path: root/python-sxs.spec

%global _empty_manifest_terminate_build 0
Name:		python-sxs
Version:	2022.4.5
Release:	1
Summary:	Interface to data produced by the Simulating eXtreme Spacetimes collaboration
License:	MIT
URL:		https://github.com/sxs-collaboration/sxs
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f8/12/c501831cdc626b032b01cbf2d97a6bdd3db0b645ed6ef43dca1bd7ce3103/sxs-2022.4.5.tar.gz
BuildArch:	noarch

Requires:	python3-numpy
Requires:	python3-scipy
Requires:	python3-numba
Requires:	python3-mkapi
Requires:	python3-quaternionic
Requires:	python3-spherical
Requires:	python3-h5py
Requires:	python3-inflection
Requires:	python3-requests
Requires:	python3-pytest-forked
Requires:	python3-tqdm
Requires:	python3-pytz
Requires:	python3-urllib3
Requires:	python3-pandas
Requires:	python3-mkdocs
Requires:	python3-black
Requires:	python3-pymdown-extensions
Requires:	python3-spinsfast
Requires:	python3-ipywidgets
Requires:	python3-ipykernel
Requires:	python3-jupyter_contrib_nbextensions
Requires:	python3-jupyterlab
Requires:	python3-line_profiler
Requires:	python3-memory_profiler
Requires:	python3-matplotlib
Requires:	python3-sympy
Requires:	python3-corner
Requires:	python3-qgrid
Requires:	python3-rise
Requires:	python3-numpy-quaternion
Requires:	python3-scri
Requires:	python3-pylatexenc
Requires:	python3-feedparser
Requires:	python3-caltechdata-api

%description
[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)


# Simulating eXtreme Spacetimes python package

The `sxs` python package provides a high-level interface for using data
produced by the SXS collaboration.  In particular, the function `sxs.load` can
automatically find, download, and load data, returning objects that provide
common interfaces to the various types of data, without forcing the user to
worry about details like data formats or where to find the data.  It can also
automatically select the newest or highest-resolution dataset for a given
simulation, or return a range of versions or resolutions.  Currently, the
high-level objects encapsulate

  * Catalog — a listing of all data produced by the SXS collaboration
  * Metadata — data describing the simulation parameters
  * Horizons — time-series data describing the apparent horizons
  * Waveforms — time-series data describing the extrapolated gravitational-wave
    modes


## Installation

Because this package is pure python code, installation is very simple.  In
particular, with a reasonably modern installation, you can just run a command
like

```bash
conda install -c conda-forge sxs
```

or

```bash
python -m pip install sxs
```

Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
installation of python, which is the most recommended approach for scientific
python; the second command assumes that you have an appropriate python
environment set up in some other way.  Either of these commands will download
and install the `sxs` package and its most vital requirements.

If you want to install all the goodies that enable things like jupyter
notebooks with plots and interactive tables, you could run

```bash
conda install -c conda-forge sxs-ecosystem
```

or

```bash
python -m pip install sxs[ecosystem]
```

You will probably also want to set some sensible defaults to automatically
download and cache data:

```bash
python -c "import sxs; sxs.write_config(download=True, cache=True)"
```

This will write a configuration file in the directory returned by
`sxs.sxs_directory("config")`, and downloaded data will be cached in the
directory returned by `sxs.sxs_directory("cache")`.  See [that function's
documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
for details.


## Usage

An extensive demonstration of this package's capabilities is available
[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
interactive jupyter notebooks that are actually running this code and some
pre-downloaded data.  The following is just a very brief overview of the `sxs`
package's main components.

There are four important objects to understand in this package:

```python
import sxs

catalog = sxs.load("catalog")
metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
```

[The `catalog`
object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
contains information about every simulation in the catalog, including all
available data files, and information about how to get them.  You probably
don't need to actually know about details like where to get the data, but
`catalog` can help you find the simulations you care about.  Most importantly,
`catalog.simulations` is a `dict` object, where the keys are names of
simulations (like "SXS:BBH:0123") and the values are the same types as [the
`metadata`
object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
which contains metadata about that simulation — things like mass ratio, spins,
etc.  This `metadata` reflects the actual output of the simulations, which
leads to some inconsistencies in their formats.  A more consistent interface
(though it is biased toward returning NaNs where a human might glean more
information) is provided by `catalog.table`, which returns a
[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
types for each column.

The actual data itself is primarily contained in the next two objects.  [The
`horizons`
object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
representing the original two horizons of the black-hole binary and the common
horizon that forms at merger.  In matter simulations, one or more of these may
be `None`.  Otherwise, each of these three is a
[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
object, containing several timeseries relating to mass, spin, and position.

Finally, the
[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
encapsulates the modes of the waveform and the corresponding time information,
along with relevant metadata like data type, spin weight, etc., and useful
features like numpy-array-style slicing.


%package -n python3-sxs
Summary:	Interface to data produced by the Simulating eXtreme Spacetimes collaboration
Provides:	python-sxs
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-sxs
[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)


# Simulating eXtreme Spacetimes python package

The `sxs` python package provides a high-level interface for using data
produced by the SXS collaboration.  In particular, the function `sxs.load` can
automatically find, download, and load data, returning objects that provide
common interfaces to the various types of data, without forcing the user to
worry about details like data formats or where to find the data.  It can also
automatically select the newest or highest-resolution dataset for a given
simulation, or return a range of versions or resolutions.  Currently, the
high-level objects encapsulate

  * Catalog — a listing of all data produced by the SXS collaboration
  * Metadata — data describing the simulation parameters
  * Horizons — time-series data describing the apparent horizons
  * Waveforms — time-series data describing the extrapolated gravitational-wave
    modes


## Installation

Because this package is pure python code, installation is very simple.  In
particular, with a reasonably modern installation, you can just run a command
like

```bash
conda install -c conda-forge sxs
```

or

```bash
python -m pip install sxs
```

Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
installation of python, which is the most recommended approach for scientific
python; the second command assumes that you have an appropriate python
environment set up in some other way.  Either of these commands will download
and install the `sxs` package and its most vital requirements.

If you want to install all the goodies that enable things like jupyter
notebooks with plots and interactive tables, you could run

```bash
conda install -c conda-forge sxs-ecosystem
```

or

```bash
python -m pip install sxs[ecosystem]
```

You will probably also want to set some sensible defaults to automatically
download and cache data:

```bash
python -c "import sxs; sxs.write_config(download=True, cache=True)"
```

This will write a configuration file in the directory returned by
`sxs.sxs_directory("config")`, and downloaded data will be cached in the
directory returned by `sxs.sxs_directory("cache")`.  See [that function's
documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
for details.


## Usage

An extensive demonstration of this package's capabilities is available
[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
interactive jupyter notebooks that are actually running this code and some
pre-downloaded data.  The following is just a very brief overview of the `sxs`
package's main components.

There are four important objects to understand in this package:

```python
import sxs

catalog = sxs.load("catalog")
metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
```

[The `catalog`
object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
contains information about every simulation in the catalog, including all
available data files, and information about how to get them.  You probably
don't need to actually know about details like where to get the data, but
`catalog` can help you find the simulations you care about.  Most importantly,
`catalog.simulations` is a `dict` object, where the keys are names of
simulations (like "SXS:BBH:0123") and the values are the same types as [the
`metadata`
object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
which contains metadata about that simulation — things like mass ratio, spins,
etc.  This `metadata` reflects the actual output of the simulations, which
leads to some inconsistencies in their formats.  A more consistent interface
(though it is biased toward returning NaNs where a human might glean more
information) is provided by `catalog.table`, which returns a
[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
types for each column.

The actual data itself is primarily contained in the next two objects.  [The
`horizons`
object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
representing the original two horizons of the black-hole binary and the common
horizon that forms at merger.  In matter simulations, one or more of these may
be `None`.  Otherwise, each of these three is a
[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
object, containing several timeseries relating to mass, spin, and position.

Finally, the
[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
encapsulates the modes of the waveform and the corresponding time information,
along with relevant metadata like data type, spin weight, etc., and useful
features like numpy-array-style slicing.


%package help
Summary:	Development documents and examples for sxs
Provides:	python3-sxs-doc
%description help
[![Test Status](https://github.com/sxs-collaboration/sxs/workflows/tests/badge.svg)](https://github.com/sxs-collaboration/sxs/actions)
[![Documentation Status](https://readthedocs.org/projects/sxs/badge/?version=main)](https://sxs.readthedocs.io/en/main/?badge=main)
[![PyPI Version](https://img.shields.io/pypi/v/sxs?color=)](https://pypi.org/project/sxs/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/sxs.svg?color=)](https://anaconda.org/conda-forge/sxs)
[![MIT License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/sxs-collaboration/sxs/blob/main/LICENSE)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/moble/sxs_notebooks/master)


# Simulating eXtreme Spacetimes python package

The `sxs` python package provides a high-level interface for using data
produced by the SXS collaboration.  In particular, the function `sxs.load` can
automatically find, download, and load data, returning objects that provide
common interfaces to the various types of data, without forcing the user to
worry about details like data formats or where to find the data.  It can also
automatically select the newest or highest-resolution dataset for a given
simulation, or return a range of versions or resolutions.  Currently, the
high-level objects encapsulate

  * Catalog — a listing of all data produced by the SXS collaboration
  * Metadata — data describing the simulation parameters
  * Horizons — time-series data describing the apparent horizons
  * Waveforms — time-series data describing the extrapolated gravitational-wave
    modes


## Installation

Because this package is pure python code, installation is very simple.  In
particular, with a reasonably modern installation, you can just run a command
like

```bash
conda install -c conda-forge sxs
```

or

```bash
python -m pip install sxs
```

Here, `conda` requires the [conda](https://docs.anaconda.com/anaconda/install/)
installation of python, which is the most recommended approach for scientific
python; the second command assumes that you have an appropriate python
environment set up in some other way.  Either of these commands will download
and install the `sxs` package and its most vital requirements.

If you want to install all the goodies that enable things like jupyter
notebooks with plots and interactive tables, you could run

```bash
conda install -c conda-forge sxs-ecosystem
```

or

```bash
python -m pip install sxs[ecosystem]
```

You will probably also want to set some sensible defaults to automatically
download and cache data:

```bash
python -c "import sxs; sxs.write_config(download=True, cache=True)"
```

This will write a configuration file in the directory returned by
`sxs.sxs_directory("config")`, and downloaded data will be cached in the
directory returned by `sxs.sxs_directory("cache")`.  See [that function's
documentation](https://sxs.readthedocs.io/en/main/api/sxs.utilities.sxs_directories/#sxsutilitiessxs_directoriessxs_directory)
for details.


## Usage

An extensive demonstration of this package's capabilities is available
[here](https://mybinder.org/v2/gh/moble/sxs_notebooks/master), in the form of
interactive jupyter notebooks that are actually running this code and some
pre-downloaded data.  The following is just a very brief overview of the `sxs`
package's main components.

There are four important objects to understand in this package:

```python
import sxs

catalog = sxs.load("catalog")
metadata = sxs.load("SXS:BBH:0123/Lev/metadata.json")
horizons = sxs.load("SXS:BBH:0123/Lev/Horizons.h5")
waveform = sxs.load("SXS:BBH:0123/Lev/rhOverM", extrapolation_order=2)
```

[The `catalog`
object](https://sxs.readthedocs.io/en/main/api/sxs.catalog.catalog/#sxs.catalog.catalog.Catalog)
contains information about every simulation in the catalog, including all
available data files, and information about how to get them.  You probably
don't need to actually know about details like where to get the data, but
`catalog` can help you find the simulations you care about.  Most importantly,
`catalog.simulations` is a `dict` object, where the keys are names of
simulations (like "SXS:BBH:0123") and the values are the same types as [the
`metadata`
object](https://sxs.readthedocs.io/en/main/api/sxs.metadata.metadata/#sxs.metadata.metadata.Metadata),
which contains metadata about that simulation — things like mass ratio, spins,
etc.  This `metadata` reflects the actual output of the simulations, which
leads to some inconsistencies in their formats.  A more consistent interface
(though it is biased toward returning NaNs where a human might glean more
information) is provided by `catalog.table`, which returns a
[`pandas`](https://pandas.pydata.org/docs/) `DataFrame` with specific data
types for each column.

The actual data itself is primarily contained in the next two objects.  [The
`horizons`
object](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.Horizons)
has three attributes — `horizons.A`, `horizons.B`, and `horizons.C` — typically
representing the original two horizons of the black-hole binary and the common
horizon that forms at merger.  In matter simulations, one or more of these may
be `None`.  Otherwise, each of these three is a
[`HorizonQuantities`](https://sxs.readthedocs.io/en/main/api/sxs.horizons/#sxs.horizons.HorizonQuantities)
object, containing several timeseries relating to mass, spin, and position.

Finally, the
[`waveform`](https://sxs.readthedocs.io/en/main/api/sxs.waveforms.waveform_modes/#sxs.waveforms.waveform_modes.WaveformModes)
encapsulates the modes of the waveform and the corresponding time information,
along with relevant metadata like data type, spin weight, etc., and useful
features like numpy-array-style slicing.


%prep
%autosetup -n sxs-2022.4.5

%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-sxs -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2022.4.5-1
- Package Spec generated