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+%global _empty_manifest_terminate_build 0
+Name:		python-das
+Version:	0.30.0
+Release:	1
+Summary:	DAS
+License:	None
+URL:		https://github.com/janclemenslab/das
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/40/4f/ea2bd7979e95bb9dc3e904f0723ef68736d7ce5304a6049f4e1a1298b0d2/das-0.30.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-h5py
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-pyyaml
+Requires:	python3-peakutils
+Requires:	python3-zarr
+Requires:	python3-flammkuchen
+Requires:	python3-defopt
+Requires:	python3-matplotlib
+Requires:	python3-pandas
+Requires:	python3-librosa
+Requires:	python3-matplotlib
+Requires:	python3-matplotlib_scalebar
+Requires:	python3-colorcet
+Requires:	python3-keras-tuner
+Requires:	python3-kt-legacy
+Requires:	python3-rich
+
+%description
+<!-- [![Test install](https://github.com/janclemenslab/das/actions/workflows/main.yml/badge.svg)](https://github.com/janclemenslab/das/actions/workflows/main.yml) -->
+
+# Deep Audio Segmenter
+_DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts.
+
+If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues).
+
+Please cite _DAS_ as:
+
+Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021).
+_Fast and accurate annotation of acoustic signals with deep neural networks._
+[eLife](https://doi.org/10.7554/eLife.68837)
+
+## Installation
+### Pre-requisites
+
+
+__Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)).
+
+If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`.
+
+<!-- ```shell
+curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh
+sh miniconda.sh -b -p $HOME/miniconda
+export PATH="$HOME/miniconda/bin:$PATH"
+``` -->
+<!--
+__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. -->
+
+__Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats.
+
+### Install _DAS_
+Create an anaconda environment called `das` that contains all the required packages:
+```shell
+conda install mamba -c conda-forge -n base -y
+mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+For linux, the last line needs to be:
+```shell
+CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+## Usage
+To start the graphical user interface:
+```shell
+conda activate das
+das gui
+```
+
+The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_:
+
+- A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations.
+- How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html).
+- How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html).
+
+
+
+## Acknowledgements
+The following packages were modified and integrated into das:
+
+- Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`)
+- Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`)
+
+See the sub-module directories for the original READMEs.
+
+
+%package -n python3-das
+Summary:	DAS
+Provides:	python-das
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-das
+<!-- [![Test install](https://github.com/janclemenslab/das/actions/workflows/main.yml/badge.svg)](https://github.com/janclemenslab/das/actions/workflows/main.yml) -->
+
+# Deep Audio Segmenter
+_DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts.
+
+If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues).
+
+Please cite _DAS_ as:
+
+Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021).
+_Fast and accurate annotation of acoustic signals with deep neural networks._
+[eLife](https://doi.org/10.7554/eLife.68837)
+
+## Installation
+### Pre-requisites
+
+
+__Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)).
+
+If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`.
+
+<!-- ```shell
+curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh
+sh miniconda.sh -b -p $HOME/miniconda
+export PATH="$HOME/miniconda/bin:$PATH"
+``` -->
+<!--
+__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. -->
+
+__Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats.
+
+### Install _DAS_
+Create an anaconda environment called `das` that contains all the required packages:
+```shell
+conda install mamba -c conda-forge -n base -y
+mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+For linux, the last line needs to be:
+```shell
+CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+## Usage
+To start the graphical user interface:
+```shell
+conda activate das
+das gui
+```
+
+The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_:
+
+- A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations.
+- How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html).
+- How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html).
+
+
+
+## Acknowledgements
+The following packages were modified and integrated into das:
+
+- Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`)
+- Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`)
+
+See the sub-module directories for the original READMEs.
+
+
+%package help
+Summary:	Development documents and examples for das
+Provides:	python3-das-doc
+%description help
+<!-- [![Test install](https://github.com/janclemenslab/das/actions/workflows/main.yml/badge.svg)](https://github.com/janclemenslab/das/actions/workflows/main.yml) -->
+
+# Deep Audio Segmenter
+_DAS_ is a method for automatically annotating song from raw audio recordings based on a deep neural network. _DAS_ can be used with a graphical user interface, from the terminal, or from within python scripts.
+
+If you have questions, feedback, or find bugs please raise an [issue](https://github.com/janclemenslab/das/issues).
+
+Please cite _DAS_ as:
+
+Elsa Steinfath, Adrian Palacios, Julian Rottschäfer, Deniz Yuezak, Jan Clemens (2021).
+_Fast and accurate annotation of acoustic signals with deep neural networks._
+[eLife](https://doi.org/10.7554/eLife.68837)
+
+## Installation
+### Pre-requisites
+
+
+__Anaconda__: _DAS_ is installed using an anaconda environment. For that, first install the [anaconda python distribution](https://docs.anaconda.com/anaconda/install/) (or [miniconda](https://docs.conda.io/en/latest/miniconda.html)).
+
+If you have conda already installed, make sure you have conda v4.8.4+. If not, update from an older version with `conda update conda`.
+
+<!-- ```shell
+curl https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh
+sh miniconda.sh -b -p $HOME/miniconda
+export PATH="$HOME/miniconda/bin:$PATH"
+``` -->
+<!--
+__CUDA libraries for using the GPU__: While _DAS_ works well for annotating song using the CPU, a GPU will greatly improve annotation speed and is recommended for training a _DAS_ network. The network is implemented in the deep-learning framework Tensorflow. To make sure that Tensorflow can use your GPU, the required CUDA libraries need to be installed. See the [tensorflow docs](https://www.tensorflow.org/install/gpu) for details. -->
+
+__Libsoundfile on linux__: The graphical user interface (GUI) reads audio data using [soundfile](http://pysoundfile.readthedocs.io/), which relies on `libsndfile`. `libsndfile` will be automatically installed on Windows and macOS. On Linux, the library needs to be installed manually with: `sudo apt-get install libsndfile1`. Note that _DAS_ will work w/o `libsndfile` but will not be able to load exotic audio formats.
+
+### Install _DAS_
+Create an anaconda environment called `das` that contains all the required packages:
+```shell
+conda install mamba -c conda-forge -n base -y
+mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+For linux, the last line needs to be:
+```shell
+CONDA_OVERRIDE_CUDA=11.2 mamba create python=3.9 das -c conda-forge -c ncb -c anaconda -c nvidia -n das -y
+```
+
+## Usage
+To start the graphical user interface:
+```shell
+conda activate das
+das gui
+```
+
+The documentation at [https://janclemenslab.org/das/](https://janclemenslab.org/das/) provides information on the usage of _DAS_:
+
+- A [quick start tutorial](https://janclemenslab.org/das/quickstart.html) walks through all steps from manually annotating song, over training a network, to generating new annotations.
+- How to use the [graphical user interface](https://janclemenslab.org/das/tutorials_gui/tutorials_gui.html).
+- How to use _DAS_ [from the terminal or from python scripts](https://janclemenslab.org/das/tutorials/tutorials.html).
+
+
+
+## Acknowledgements
+The following packages were modified and integrated into das:
+
+- Keras implementation of TCN models modified from [keras-tcn](https://github.com/philipperemy/keras-tcn) (in `das.tcn`)
+- Trainable STFT layer implementation modified from [kapre](https://github.com/keunwoochoi/kapre) (in `das.kapre`)
+
+See the sub-module directories for the original READMEs.
+
+
+%prep
+%autosetup -n das-0.30.0
+
+%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-das -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.30.0-1
+- Package Spec generated