%global _empty_manifest_terminate_build 0 Name: python-pycausalmatch Version: 0.0.4 Release: 1 Summary: Causal Impact of an intervention integrated with control group selection License: MIT License URL: https://github.com/unbiasedmodeler/pycausalmatch Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d3/53/f92a3af9e3cf629832792704266531720c8ac8e925732d51f10a9ae5c8f6/pycausalmatch-0.0.4.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-dtw-python Requires: python3-tfcausalimpact %description ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │   ├── external <- Data from third party sources. │   ├── interim <- Intermediate data that has been transformed. │   ├── processed <- The final, canonical data sets for modeling. │   └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │   └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported ├── src <- Source code for use in this project. │   ├── __init__.py <- Makes src a Python module │ │ │   ├── data <- Scripts to download or generate data │   │   └── make_dataset.py │ │ │   ├── features <- Scripts to turn raw data into features for modeling │   │   └── build_features.py │ │ │   ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │   │   ├── predict_model.py │   │   └── train_model.py │ │ │   └── visualization <- Scripts to create exploratory and results oriented visualizations │   └── visualize.py │ %package -n python3-pycausalmatch Summary: Causal Impact of an intervention integrated with control group selection Provides: python-pycausalmatch BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pycausalmatch ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │   ├── external <- Data from third party sources. │   ├── interim <- Intermediate data that has been transformed. │   ├── processed <- The final, canonical data sets for modeling. │   └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │   └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported ├── src <- Source code for use in this project. │   ├── __init__.py <- Makes src a Python module │ │ │   ├── data <- Scripts to download or generate data │   │   └── make_dataset.py │ │ │   ├── features <- Scripts to turn raw data into features for modeling │   │   └── build_features.py │ │ │   ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │   │   ├── predict_model.py │   │   └── train_model.py │ │ │   └── visualization <- Scripts to create exploratory and results oriented visualizations │   └── visualize.py │ %package help Summary: Development documents and examples for pycausalmatch Provides: python3-pycausalmatch-doc %description help ├── LICENSE ├── Makefile <- Makefile with commands like `make data` or `make train` ├── README.md <- The top-level README for developers using this project. ├── data │   ├── external <- Data from third party sources. │   ├── interim <- Intermediate data that has been transformed. │   ├── processed <- The final, canonical data sets for modeling. │   └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── references <- Data dictionaries, manuals, and all other explanatory materials. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │   └── figures <- Generated graphics and figures to be used in reporting │ ├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. │ generated with `pip freeze > requirements.txt` │ ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported ├── src <- Source code for use in this project. │   ├── __init__.py <- Makes src a Python module │ │ │   ├── data <- Scripts to download or generate data │   │   └── make_dataset.py │ │ │   ├── features <- Scripts to turn raw data into features for modeling │   │   └── build_features.py │ │ │   ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │   │   ├── predict_model.py │   │   └── train_model.py │ │ │   └── visualization <- Scripts to create exploratory and results oriented visualizations │   └── visualize.py │ %prep %autosetup -n pycausalmatch-0.0.4 %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-pycausalmatch -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 0.0.4-1 - Package Spec generated