%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) [![travis](https://travis-ci.org/SuperCowPowers/zat.svg?branch=master)](https://travis-ci.org/SuperCowPowers/zat) [![codecov.io](http://codecov.io/github/SuperCowPowers/zat/coverage.svg?branch=master)](http://codecov.io/github/SuperCowPowers/zat?branch=master) [![supported-versions](https://img.shields.io/pypi/pyversions/zat.svg)](https://pypi.python.org/pypi/zat) [![license](https://img.shields.io/badge/License-Apache%202.0-green.svg)](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 () - 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) ### Install $ pip install zat ### Documentation ### 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. Visit SuperCowPowers %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) [![travis](https://travis-ci.org/SuperCowPowers/zat.svg?branch=master)](https://travis-ci.org/SuperCowPowers/zat) [![codecov.io](http://codecov.io/github/SuperCowPowers/zat/coverage.svg?branch=master)](http://codecov.io/github/SuperCowPowers/zat?branch=master) [![supported-versions](https://img.shields.io/pypi/pyversions/zat.svg)](https://pypi.python.org/pypi/zat) [![license](https://img.shields.io/badge/License-Apache%202.0-green.svg)](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 () - 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) ### Install $ pip install zat ### Documentation ### 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. Visit SuperCowPowers %package help Summary: Development documents and examples for bat Provides: python3-bat-doc %description help ## Zeek Analysis Tools (ZAT) [![travis](https://travis-ci.org/SuperCowPowers/zat.svg?branch=master)](https://travis-ci.org/SuperCowPowers/zat) [![codecov.io](http://codecov.io/github/SuperCowPowers/zat/coverage.svg?branch=master)](http://codecov.io/github/SuperCowPowers/zat?branch=master) [![supported-versions](https://img.shields.io/pypi/pyversions/zat.svg)](https://pypi.python.org/pypi/zat) [![license](https://img.shields.io/badge/License-Apache%202.0-green.svg)](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 () - 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) ### Install $ pip install zat ### Documentation ### 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. Visit SuperCowPowers %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 - 0.3.9-1 - Package Spec generated