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|
%global _empty_manifest_terminate_build 0
Name: python-geoviews
Version: 1.9.6
Release: 1
Summary: GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research.
License: BSD 3-Clause
URL: https://geoviews.org
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6d/67/5a62448b73e45a5ee2870874a943b5534bd192c9f1996b993537739e49d1/geoviews-1.9.6.tar.gz
BuildArch: noarch
Requires: python3-bokeh
Requires: python3-cartopy
Requires: python3-holoviews
Requires: python3-packaging
Requires: python3-numpy
Requires: python3-shapely
Requires: python3-param
Requires: python3-panel
Requires: python3-cartopy
Requires: python3-codecov
Requires: python3-datashader
Requires: python3-fiona
Requires: python3-flake8
Requires: python3-geopandas
Requires: python3-graphviz
Requires: python3-iris
Requires: python3-jupyter
Requires: python3-lxml
Requires: python3-matplotlib
Requires: python3-mock
Requires: python3-nbsite
Requires: python3-nbsmoke
Requires: python3-netcdf4
Requires: python3-pandas
Requires: python3-pooch
Requires: python3-pyct
Requires: python3-pydata-sphinx-theme
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-scipy
Requires: python3-selenium
Requires: python3-shapely
Requires: python3-sphinx-copybutton
Requires: python3-xarray
Requires: python3-xesmf
Requires: python3-param
Requires: python3-pyct
Requires: python3-bokeh
Requires: python3-pyviz-comms
Requires: python3-geopandas
Requires: python3-netcdf4
Requires: python3-jupyter
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-pyct
Requires: python3-scipy
Requires: python3-shapely
Requires: python3-xarray
Requires: python3-pooch
Requires: python3-datashader
Requires: python3-iris
Requires: python3-xesmf
Requires: python3-mock
Requires: python3-fiona
Requires: python3-nbsite
Requires: python3-cartopy
Requires: python3-graphviz
Requires: python3-lxml
Requires: python3-selenium
Requires: python3-pydata-sphinx-theme
Requires: python3-sphinx-copybutton
Requires: python3-geopandas
Requires: python3-netcdf4
Requires: python3-jupyter
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-pyct
Requires: python3-scipy
Requires: python3-shapely
Requires: python3-xarray
Requires: python3-pooch
Requires: python3-datashader
Requires: python3-iris
Requires: python3-xesmf
Requires: python3-mock
Requires: python3-fiona
Requires: python3-geopandas
Requires: python3-netcdf4
Requires: python3-jupyter
Requires: python3-matplotlib
Requires: python3-pandas
Requires: python3-pyct
Requires: python3-scipy
Requires: python3-shapely
Requires: python3-xarray
Requires: python3-pooch
Requires: python3-datashader
Requires: python3-pytest-cov
Requires: python3-codecov
Requires: python3-flake8
Requires: python3-nbsmoke
Requires: python3-pytest
Requires: python3-fiona
%description
**Geographic visualizations for HoloViews.**
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/geoviews/actions/workflows/test.yaml?query=branch%3Amain) |
| Coverage | [](https://codecov.io/gh/holoviz/geoviews) |
| Latest dev release | [](https://github.com/holoviz/geoviews/tags) [](https://pyviz-dev.github.io/geoviews/) |
| Latest release | [](https://github.com/holoviz/geoviews/releases) [](https://pypi.python.org/pypi/geoviews) [](https://anaconda.org/pyviz/geoviews) [](https://anaconda.org/conda-forge/geoviews) [](https://anaconda.org/anaconda/geoviews) |
| Docs | [](https://github.com/holoviz/geoviews/tree/gh-pages) [](http://geoviews.org) |
| Support | [](https://discourse.holoviz.org/) |
## What is it?
GeoViews is a Python library that makes it easy to explore and
visualize any data that includes geographic locations. It has
particularly powerful support for multidimensional meteorological
and oceanographic datasets, such as those used in weather, climate,
and remote sensing research, but is useful for almost anything
that you would want to plot on a map! You can see lots of example
notebooks at [geoviews.org](https://geoviews.org), and a good
overview is in our [blog post announcement](https://www.continuum.io/blog/developer-blog/introducing-geoviews).
GeoViews is built on the [HoloViews](https://holoviews.org) library for
building flexible visualizations of multidimensional data. GeoViews
adds a family of geographic plot types based on the
[Cartopy](http://scitools.org.uk/cartopy) library, plotted using
either the [Matplotlib](http://matplotlib.org) or
[Bokeh](https://bokeh.org) packages.
Each of the new GeoElement plot types is a new HoloViews Element that
has an associated geographic projection based on ``cartopy.crs``. The
GeoElements currently include ``Feature``, ``WMTS``, ``Tiles``,
``Points``, ``Contours``, ``Image``, ``QuadMesh``, ``TriMesh``,
``RGB``, ``HSV``, ``Labels``, ``Graph``, ``HexTiles``, ``VectorField``
and ``Text`` objects, each of which can easily be overlaid in the same
plots. E.g. an object with temperature data can be overlaid with
coastline data using an expression like ``gv.Image(temperature) *
gv.Feature(cartopy.feature.COASTLINE)``. Each GeoElement can also be
freely combined in layouts with any other HoloViews Element , making
it simple to make even complex multi-figure layouts of overlaid
objects.
## Installation
If you want the latest GeoViews, you will need an up-to-date environment. Updating is never risk-free, but it is a good idea in general and the commands `conda list --revisions` and `conda install --revision N` can usually recover from updates gone awry.
```
conda update --all
```
You can then install GeoViews and all of its dependencies with the following:
```
conda install -c pyviz geoviews
```
Alternatively you can install the geoviews-core package, which
only installs the minimal dependencies required to run geoviews:
```
conda install -c pyviz geoviews-core
```
In certain circumstances proj6 issues may prevent installation or
cause issues (particularly with cartopy<=0.17). If you encounter these
issues ensure you also pin proj4::
conda install proj4<6
Once installed you can copy the examples into the current directory
using the ``geoviews`` command and run them using the Jupyter
notebook:
```
geoviews examples
cd geoviews-examples
jupyter notebook
```
(Here `geoviews examples` is a shorthand for `geoviews copy-examples
--path geoviews-examples && geoviews fetch-data --path
geoviews-examples`.)
In the classic Jupyter notebook environment and JupyterLab, first make sure to load the `gv.extension()`. GeoViews objects will then render themselves if they are the last item in a notebook cell. For versions of `jupyterlab>=3.0` the necessary extension is automatically bundled in the `pyviz_comms` package, which must be >=2.0. However note that for version of `jupyterlab<3.0` you must also manually install the JupyterLab extension with:
```bash
jupyter labextension install @pyviz/jupyterlab_pyviz
```
Once you have installed JupyterLab and the extension launch it with:
```
jupyter-lab
```
If you want to try out the latest features between releases, you can
get the latest dev release by specifying `-c pyviz/label/dev` in place
of `-c pyviz`.
### Additional dependencies
If you need to install libraries only available from conda-forge, such
as Iris (to use data stored in Iris cubes) or xesmf, you should
install from conda-forge:
```
conda create -n env-name -c pyviz -c conda-forge geoviews iris xesmf
conda activate env-name
%package -n python3-geoviews
Summary: GeoViews is a Python library that makes it easy to explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research.
Provides: python-geoviews
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-geoviews
**Geographic visualizations for HoloViews.**
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/geoviews/actions/workflows/test.yaml?query=branch%3Amain) |
| Coverage | [](https://codecov.io/gh/holoviz/geoviews) |
| Latest dev release | [](https://github.com/holoviz/geoviews/tags) [](https://pyviz-dev.github.io/geoviews/) |
| Latest release | [](https://github.com/holoviz/geoviews/releases) [](https://pypi.python.org/pypi/geoviews) [](https://anaconda.org/pyviz/geoviews) [](https://anaconda.org/conda-forge/geoviews) [](https://anaconda.org/anaconda/geoviews) |
| Docs | [](https://github.com/holoviz/geoviews/tree/gh-pages) [](http://geoviews.org) |
| Support | [](https://discourse.holoviz.org/) |
## What is it?
GeoViews is a Python library that makes it easy to explore and
visualize any data that includes geographic locations. It has
particularly powerful support for multidimensional meteorological
and oceanographic datasets, such as those used in weather, climate,
and remote sensing research, but is useful for almost anything
that you would want to plot on a map! You can see lots of example
notebooks at [geoviews.org](https://geoviews.org), and a good
overview is in our [blog post announcement](https://www.continuum.io/blog/developer-blog/introducing-geoviews).
GeoViews is built on the [HoloViews](https://holoviews.org) library for
building flexible visualizations of multidimensional data. GeoViews
adds a family of geographic plot types based on the
[Cartopy](http://scitools.org.uk/cartopy) library, plotted using
either the [Matplotlib](http://matplotlib.org) or
[Bokeh](https://bokeh.org) packages.
Each of the new GeoElement plot types is a new HoloViews Element that
has an associated geographic projection based on ``cartopy.crs``. The
GeoElements currently include ``Feature``, ``WMTS``, ``Tiles``,
``Points``, ``Contours``, ``Image``, ``QuadMesh``, ``TriMesh``,
``RGB``, ``HSV``, ``Labels``, ``Graph``, ``HexTiles``, ``VectorField``
and ``Text`` objects, each of which can easily be overlaid in the same
plots. E.g. an object with temperature data can be overlaid with
coastline data using an expression like ``gv.Image(temperature) *
gv.Feature(cartopy.feature.COASTLINE)``. Each GeoElement can also be
freely combined in layouts with any other HoloViews Element , making
it simple to make even complex multi-figure layouts of overlaid
objects.
## Installation
If you want the latest GeoViews, you will need an up-to-date environment. Updating is never risk-free, but it is a good idea in general and the commands `conda list --revisions` and `conda install --revision N` can usually recover from updates gone awry.
```
conda update --all
```
You can then install GeoViews and all of its dependencies with the following:
```
conda install -c pyviz geoviews
```
Alternatively you can install the geoviews-core package, which
only installs the minimal dependencies required to run geoviews:
```
conda install -c pyviz geoviews-core
```
In certain circumstances proj6 issues may prevent installation or
cause issues (particularly with cartopy<=0.17). If you encounter these
issues ensure you also pin proj4::
conda install proj4<6
Once installed you can copy the examples into the current directory
using the ``geoviews`` command and run them using the Jupyter
notebook:
```
geoviews examples
cd geoviews-examples
jupyter notebook
```
(Here `geoviews examples` is a shorthand for `geoviews copy-examples
--path geoviews-examples && geoviews fetch-data --path
geoviews-examples`.)
In the classic Jupyter notebook environment and JupyterLab, first make sure to load the `gv.extension()`. GeoViews objects will then render themselves if they are the last item in a notebook cell. For versions of `jupyterlab>=3.0` the necessary extension is automatically bundled in the `pyviz_comms` package, which must be >=2.0. However note that for version of `jupyterlab<3.0` you must also manually install the JupyterLab extension with:
```bash
jupyter labextension install @pyviz/jupyterlab_pyviz
```
Once you have installed JupyterLab and the extension launch it with:
```
jupyter-lab
```
If you want to try out the latest features between releases, you can
get the latest dev release by specifying `-c pyviz/label/dev` in place
of `-c pyviz`.
### Additional dependencies
If you need to install libraries only available from conda-forge, such
as Iris (to use data stored in Iris cubes) or xesmf, you should
install from conda-forge:
```
conda create -n env-name -c pyviz -c conda-forge geoviews iris xesmf
conda activate env-name
%package help
Summary: Development documents and examples for geoviews
Provides: python3-geoviews-doc
%description help
**Geographic visualizations for HoloViews.**
| | |
| --- | --- |
| Build Status | [](https://github.com/holoviz/geoviews/actions/workflows/test.yaml?query=branch%3Amain) |
| Coverage | [](https://codecov.io/gh/holoviz/geoviews) |
| Latest dev release | [](https://github.com/holoviz/geoviews/tags) [](https://pyviz-dev.github.io/geoviews/) |
| Latest release | [](https://github.com/holoviz/geoviews/releases) [](https://pypi.python.org/pypi/geoviews) [](https://anaconda.org/pyviz/geoviews) [](https://anaconda.org/conda-forge/geoviews) [](https://anaconda.org/anaconda/geoviews) |
| Docs | [](https://github.com/holoviz/geoviews/tree/gh-pages) [](http://geoviews.org) |
| Support | [](https://discourse.holoviz.org/) |
## What is it?
GeoViews is a Python library that makes it easy to explore and
visualize any data that includes geographic locations. It has
particularly powerful support for multidimensional meteorological
and oceanographic datasets, such as those used in weather, climate,
and remote sensing research, but is useful for almost anything
that you would want to plot on a map! You can see lots of example
notebooks at [geoviews.org](https://geoviews.org), and a good
overview is in our [blog post announcement](https://www.continuum.io/blog/developer-blog/introducing-geoviews).
GeoViews is built on the [HoloViews](https://holoviews.org) library for
building flexible visualizations of multidimensional data. GeoViews
adds a family of geographic plot types based on the
[Cartopy](http://scitools.org.uk/cartopy) library, plotted using
either the [Matplotlib](http://matplotlib.org) or
[Bokeh](https://bokeh.org) packages.
Each of the new GeoElement plot types is a new HoloViews Element that
has an associated geographic projection based on ``cartopy.crs``. The
GeoElements currently include ``Feature``, ``WMTS``, ``Tiles``,
``Points``, ``Contours``, ``Image``, ``QuadMesh``, ``TriMesh``,
``RGB``, ``HSV``, ``Labels``, ``Graph``, ``HexTiles``, ``VectorField``
and ``Text`` objects, each of which can easily be overlaid in the same
plots. E.g. an object with temperature data can be overlaid with
coastline data using an expression like ``gv.Image(temperature) *
gv.Feature(cartopy.feature.COASTLINE)``. Each GeoElement can also be
freely combined in layouts with any other HoloViews Element , making
it simple to make even complex multi-figure layouts of overlaid
objects.
## Installation
If you want the latest GeoViews, you will need an up-to-date environment. Updating is never risk-free, but it is a good idea in general and the commands `conda list --revisions` and `conda install --revision N` can usually recover from updates gone awry.
```
conda update --all
```
You can then install GeoViews and all of its dependencies with the following:
```
conda install -c pyviz geoviews
```
Alternatively you can install the geoviews-core package, which
only installs the minimal dependencies required to run geoviews:
```
conda install -c pyviz geoviews-core
```
In certain circumstances proj6 issues may prevent installation or
cause issues (particularly with cartopy<=0.17). If you encounter these
issues ensure you also pin proj4::
conda install proj4<6
Once installed you can copy the examples into the current directory
using the ``geoviews`` command and run them using the Jupyter
notebook:
```
geoviews examples
cd geoviews-examples
jupyter notebook
```
(Here `geoviews examples` is a shorthand for `geoviews copy-examples
--path geoviews-examples && geoviews fetch-data --path
geoviews-examples`.)
In the classic Jupyter notebook environment and JupyterLab, first make sure to load the `gv.extension()`. GeoViews objects will then render themselves if they are the last item in a notebook cell. For versions of `jupyterlab>=3.0` the necessary extension is automatically bundled in the `pyviz_comms` package, which must be >=2.0. However note that for version of `jupyterlab<3.0` you must also manually install the JupyterLab extension with:
```bash
jupyter labextension install @pyviz/jupyterlab_pyviz
```
Once you have installed JupyterLab and the extension launch it with:
```
jupyter-lab
```
If you want to try out the latest features between releases, you can
get the latest dev release by specifying `-c pyviz/label/dev` in place
of `-c pyviz`.
### Additional dependencies
If you need to install libraries only available from conda-forge, such
as Iris (to use data stored in Iris cubes) or xesmf, you should
install from conda-forge:
```
conda create -n env-name -c pyviz -c conda-forge geoviews iris xesmf
conda activate env-name
%prep
%autosetup -n geoviews-1.9.6
%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-geoviews -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 1.9.6-1
- Package Spec generated
|
