.fit_transform(X) # Fit distributions on empirical data X
dfit.predict(y) # Predict the probability of the resonse variables
dfit.plot() # Plot the best fitted distribution (y is included if prediction is made)
```
### Examples
#
##### [Example: Quick start to find best fit for your input data](https://erdogant.github.io/distfit/pages/html/Examples.html#)
```python
# [distfit] >INFO> fit
# [distfit] >INFO> transform
# [distfit] >INFO> [norm ] [0.00 sec] [RSS: 0.00108326] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [expon ] [0.00 sec] [RSS: 0.404237] [loc=-6.897 scale=6.849]
# [distfit] >INFO> [pareto ] [0.00 sec] [RSS: 0.404237] [loc=-536870918.897 scale=536870912.000]
# [distfit] >INFO> [dweibull ] [0.06 sec] [RSS: 0.0115552] [loc=-0.031 scale=1.722]
# [distfit] >INFO> [t ] [0.59 sec] [RSS: 0.00108349] [loc=-0.048 scale=1.997]
# [distfit] >INFO> [genextreme] [0.17 sec] [RSS: 0.00300806] [loc=-0.806 scale=1.979]
# [distfit] >INFO> [gamma ] [0.05 sec] [RSS: 0.00108459] [loc=-1862.903 scale=0.002]
# [distfit] >INFO> [lognorm ] [0.32 sec] [RSS: 0.00121597] [loc=-110.597 scale=110.530]
# [distfit] >INFO> [beta ] [0.10 sec] [RSS: 0.00105629] [loc=-16.364 scale=32.869]
# [distfit] >INFO> [uniform ] [0.00 sec] [RSS: 0.287339] [loc=-6.897 scale=14.437]
# [distfit] >INFO> [loggamma ] [0.12 sec] [RSS: 0.00109042] [loc=-370.746 scale=55.722]
# [distfit] >INFO> Compute confidence intervals [parametric]
# [distfit] >INFO> Compute significance for 9 samples.
# [distfit] >INFO> Multiple test correction method applied: [fdr_bh].
# [distfit] >INFO> Create PDF plot for the parametric method.
# [distfit] >INFO> Mark 5 significant regions
# [distfit] >INFO> Estimated distribution: beta [loc:-16.364265, scale:32.868811]
```
#
##### [Example: Plot summary of the tested distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#plot-rss)
After we have a fitted model, we can make some predictions using the theoretical distributions.
After making some predictions, we can plot again but now the predictions are automatically included.
#
##### [Example: Make predictions using the fitted distribution](https://erdogant.github.io/distfit/pages/html/Examples.html#make-predictions)
#
##### [Example: Test for one specific distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-one-specific-distribution)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Test for multiple distributions](https://erdogant.github.io/distfit/pages/html/Examples.html#fit-for-multiple-distributions)
The full list of distributions is listed here: https://erdogant.github.io/distfit/pages/html/Parametric.html
#
##### [Example: Fit discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html)
```python
from scipy.stats import binom
# Generate random numbers
# Set parameters for the test-case
n = 8
p = 0.5
# Generate 10000 samples of the distribution of (n, p)
X = binom(n, p).rvs(10000)
print(X)
# [5 1 4 5 5 6 2 4 6 5 4 4 4 7 3 4 4 2 3 3 4 4 5 1 3 2 7 4 5 2 3 4 3 3 2 3 5
# 4 6 7 6 2 4 3 3 5 3 5 3 4 4 4 7 5 4 5 3 4 3 3 4 3 3 6 3 3 5 4 4 2 3 2 5 7
# 5 4 8 3 4 3 5 4 3 5 5 2 5 6 7 4 5 5 5 4 4 3 4 5 6 2...]
# Import distfit
from distfit import distfit
# Initialize for discrete distribution fitting
dfit = distfit(method='discrete')
# Run distfit to and determine whether we can find the parameters from the data.
dfit.fit_transform(X)
# [distfit] >fit..
# [distfit] >transform..
# [distfit] >Fit using binomial distribution..
# [distfit] >[binomial] [SSE: 7.79] [n: 8] [p: 0.499959] [chi^2: 1.11]
# [distfit] >Compute confidence interval [discrete]
```
#
##### [Example: Make predictions on unseen data for discrete distribution](https://erdogant.github.io/distfit/pages/html/Discrete.html#make-predictions)
#
##### [Example: Generate samples based on the fitted distribution](https://erdogant.github.io/distfit/pages/html/Generate.html)
### Contributors
Setting up and maintaining distfit has been possible thanks to users and contributors. Thanks:
### Citation
Please cite ``distfit`` in your publications if this is useful for your research. See column right for citation information.
### Maintainer
* Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
* Contributions are welcome.
* If you wish to buy me a Coffee for this work, it is very appreciated :)
%prep
%autosetup -n distfit-1.6.10
%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-distfit -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.6.10-1
- Package Spec generated