%global _empty_manifest_terminate_build 0 Name: python-dash-bio Version: 1.0.2 Release: 1 Summary: Dash components for bioinformatics License: MIT License URL: http://github.com/plotly/dash-bio Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7e/eb/5f12d4b0b91974e2457aaf344230010488453edadffb9e5c241eb9f02d34/dash_bio-1.0.2.tar.gz BuildArch: noarch %description # Dash Bio [![CircleCI](https://circleci.com/gh/plotly/dash-bio/tree/master.svg?style=svg)](https://circleci.com/gh/plotly/dash-bio) [![PyPI version](https://badge.fury.io/py/dash-bio.svg)](https://badge.fury.io/py/dash-bio) Dash Bio is a suite of bioinformatics components built to work with [Dash](https://github.com/plotly/dash/). Announcement: https://medium.com/@plotlygraphs/announcing-dash-bio-ed8835d5da0c Demo: [https://dash-gallery.plotly.host/Portal/?search=Bioinformatics](https://dash-gallery.plotly.host/Portal/?search=Bioinformatics) Documentation: [https://dash.plotly.com/dash-bio](https://dash.plotly.com/dash-bio) ## Components The Dash Bio components each fall into one of three categories: - Custom chart types - Sequence analysis tools - 3D rendering tools ### Custom chart types - Dash Circos - Dash Clustergram - Dash Manhattan Plot - Dash Needle Plot - Dash Volcano Plot ### Sequence analysis tools - Dash Alignment Chart - Dash Onco Print - Dash Forna Container - Dash Sequence Viewer ### Visualization tools - Dash Mol2D - Dash Mol3D - Dash Speck - Dash Ngl ## Using Dash Bio It's easy to add a fully interactive chromosomal, molecular or genomic visualization to your Dash app by simply including the Dash Bio component into your app layout as follows: ```python import urllib.request as urlreq from dash import Dash, html import dash_bio as dashbio app = Dash(__name__) data = urlreq.urlopen( 'https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/alignment_viewer_p53.fasta' ).read().decode('utf-8') app.layout = html.Div([ dashbio.AlignmentChart( id='my-default-alignment-viewer', data=data ) ]) if __name__ == '__main__': app.run_server(debug=True) ``` See the [Dash Bio documentation](https://dash.plotly.com/dash-bio) for more components and examples. ## Run Dash Bio in a JupyterLab environment 1. Create a virtual environment: The following steps require a virtual environment tool to be installed on your computer: `pip install virtualenv` a. On macOS and Linux: `python3 -m venv env` b. On Windows, enter: `py -m venv env` 2. Activate your new environment: a. On macOS and Linux, enter: `source env/bin/activate` b. On Windows, enter: `.\env\Scripts\activate` 3. Install required libraries (make sure you have pip installed with `pip help`): ``` pip install dash dash-bio pandas numpy Jupyterlab ``` 4. To run Dash inside Jupyter lab: a. Install jupyter-dash: `pip install jupyter-dash` b. Enter `jupyter lab build` (Note: This step requires Node.js and NPM installed on yourcomputer. To check if Node and NPM are installed, enter `node -v` and `npm -v` in your terminal. For install instructions see [nodejs.org](https://nodejs.org/en/). 5. To display Plotly figures in JupyterLab: ``` pip install jupyterlab "ipywidgets>=7.5” jupyter labextension install jupyterlab-plotly@4.14.3 ``` 6. Start JupyterLab by typing: `jupyter lab` Important: JupyterLab must be run within the virtual environment that was previously activated. For more on running a Dash app in Jupyter Lab visit [Getting Started with Jupyter Dash](https://github.com/plotly/jupyter-dash/blob/master/notebooks/getting_started.ipynb). ## Dash Learn more about Dash at [https://plotly.com/products/dash/](https://plotly.com/products/dash/). ## Consulting and OEM For inquiries about Dash app development, advanced OEM integration, and more, please [reach out](https://plotly.typeform.com/to/mH1Cpb). ## Contributing and Local Development If you would like to contribute to this repository, or run demo apps and tests, please refer to the [contributing guidelines](https://github.com/plotly/dash-bio/blob/master/CONTRIBUTING.md). %package -n python3-dash-bio Summary: Dash components for bioinformatics Provides: python-dash-bio BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dash-bio # Dash Bio [![CircleCI](https://circleci.com/gh/plotly/dash-bio/tree/master.svg?style=svg)](https://circleci.com/gh/plotly/dash-bio) [![PyPI version](https://badge.fury.io/py/dash-bio.svg)](https://badge.fury.io/py/dash-bio) Dash Bio is a suite of bioinformatics components built to work with [Dash](https://github.com/plotly/dash/). Announcement: https://medium.com/@plotlygraphs/announcing-dash-bio-ed8835d5da0c Demo: [https://dash-gallery.plotly.host/Portal/?search=Bioinformatics](https://dash-gallery.plotly.host/Portal/?search=Bioinformatics) Documentation: [https://dash.plotly.com/dash-bio](https://dash.plotly.com/dash-bio) ## Components The Dash Bio components each fall into one of three categories: - Custom chart types - Sequence analysis tools - 3D rendering tools ### Custom chart types - Dash Circos - Dash Clustergram - Dash Manhattan Plot - Dash Needle Plot - Dash Volcano Plot ### Sequence analysis tools - Dash Alignment Chart - Dash Onco Print - Dash Forna Container - Dash Sequence Viewer ### Visualization tools - Dash Mol2D - Dash Mol3D - Dash Speck - Dash Ngl ## Using Dash Bio It's easy to add a fully interactive chromosomal, molecular or genomic visualization to your Dash app by simply including the Dash Bio component into your app layout as follows: ```python import urllib.request as urlreq from dash import Dash, html import dash_bio as dashbio app = Dash(__name__) data = urlreq.urlopen( 'https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/alignment_viewer_p53.fasta' ).read().decode('utf-8') app.layout = html.Div([ dashbio.AlignmentChart( id='my-default-alignment-viewer', data=data ) ]) if __name__ == '__main__': app.run_server(debug=True) ``` See the [Dash Bio documentation](https://dash.plotly.com/dash-bio) for more components and examples. ## Run Dash Bio in a JupyterLab environment 1. Create a virtual environment: The following steps require a virtual environment tool to be installed on your computer: `pip install virtualenv` a. On macOS and Linux: `python3 -m venv env` b. On Windows, enter: `py -m venv env` 2. Activate your new environment: a. On macOS and Linux, enter: `source env/bin/activate` b. On Windows, enter: `.\env\Scripts\activate` 3. Install required libraries (make sure you have pip installed with `pip help`): ``` pip install dash dash-bio pandas numpy Jupyterlab ``` 4. To run Dash inside Jupyter lab: a. Install jupyter-dash: `pip install jupyter-dash` b. Enter `jupyter lab build` (Note: This step requires Node.js and NPM installed on yourcomputer. To check if Node and NPM are installed, enter `node -v` and `npm -v` in your terminal. For install instructions see [nodejs.org](https://nodejs.org/en/). 5. To display Plotly figures in JupyterLab: ``` pip install jupyterlab "ipywidgets>=7.5” jupyter labextension install jupyterlab-plotly@4.14.3 ``` 6. Start JupyterLab by typing: `jupyter lab` Important: JupyterLab must be run within the virtual environment that was previously activated. For more on running a Dash app in Jupyter Lab visit [Getting Started with Jupyter Dash](https://github.com/plotly/jupyter-dash/blob/master/notebooks/getting_started.ipynb). ## Dash Learn more about Dash at [https://plotly.com/products/dash/](https://plotly.com/products/dash/). ## Consulting and OEM For inquiries about Dash app development, advanced OEM integration, and more, please [reach out](https://plotly.typeform.com/to/mH1Cpb). ## Contributing and Local Development If you would like to contribute to this repository, or run demo apps and tests, please refer to the [contributing guidelines](https://github.com/plotly/dash-bio/blob/master/CONTRIBUTING.md). %package help Summary: Development documents and examples for dash-bio Provides: python3-dash-bio-doc %description help # Dash Bio [![CircleCI](https://circleci.com/gh/plotly/dash-bio/tree/master.svg?style=svg)](https://circleci.com/gh/plotly/dash-bio) [![PyPI version](https://badge.fury.io/py/dash-bio.svg)](https://badge.fury.io/py/dash-bio) Dash Bio is a suite of bioinformatics components built to work with [Dash](https://github.com/plotly/dash/). Announcement: https://medium.com/@plotlygraphs/announcing-dash-bio-ed8835d5da0c Demo: [https://dash-gallery.plotly.host/Portal/?search=Bioinformatics](https://dash-gallery.plotly.host/Portal/?search=Bioinformatics) Documentation: [https://dash.plotly.com/dash-bio](https://dash.plotly.com/dash-bio) ## Components The Dash Bio components each fall into one of three categories: - Custom chart types - Sequence analysis tools - 3D rendering tools ### Custom chart types - Dash Circos - Dash Clustergram - Dash Manhattan Plot - Dash Needle Plot - Dash Volcano Plot ### Sequence analysis tools - Dash Alignment Chart - Dash Onco Print - Dash Forna Container - Dash Sequence Viewer ### Visualization tools - Dash Mol2D - Dash Mol3D - Dash Speck - Dash Ngl ## Using Dash Bio It's easy to add a fully interactive chromosomal, molecular or genomic visualization to your Dash app by simply including the Dash Bio component into your app layout as follows: ```python import urllib.request as urlreq from dash import Dash, html import dash_bio as dashbio app = Dash(__name__) data = urlreq.urlopen( 'https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/alignment_viewer_p53.fasta' ).read().decode('utf-8') app.layout = html.Div([ dashbio.AlignmentChart( id='my-default-alignment-viewer', data=data ) ]) if __name__ == '__main__': app.run_server(debug=True) ``` See the [Dash Bio documentation](https://dash.plotly.com/dash-bio) for more components and examples. ## Run Dash Bio in a JupyterLab environment 1. Create a virtual environment: The following steps require a virtual environment tool to be installed on your computer: `pip install virtualenv` a. On macOS and Linux: `python3 -m venv env` b. On Windows, enter: `py -m venv env` 2. Activate your new environment: a. On macOS and Linux, enter: `source env/bin/activate` b. On Windows, enter: `.\env\Scripts\activate` 3. Install required libraries (make sure you have pip installed with `pip help`): ``` pip install dash dash-bio pandas numpy Jupyterlab ``` 4. To run Dash inside Jupyter lab: a. Install jupyter-dash: `pip install jupyter-dash` b. Enter `jupyter lab build` (Note: This step requires Node.js and NPM installed on yourcomputer. To check if Node and NPM are installed, enter `node -v` and `npm -v` in your terminal. For install instructions see [nodejs.org](https://nodejs.org/en/). 5. To display Plotly figures in JupyterLab: ``` pip install jupyterlab "ipywidgets>=7.5” jupyter labextension install jupyterlab-plotly@4.14.3 ``` 6. Start JupyterLab by typing: `jupyter lab` Important: JupyterLab must be run within the virtual environment that was previously activated. For more on running a Dash app in Jupyter Lab visit [Getting Started with Jupyter Dash](https://github.com/plotly/jupyter-dash/blob/master/notebooks/getting_started.ipynb). ## Dash Learn more about Dash at [https://plotly.com/products/dash/](https://plotly.com/products/dash/). ## Consulting and OEM For inquiries about Dash app development, advanced OEM integration, and more, please [reach out](https://plotly.typeform.com/to/mH1Cpb). ## Contributing and Local Development If you would like to contribute to this repository, or run demo apps and tests, please refer to the [contributing guidelines](https://github.com/plotly/dash-bio/blob/master/CONTRIBUTING.md). %prep %autosetup -n dash-bio-1.0.2 %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-dash-bio -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 1.0.2-1 - Package Spec generated