%global _empty_manifest_terminate_build 0
Name: python-scimap
Version: 1.1.0
Release: 1
Summary: Spatial Single-Cell Analysis Toolkit
License: MIT
URL: https://pypi.org/project/scimap/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dc/df/9fbc541d80ff9ced659aa755c8bc0107a29bb1e4d20f97928b3691bc64aa/scimap-1.1.0.tar.gz
BuildArch: noarch
Requires: python3-pytest
Requires: python3-anndata
Requires: python3-pandas
Requires: python3-scipy
Requires: python3-seaborn
Requires: python3-tifffile
Requires: python3-numpy
Requires: python3-pytest-xvfb
Requires: python3-matplotlib
Requires: python3-PhenoGraph
Requires: python3-scanpy
Requires: python3-mkdocs
Requires: python3-plotly
Requires: python3-TiffFile
Requires: python3-dask[array]
Requires: python3-zarr
Requires: python3-napari
Requires: python3-numba
Requires: python3-shapely
Requires: python3-gensim
Requires: python3-mkdocs-material
Requires: python3-napari-ome-zarr
Requires: python3-llvmlite
Requires: python3-combat
%description
# Single-Cell Image Analysis Package
[](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml)
[](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml)
[](https://pepy.tech/project/scimap)
[](https://pypi.org/project/scimap)
[](https://pypi.org/project/scimap)
[](https://gitter.im/scimap_io/community)
[](https://zenodo.org/badge/latestdoi/271099296)
Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the [anndata](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html) framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.
## Installation
We strongly recommend installing `scimap` in a fresh virtual environment.
```
# If you have conda installed
conda create --name scimap python=3.8
conda activate scimap
```
Install `scimap` directly into an activated virtual environment:
```python
$ pip install scimap
```
After installation, the package can be imported as:
```python
$ python
>>> import scimap as sm
```
## Get Started
#### Detailed documentation of `scimap` functions and tutorials are available [here](http://scimap.xyz/).
*SCIMAP* development is led by [Ajit Johnson Nirmal](https://ajitjohnson.com/) at the Laboratory of Systems Pharmacology, Harvard Medical School.
## Funding
This work is supported by the following NIH grant K99-CA256497
%package -n python3-scimap
Summary: Spatial Single-Cell Analysis Toolkit
Provides: python-scimap
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-scimap
# Single-Cell Image Analysis Package
[](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml)
[](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml)
[](https://pepy.tech/project/scimap)
[](https://pypi.org/project/scimap)
[](https://pypi.org/project/scimap)
[](https://gitter.im/scimap_io/community)
[](https://zenodo.org/badge/latestdoi/271099296)
Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the [anndata](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html) framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.
## Installation
We strongly recommend installing `scimap` in a fresh virtual environment.
```
# If you have conda installed
conda create --name scimap python=3.8
conda activate scimap
```
Install `scimap` directly into an activated virtual environment:
```python
$ pip install scimap
```
After installation, the package can be imported as:
```python
$ python
>>> import scimap as sm
```
## Get Started
#### Detailed documentation of `scimap` functions and tutorials are available [here](http://scimap.xyz/).
*SCIMAP* development is led by [Ajit Johnson Nirmal](https://ajitjohnson.com/) at the Laboratory of Systems Pharmacology, Harvard Medical School.
## Funding
This work is supported by the following NIH grant K99-CA256497
%package help
Summary: Development documents and examples for scimap
Provides: python3-scimap-doc
%description help
# Single-Cell Image Analysis Package
[](https://github.com/ajitjohnson/scimap/actions/workflows/build-unix-mac-win.yml)
[](https://github.com/ajitjohnson/scimap/actions/workflows/docs.yml)
[](https://pepy.tech/project/scimap)
[](https://pypi.org/project/scimap)
[](https://pypi.org/project/scimap)
[](https://gitter.im/scimap_io/community)
[](https://zenodo.org/badge/latestdoi/271099296)
Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the [anndata](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html) framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.
## Installation
We strongly recommend installing `scimap` in a fresh virtual environment.
```
# If you have conda installed
conda create --name scimap python=3.8
conda activate scimap
```
Install `scimap` directly into an activated virtual environment:
```python
$ pip install scimap
```
After installation, the package can be imported as:
```python
$ python
>>> import scimap as sm
```
## Get Started
#### Detailed documentation of `scimap` functions and tutorials are available [here](http://scimap.xyz/).
*SCIMAP* development is led by [Ajit Johnson Nirmal](https://ajitjohnson.com/) at the Laboratory of Systems Pharmacology, Harvard Medical School.
## Funding
This work is supported by the following NIH grant K99-CA256497
%prep
%autosetup -n scimap-1.1.0
%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-scimap -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.1.0-1
- Package Spec generated