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%global _empty_manifest_terminate_build 0
Name: python-hampel
Version: 0.0.5
Release: 1
Summary: Python implementation of the Hampel Filter
License: MIT License
URL: https://github.com/MichaelisTrofficus/hampel_filter
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/50/0c/a9c77ff9b7f3f907940f520274a3946ff66587f823e41b57c29ac33ed81d/hampel-0.0.5.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-pandas
%description
# Theory
The Hampel filter is generally used to detect anomalies in data with a timeseries structure. It basically consists of a sliding window of a parameterizable size. For each window, each observation will be compared with the Median Absolute Deviation (MAD). The observation will be considered an outlier in the case in which it exceeds the MAD by n times (the parameter n is also parameterizable). For more details, see the following Medium post as well as the Wikipedia entry on MAD.
https://medium.com/wwblog/clean-up-your-time-series-data-with-a-hampel-filter-58b0bb3ebb04
https://en.wikipedia.org/wiki/Median_absolute_deviation
# Install Package
To install the package execute the following command.
```
pip install hampel
```
# Usage
```
hampel(ts, window_size=5, n=3)
hampel(ts, window_size=5, n=3, imputation=True)
```
# Arguments
- **ts** - A pandas Series object representing the timeseries
- **window_size** - Total window size will be computed as 2*window_size + 1
- **n** - Threshold, default is 3 (Pearson's rule)
- **imputation** - If set to False, then the algorithm will be used for outlier detection.
If set to True, then the algorithm will also imput the outliers with the rolling median.
# Code Example
```
import matplotlib.pyplot as plt
import pandas as pd
from hampel import hampel
ts = pd.Series([1, 2, 1 , 1 , 1, 2, 13, 2, 1, 2, 15, 1, 2])
# Just outlier detection
outlier_indices = hampel(ts, window_size=5, n=3)
print("Outlier Indices: ", outlier_indices)
# Outlier Imputation with rolling median
ts_imputation = hampel(ts, window_size=5, n=3, imputation=True)
ts.plot(style="k-")
ts_imputation.plot(style="g-")
plt.show()
```
%package -n python3-hampel
Summary: Python implementation of the Hampel Filter
Provides: python-hampel
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-hampel
# Theory
The Hampel filter is generally used to detect anomalies in data with a timeseries structure. It basically consists of a sliding window of a parameterizable size. For each window, each observation will be compared with the Median Absolute Deviation (MAD). The observation will be considered an outlier in the case in which it exceeds the MAD by n times (the parameter n is also parameterizable). For more details, see the following Medium post as well as the Wikipedia entry on MAD.
https://medium.com/wwblog/clean-up-your-time-series-data-with-a-hampel-filter-58b0bb3ebb04
https://en.wikipedia.org/wiki/Median_absolute_deviation
# Install Package
To install the package execute the following command.
```
pip install hampel
```
# Usage
```
hampel(ts, window_size=5, n=3)
hampel(ts, window_size=5, n=3, imputation=True)
```
# Arguments
- **ts** - A pandas Series object representing the timeseries
- **window_size** - Total window size will be computed as 2*window_size + 1
- **n** - Threshold, default is 3 (Pearson's rule)
- **imputation** - If set to False, then the algorithm will be used for outlier detection.
If set to True, then the algorithm will also imput the outliers with the rolling median.
# Code Example
```
import matplotlib.pyplot as plt
import pandas as pd
from hampel import hampel
ts = pd.Series([1, 2, 1 , 1 , 1, 2, 13, 2, 1, 2, 15, 1, 2])
# Just outlier detection
outlier_indices = hampel(ts, window_size=5, n=3)
print("Outlier Indices: ", outlier_indices)
# Outlier Imputation with rolling median
ts_imputation = hampel(ts, window_size=5, n=3, imputation=True)
ts.plot(style="k-")
ts_imputation.plot(style="g-")
plt.show()
```
%package help
Summary: Development documents and examples for hampel
Provides: python3-hampel-doc
%description help
# Theory
The Hampel filter is generally used to detect anomalies in data with a timeseries structure. It basically consists of a sliding window of a parameterizable size. For each window, each observation will be compared with the Median Absolute Deviation (MAD). The observation will be considered an outlier in the case in which it exceeds the MAD by n times (the parameter n is also parameterizable). For more details, see the following Medium post as well as the Wikipedia entry on MAD.
https://medium.com/wwblog/clean-up-your-time-series-data-with-a-hampel-filter-58b0bb3ebb04
https://en.wikipedia.org/wiki/Median_absolute_deviation
# Install Package
To install the package execute the following command.
```
pip install hampel
```
# Usage
```
hampel(ts, window_size=5, n=3)
hampel(ts, window_size=5, n=3, imputation=True)
```
# Arguments
- **ts** - A pandas Series object representing the timeseries
- **window_size** - Total window size will be computed as 2*window_size + 1
- **n** - Threshold, default is 3 (Pearson's rule)
- **imputation** - If set to False, then the algorithm will be used for outlier detection.
If set to True, then the algorithm will also imput the outliers with the rolling median.
# Code Example
```
import matplotlib.pyplot as plt
import pandas as pd
from hampel import hampel
ts = pd.Series([1, 2, 1 , 1 , 1, 2, 13, 2, 1, 2, 15, 1, 2])
# Just outlier detection
outlier_indices = hampel(ts, window_size=5, n=3)
print("Outlier Indices: ", outlier_indices)
# Outlier Imputation with rolling median
ts_imputation = hampel(ts, window_size=5, n=3, imputation=True)
ts.plot(style="k-")
ts_imputation.plot(style="g-")
plt.show()
```
%prep
%autosetup -n hampel-0.0.5
%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-hampel -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.5-1
- Package Spec generated
|
