%global _empty_manifest_terminate_build 0 Name: python-akiFlagger Version: 1.0.10 Release: 1 Summary: Flag patients with acute kidney injury as per the KDIGO guidelines. License: MIT License URL: https://github.com/isaranwrap/StandardizingAKI Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f1/14/151dd6fe2bc6823157d3151fdbbc09b6745f424c5214bb34f7dced58a317/akiFlagger-1.0.10.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas %description # AKIFlagger ## Introduction Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI: * *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours * *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours * *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section. ## Installation You can install the flagger with ``pip``. Simply type the following into command line and the package should install properly. ```python pip install akiFlagger ``` To ensure that it is working properly, you can open a Python session and test it with. ```python import akiFlagger print(akiFlagger.__version__) >> '1.0.0' ``` Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/. ## Getting started There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/). ## Change Log **Version 0.1.x** - Function-based implementation of flagger. **Version 0.2.x** - Switched to class-based implementation (OOP approach). **Version 0.3.x** - Switched to single-column output for AKI column. **Version 0.4.x** - Removed encounter and admission as optional columns. %package -n python3-akiFlagger Summary: Flag patients with acute kidney injury as per the KDIGO guidelines. Provides: python-akiFlagger BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-akiFlagger # AKIFlagger ## Introduction Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI: * *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours * *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours * *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section. ## Installation You can install the flagger with ``pip``. Simply type the following into command line and the package should install properly. ```python pip install akiFlagger ``` To ensure that it is working properly, you can open a Python session and test it with. ```python import akiFlagger print(akiFlagger.__version__) >> '1.0.0' ``` Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/. ## Getting started There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/). ## Change Log **Version 0.1.x** - Function-based implementation of flagger. **Version 0.2.x** - Switched to class-based implementation (OOP approach). **Version 0.3.x** - Switched to single-column output for AKI column. **Version 0.4.x** - Removed encounter and admission as optional columns. %package help Summary: Development documents and examples for akiFlagger Provides: python3-akiFlagger-doc %description help # AKIFlagger ## Introduction Acute Kidney Injury (AKI) is a sudden onset of kidney failure and damage marked by an increase in the serum creatinine levels (amongst other biomarkers) of the patient. Kidney Disease Improving Global Outcomes (KDIGO) has a set of guidelines and standard definitions of AKI: * *Stage 1*: 50% increase in creatinine in < 7 days or 0.3 increase in creatinine in < 48 hours * *Stage 2*: 100% increase in (or doubling of) creatinine in < 48 hours * *Stage 3*: 200% increase in (or tripling of) creatinine in < 48 hours This package contains a flagger to determine if a patient has developed AKI based on longitudinal data of serum creatinine measurements. More information about the specific data input format can be found in the documentation under the *Getting Started* section. ## Installation You can install the flagger with ``pip``. Simply type the following into command line and the package should install properly. ```python pip install akiFlagger ``` To ensure that it is working properly, you can open a Python session and test it with. ```python import akiFlagger print(akiFlagger.__version__) >> '1.0.0' ``` Alternatively, you can download the source and wheel files to build manually from https://pypi.org/project/akiFlagger/. ## Getting started There is a [walk-through notebook](https://colab.research.google.com/github/isaranwrap/StandardizingAKI/blob/master/GettingStarted.ipynb) available on Github to introduce the necessary components and parameters of the flagger. The notebook can be accessed via Google Colab notebooks. The notebook has also been adapted in the [documentation](https://akiflagger.readthedocs.io/en/latest/). ## Change Log **Version 0.1.x** - Function-based implementation of flagger. **Version 0.2.x** - Switched to class-based implementation (OOP approach). **Version 0.3.x** - Switched to single-column output for AKI column. **Version 0.4.x** - Removed encounter and admission as optional columns. %prep %autosetup -n akiFlagger-1.0.10 %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-akiFlagger -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.0.10-1 - Package Spec generated