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+/vampireanalysis-3.4.5.tar.gz
diff --git a/python-vampireanalysis.spec b/python-vampireanalysis.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-vampireanalysis
+Version:	3.4.5
+Release:	1
+Summary:	VAMPIRE Image Analysis Package
+License:	MIT License
+URL:		https://wirtzlab.johnshopkins.edu
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c9/49/3aebc2b258bd591dd4b9e0fd45ffcbcde409704e6a39b4897fdcb3afa5dd/vampireanalysis-3.4.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-scipy
+Requires:	python3-pandas
+Requires:	python3-numpy
+Requires:	python3-pillow
+Requires:	python3-matplotlib
+Requires:	python3-scikit-learn
+Requires:	python3-scikit-image
+Requires:	python3-opencv-python
+Requires:	python3-dask
+
+%description
+# VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition)
+**A robust method to quantify cell morphological heterogeneity**
+
+**1. System requirements**\
+    OS : Windows 10 (64 bit) Version 1909\
+    Software is not compatible with older versions of Windows.\
+    Mac OS is not officially supported, but it may work when installed using pip.\
+    Non-standard hardware is not required.
+    
+**2. Installation Guide**\
+    **Executable file option:**\
+    No installation required. Download the executable file from https://github.com/kukionfr/VAMPIRE_open/releases/download/executable/vampire.exe \
+    Open the executable file to launch the graphic user interface (GUI) of the software\
+
+**PIP installation option:**\
+    Type the following into command prompt window to install vampireanlysis on PYPI (the Python package index) using pip installer
+    
+    pip install vampireanalysis
+    
+To launch the GUI, type "vampire" into command prompt window.
+    
+**3. Demo**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    Sample images to run VAMPIRE can be found in Supplementary Data: https://github.com/kukionfr/VAMPIRE_open/tree/master/Supplementary%20Data \
+    Bigger dataset is also available in these two repositories:\
+    1. https://github.com/kukionfr/Aging_human_dermal_fibroblast_nucleus \
+    2. https://github.com/kukionfr/Micropattern_MEF_LMNA_Image \
+    Expected output of the procedure is provided in the Figure 5 of the manuscript and also in the supplementary files.\
+    Expected run time for demo :\
+        Step 1-2, Segment cells or nuclei, 5~10 mins\
+        Step 3, Create a list of images to build the shape-analysis model, 1-3 mins\
+        Steps 4-9, Build shape-analysis model in VAMPIRE, 1-5 mins\
+        Steps 10-12, Application of the model to analyze shapes across conditions, 1-5 mins\
+        Total, steps 1-12, complete VAMPIRE analysis, 8-23 mins
+        
+**4. Instructions for use**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    By following the Procedure section, the users can reproduce the expected output data provided in the supplementary files.
+
+**5. Code functionality**\
+    The source code can be installed using pip: “pip install vampireanalysis” for Python 3.6 or later.\
+    After installation using pip, type “vampire” in the command window prompt to launch the GUI.\
+    
+•	vampire.py : launch Tk interface for VAMPIRE GUI.\
+•	mainbody.py : read the boundaries of cells or nuclei and process them through three key functions of VAMPIRE analysis: 1. Registration 2. PCA 3. Cluster.\
+•	collect_selected_bstack.py : read the boundaries of cells or nuclei based on the CSV files that contains list of image sets to build or apply the VAMPIRE model.\
+•	bdreg.py: register boundaries of cells or nuclei to eliminate rotational variance.\
+•	pca_bdreg.py : apply PCA to the registered boundaries.\
+•	PCA_custom.py  : principal component analysis code.\
+•	clusterSM.py : apply K-means clustering to PCA processed boundaries of cells or nuclei and assign the cluster number label to each cell or nuclei.\
+•	update_csv.py : generate VAMPIRE datasheet based on the assigned cluster label\
+Codes that are not mentions here belongs to the codes explained. The provided explanation applies to those as well.\
+
+**Python library dependencies**\
+pandas==1.1.0\
+numpy==1.19.1\
+scikit-learn==0.23.2\
+matplotlib==3.3.0\
+pillow==7.2.0\
+opencv-python==4.3.0.36\
+dask==2.22.0\
+scipy==1.5.2\
+scikit-image==0.17.2
+
+
+
+
+%package -n python3-vampireanalysis
+Summary:	VAMPIRE Image Analysis Package
+Provides:	python-vampireanalysis
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-vampireanalysis
+# VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition)
+**A robust method to quantify cell morphological heterogeneity**
+
+**1. System requirements**\
+    OS : Windows 10 (64 bit) Version 1909\
+    Software is not compatible with older versions of Windows.\
+    Mac OS is not officially supported, but it may work when installed using pip.\
+    Non-standard hardware is not required.
+    
+**2. Installation Guide**\
+    **Executable file option:**\
+    No installation required. Download the executable file from https://github.com/kukionfr/VAMPIRE_open/releases/download/executable/vampire.exe \
+    Open the executable file to launch the graphic user interface (GUI) of the software\
+
+**PIP installation option:**\
+    Type the following into command prompt window to install vampireanlysis on PYPI (the Python package index) using pip installer
+    
+    pip install vampireanalysis
+    
+To launch the GUI, type "vampire" into command prompt window.
+    
+**3. Demo**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    Sample images to run VAMPIRE can be found in Supplementary Data: https://github.com/kukionfr/VAMPIRE_open/tree/master/Supplementary%20Data \
+    Bigger dataset is also available in these two repositories:\
+    1. https://github.com/kukionfr/Aging_human_dermal_fibroblast_nucleus \
+    2. https://github.com/kukionfr/Micropattern_MEF_LMNA_Image \
+    Expected output of the procedure is provided in the Figure 5 of the manuscript and also in the supplementary files.\
+    Expected run time for demo :\
+        Step 1-2, Segment cells or nuclei, 5~10 mins\
+        Step 3, Create a list of images to build the shape-analysis model, 1-3 mins\
+        Steps 4-9, Build shape-analysis model in VAMPIRE, 1-5 mins\
+        Steps 10-12, Application of the model to analyze shapes across conditions, 1-5 mins\
+        Total, steps 1-12, complete VAMPIRE analysis, 8-23 mins
+        
+**4. Instructions for use**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    By following the Procedure section, the users can reproduce the expected output data provided in the supplementary files.
+
+**5. Code functionality**\
+    The source code can be installed using pip: “pip install vampireanalysis” for Python 3.6 or later.\
+    After installation using pip, type “vampire” in the command window prompt to launch the GUI.\
+    
+•	vampire.py : launch Tk interface for VAMPIRE GUI.\
+•	mainbody.py : read the boundaries of cells or nuclei and process them through three key functions of VAMPIRE analysis: 1. Registration 2. PCA 3. Cluster.\
+•	collect_selected_bstack.py : read the boundaries of cells or nuclei based on the CSV files that contains list of image sets to build or apply the VAMPIRE model.\
+•	bdreg.py: register boundaries of cells or nuclei to eliminate rotational variance.\
+•	pca_bdreg.py : apply PCA to the registered boundaries.\
+•	PCA_custom.py  : principal component analysis code.\
+•	clusterSM.py : apply K-means clustering to PCA processed boundaries of cells or nuclei and assign the cluster number label to each cell or nuclei.\
+•	update_csv.py : generate VAMPIRE datasheet based on the assigned cluster label\
+Codes that are not mentions here belongs to the codes explained. The provided explanation applies to those as well.\
+
+**Python library dependencies**\
+pandas==1.1.0\
+numpy==1.19.1\
+scikit-learn==0.23.2\
+matplotlib==3.3.0\
+pillow==7.2.0\
+opencv-python==4.3.0.36\
+dask==2.22.0\
+scipy==1.5.2\
+scikit-image==0.17.2
+
+
+
+
+%package help
+Summary:	Development documents and examples for vampireanalysis
+Provides:	python3-vampireanalysis-doc
+%description help
+# VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition)
+**A robust method to quantify cell morphological heterogeneity**
+
+**1. System requirements**\
+    OS : Windows 10 (64 bit) Version 1909\
+    Software is not compatible with older versions of Windows.\
+    Mac OS is not officially supported, but it may work when installed using pip.\
+    Non-standard hardware is not required.
+    
+**2. Installation Guide**\
+    **Executable file option:**\
+    No installation required. Download the executable file from https://github.com/kukionfr/VAMPIRE_open/releases/download/executable/vampire.exe \
+    Open the executable file to launch the graphic user interface (GUI) of the software\
+
+**PIP installation option:**\
+    Type the following into command prompt window to install vampireanlysis on PYPI (the Python package index) using pip installer
+    
+    pip install vampireanalysis
+    
+To launch the GUI, type "vampire" into command prompt window.
+    
+**3. Demo**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    Sample images to run VAMPIRE can be found in Supplementary Data: https://github.com/kukionfr/VAMPIRE_open/tree/master/Supplementary%20Data \
+    Bigger dataset is also available in these two repositories:\
+    1. https://github.com/kukionfr/Aging_human_dermal_fibroblast_nucleus \
+    2. https://github.com/kukionfr/Micropattern_MEF_LMNA_Image \
+    Expected output of the procedure is provided in the Figure 5 of the manuscript and also in the supplementary files.\
+    Expected run time for demo :\
+        Step 1-2, Segment cells or nuclei, 5~10 mins\
+        Step 3, Create a list of images to build the shape-analysis model, 1-3 mins\
+        Steps 4-9, Build shape-analysis model in VAMPIRE, 1-5 mins\
+        Steps 10-12, Application of the model to analyze shapes across conditions, 1-5 mins\
+        Total, steps 1-12, complete VAMPIRE analysis, 8-23 mins
+        
+**4. Instructions for use**\
+    Instructions to run on data can be found in the Procedure section of the manuscript.\
+    By following the Procedure section, the users can reproduce the expected output data provided in the supplementary files.
+
+**5. Code functionality**\
+    The source code can be installed using pip: “pip install vampireanalysis” for Python 3.6 or later.\
+    After installation using pip, type “vampire” in the command window prompt to launch the GUI.\
+    
+•	vampire.py : launch Tk interface for VAMPIRE GUI.\
+•	mainbody.py : read the boundaries of cells or nuclei and process them through three key functions of VAMPIRE analysis: 1. Registration 2. PCA 3. Cluster.\
+•	collect_selected_bstack.py : read the boundaries of cells or nuclei based on the CSV files that contains list of image sets to build or apply the VAMPIRE model.\
+•	bdreg.py: register boundaries of cells or nuclei to eliminate rotational variance.\
+•	pca_bdreg.py : apply PCA to the registered boundaries.\
+•	PCA_custom.py  : principal component analysis code.\
+•	clusterSM.py : apply K-means clustering to PCA processed boundaries of cells or nuclei and assign the cluster number label to each cell or nuclei.\
+•	update_csv.py : generate VAMPIRE datasheet based on the assigned cluster label\
+Codes that are not mentions here belongs to the codes explained. The provided explanation applies to those as well.\
+
+**Python library dependencies**\
+pandas==1.1.0\
+numpy==1.19.1\
+scikit-learn==0.23.2\
+matplotlib==3.3.0\
+pillow==7.2.0\
+opencv-python==4.3.0.36\
+dask==2.22.0\
+scipy==1.5.2\
+scikit-image==0.17.2
+
+
+
+
+%prep
+%autosetup -n vampireanalysis-3.4.5
+
+%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-vampireanalysis -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 3.4.5-1
+- Package Spec generated
diff --git a/sources b/sources
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+a50f4b993f126ab9029b613b306723cc  vampireanalysis-3.4.5.tar.gz