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|
%global _empty_manifest_terminate_build 0
Name: python-bat
Version: 0.3.9
Release: 1
Summary: Zeek Analysis Tools
License: Apache
URL: https://github.com/SuperCowPowers/zat
Source0: https://mirrors.aliyun.com/pypi/web/packages/94/2b/f3f4b79048a4989f6432de4fc272578a796ce0927220c5ae5a4b71eb9479/bat-0.3.9.tar.gz
BuildArch: noarch
Requires: python3-requests
Requires: python3-watchdog
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-pandas
Requires: python3-scikit-learn
Requires: python3-pyspark
Requires: python3-pyarrow
Requires: python3-yara-python
Requires: python3-tldextract
%description
## Zeek Analysis Tools (ZAT) [](https://travis-ci.org/SuperCowPowers/zat) [](http://codecov.io/github/SuperCowPowers/zat?branch=master) [](https://pypi.python.org/pypi/zat) [](https://choosealicense.com/licenses/apache-2.0)
The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, and Spark
### Recent Improvements (Fall 2019):
- Renamed to **Zeek** Analysis Tools \:)
- Better Docs (<https://supercowpowers.github.io/zat/>)
- Faster/Smaller Pandas Dataframes for large log files: [Large Dataframes](https://supercowpowers.github.io/zat/large_dataframes.html)
- Better Panda Dataframe to Matrix (ndarray) support: [Dataframe To Matrix](https://supercowpowers.github.io/zat/dataframe_to_matrix.html)
- Scalable conversion from Zeek logs to Parquet: [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- Vastly improved Spark Dataframe Class: [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- Updated/improved Notebooks: [Analysis Notebooks](#analysis-notebooks)
### BroCon 2017 Presentation
Data Analysis, Machine Learning, Bro, and You!
([Video](https://www.youtube.com/watch?v=pG5lU9CLnIU))
### Why ZAT?
Zeek already has a flexible, powerful scripting language why should I use
ZAT?
**Offloading:** Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.
**Data Analysis:** We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.
## Getting Started
- [Examples of Using ZAT](https://supercowpowers.github.io/zat/examples.html)
### Analysis Notebooks
- [Zeek to Scikit-Learn](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Scikit_Learn.ipynb)
- [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- [Spark Clustering](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Spark_Clustering.ipynb)
- [Zeek to Kafka](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka.ipynb)
- [Zeek to Kafka to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka_to_Spark.ipynb)
- [Clustering: Picking K (or not)](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Clustering_Picking_K.ipynb)
- [Anomaly Detection Exploration](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Anomaly_Detection.ipynb)
- [Risky Domains Stats and Deployment](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Risky_Domains.ipynb)
- [Zeek to Matplotlib](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Plot.ipynb)
<img align="right" style="padding:25px" src="notebooks/images/SCP_med.png" width="160">
### Install
$ pip install zat
### Documentation
<https://supercowpowers.github.io/zat/>
### About SuperCowPowers
The company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones. <a href="https://www.supercowpowers.com" target="_blank">Visit SuperCowPowers</a>
%package -n python3-bat
Summary: Zeek Analysis Tools
Provides: python-bat
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-bat
## Zeek Analysis Tools (ZAT) [](https://travis-ci.org/SuperCowPowers/zat) [](http://codecov.io/github/SuperCowPowers/zat?branch=master) [](https://pypi.python.org/pypi/zat) [](https://choosealicense.com/licenses/apache-2.0)
The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, and Spark
### Recent Improvements (Fall 2019):
- Renamed to **Zeek** Analysis Tools \:)
- Better Docs (<https://supercowpowers.github.io/zat/>)
- Faster/Smaller Pandas Dataframes for large log files: [Large Dataframes](https://supercowpowers.github.io/zat/large_dataframes.html)
- Better Panda Dataframe to Matrix (ndarray) support: [Dataframe To Matrix](https://supercowpowers.github.io/zat/dataframe_to_matrix.html)
- Scalable conversion from Zeek logs to Parquet: [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- Vastly improved Spark Dataframe Class: [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- Updated/improved Notebooks: [Analysis Notebooks](#analysis-notebooks)
### BroCon 2017 Presentation
Data Analysis, Machine Learning, Bro, and You!
([Video](https://www.youtube.com/watch?v=pG5lU9CLnIU))
### Why ZAT?
Zeek already has a flexible, powerful scripting language why should I use
ZAT?
**Offloading:** Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.
**Data Analysis:** We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.
## Getting Started
- [Examples of Using ZAT](https://supercowpowers.github.io/zat/examples.html)
### Analysis Notebooks
- [Zeek to Scikit-Learn](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Scikit_Learn.ipynb)
- [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- [Spark Clustering](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Spark_Clustering.ipynb)
- [Zeek to Kafka](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka.ipynb)
- [Zeek to Kafka to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka_to_Spark.ipynb)
- [Clustering: Picking K (or not)](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Clustering_Picking_K.ipynb)
- [Anomaly Detection Exploration](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Anomaly_Detection.ipynb)
- [Risky Domains Stats and Deployment](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Risky_Domains.ipynb)
- [Zeek to Matplotlib](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Plot.ipynb)
<img align="right" style="padding:25px" src="notebooks/images/SCP_med.png" width="160">
### Install
$ pip install zat
### Documentation
<https://supercowpowers.github.io/zat/>
### About SuperCowPowers
The company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones. <a href="https://www.supercowpowers.com" target="_blank">Visit SuperCowPowers</a>
%package help
Summary: Development documents and examples for bat
Provides: python3-bat-doc
%description help
## Zeek Analysis Tools (ZAT) [](https://travis-ci.org/SuperCowPowers/zat) [](http://codecov.io/github/SuperCowPowers/zat?branch=master) [](https://pypi.python.org/pypi/zat) [](https://choosealicense.com/licenses/apache-2.0)
The ZAT Python package supports the processing and analysis of Zeek data
with Pandas, scikit-learn, and Spark
### Recent Improvements (Fall 2019):
- Renamed to **Zeek** Analysis Tools \:)
- Better Docs (<https://supercowpowers.github.io/zat/>)
- Faster/Smaller Pandas Dataframes for large log files: [Large Dataframes](https://supercowpowers.github.io/zat/large_dataframes.html)
- Better Panda Dataframe to Matrix (ndarray) support: [Dataframe To Matrix](https://supercowpowers.github.io/zat/dataframe_to_matrix.html)
- Scalable conversion from Zeek logs to Parquet: [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- Vastly improved Spark Dataframe Class: [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- Updated/improved Notebooks: [Analysis Notebooks](#analysis-notebooks)
### BroCon 2017 Presentation
Data Analysis, Machine Learning, Bro, and You!
([Video](https://www.youtube.com/watch?v=pG5lU9CLnIU))
### Why ZAT?
Zeek already has a flexible, powerful scripting language why should I use
ZAT?
**Offloading:** Running complex tasks like statistics, state machines,
machine learning, etc.. should be offloaded from Zeek so that Zeek can
focus on the efficient processing of high volume network traffic.
**Data Analysis:** We have a large set of support classes that help
bridge from raw Zeek data to packages like Pandas, scikit-learn, and
Spark. We also have example notebooks that show step-by-step how to get
from here to there.
## Getting Started
- [Examples of Using ZAT](https://supercowpowers.github.io/zat/examples.html)
### Analysis Notebooks
- [Zeek to Scikit-Learn](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Scikit_Learn.ipynb)
- [Zeek to Parquet](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Parquet.ipynb)
- [Zeek to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Spark.ipynb)
- [Spark Clustering](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Spark_Clustering.ipynb)
- [Zeek to Kafka](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka.ipynb)
- [Zeek to Kafka to Spark](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Kafka_to_Spark.ipynb)
- [Clustering: Picking K (or not)](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Clustering_Picking_K.ipynb)
- [Anomaly Detection Exploration](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Anomaly_Detection.ipynb)
- [Risky Domains Stats and Deployment](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Risky_Domains.ipynb)
- [Zeek to Matplotlib](https://nbviewer.jupyter.org/github/SuperCowPowers/zat/blob/master/notebooks/Zeek_to_Plot.ipynb)
<img align="right" style="padding:25px" src="notebooks/images/SCP_med.png" width="160">
### Install
$ pip install zat
### Documentation
<https://supercowpowers.github.io/zat/>
### About SuperCowPowers
The company was formed so that its developers could follow their passion for Python, streaming data pipelines and having fun with data analysis. We also think cows are cool and should be superheros or at least carry around rayguns and burner phones. <a href="https://www.supercowpowers.com" target="_blank">Visit SuperCowPowers</a>
%prep
%autosetup -n bat-0.3.9
%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-bat -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.9-1
- Package Spec generated
|
