semvec/pypi-packages huggingface dataset:
astroemu 0.1.2
Generalised framework for emulating spectral signals.
Requires Python: >=3.11

# Next Generation Emulators for Cosmology and Astrophysics

| | |
|---| ---|
| Author| Harry Bevins|
| Version| 0.1.2 |
| Homepage | https://github.com/htjb/astroemu |

UNDER DEVELOPMENT

`astroemu` implements a generalized framework for emulating 
spectral signals
and is inspired by the [`globalemu`](https://github.com/htjb/globalemu) package.

The neural network emulators are implemented in JAX and the dataloaders are
built on top of PyTorch.

As with `globalemu` the idea is to input the independent variables alongside 
the physical parameters of your model then predicting a single corresponding 
spectral value. Full spectra can then be generated via a vectorised call to 
the network. The training data is tiled in the dataloaders so that the 
parameters and independent variables are concatenated as inputs and
stacked up alongside the outputs. For example if we have a signal 
$y = f(x, \theta)$ and we have N $\theta$ samples and m $x$ and $y$ values then
our training data looks like

|Input|Output|
|--|--|
|[$\theta_{0}$, $x_0$]| $y_0$ |
|[$\theta_{0}$, $x_1$]| $y_1$ |
|[$\theta_{0}$, $x_2$]| $y_2$ |
|[$\theta_{0}$, ...]|...|
|[$\theta_{0}$, $x_m$]|$y_m$|
|[..., ...]| ...|
|[$\theta_N$, $x_m$]|$y_m$|

For more details see the `globalemu` [paper](https://arxiv.org/abs/2104.04336).
A paper is in preparation demonstrating applications of this package to a broad 
range of astrophysical signals.

## Contributions

Contributions are welcome! Please open an issue to discuss and have a 
read of the Contribution guidelines.
pyarrow 23.0.1
Python library for Apache Arrow
Requires Python: >=3.10

<!---
  Licensed to the Apache Software Foundation (ASF) under one
  or more contributor license agreements.  See the NOTICE file
  distributed with this work for additional information
  regarding copyright ownership.  The ASF licenses this file
  to you under the Apache License, Version 2.0 (the
  "License"); you may not use this file except in compliance
  with the License.  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing,
  software distributed under the License is distributed on an
  "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  KIND, either express or implied.  See the License for the
  specific language governing permissions and limitations
  under the License.
-->

## Python library for Apache Arrow

[![pypi](https://img.shields.io/pypi/v/pyarrow.svg)](https://pypi.org/project/pyarrow/) [![conda-forge](https://anaconda.org/conda-forge/pyarrow/badges/version.svg)](https://anaconda.org/conda-forge/pyarrow)

This library provides a Python API for functionality provided by the Arrow C++
libraries, along with tools for Arrow integration and interoperability with
pandas, NumPy, and other software in the Python ecosystem.

## Installing

Across platforms, you can install a recent version of pyarrow with the conda
package manager:

```shell
conda install pyarrow -c conda-forge
```

On Linux, macOS, and Windows, you can also install binary wheels from PyPI with
pip:

```shell
pip install pyarrow
```

If you encounter any issues importing the pip wheels on Windows, you may need
to install the latest [Visual C++ Redistributable for Visual Studio][3].

## Development

See [Python Development][2] in the documentation subproject.

### Building the documentation

See [documentation build instructions][1] in the documentation subproject.

[1]: https://github.com/apache/arrow/blob/main/docs/source/developers/documentation.rst
[2]: https://arrow.apache.org/docs/developers/python/index.html
[3]: https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170#latest-microsoft-visual-c-redistributable-version