semvec/pypi-packages huggingface dataset:
psh-pytorch 0.0.3
Perfect Spatial Hashing in PyTorch
Requires Python: >=3.7

# Perfect Spatial Hashing in PyTorch
This library is an unofficial implementation of [Perfect Spatial Hashing by Lefebvre and Hoppe](https://hhoppe.com/perfecthash.pdf).

## Usage
Install the library with:
```bash
# For the latest version:
pip install git+https://github.com/dli7319/psh-pytorch.git

# For the PyPI version:
# pip install psh-pytorch
```

Instantiate a `PerfectSpatialHash` object with:
```python
from psh_pytorch import PerfectSpatialHash

occupancy_grid # A 2D (or higher dimensional) occupancy grid
out_features = 3
perfect_hash = PerfectSpatialHash(
    occupancy_grid, out_features)

# 2D forward pass
indices = torch.stack(torch.meshgrid(
    torch.arange(128, device=device),
    torch.arange(128, device=device),
    indexing='ij'), -1)
values, sparsity = perfect_hash(indices.reshape(-1, 2))
#   Values are of shape (128 * 128, 3)
#   Sparsity is of shape (128 * 128)
#   Note that values are unmasked
masked_values = values * sparsity.unsqueeze(-1)
```

## Examples
See `examples/` for a simple example of how to use the library.

If you clone the repository, you can run the example with:
```bash
python -m examples.2D_bulb
```

## Limitations

1. Currently, interpolation is not supported. Hence, you must use long indices and there are no gradients with respect to the indices.

## Development

* Run tests with `pytest`.

## Acknowledgement
If you find this library useful, please consider citing the original paper:
```bibtex
@article{lefebvre2006perfect,
author = {Lefebvre, Sylvain and Hoppe, Hugues},
title = {Perfect Spatial Hashing},
year = {2006},
issue_date = {July 2006},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {25},
number = {3},
issn = {0730-0301},
url = {https://doi.org/10.1145/1141911.1141926},
doi = {10.1145/1141911.1141926},
journal = {ACM Trans. Graph.},
month = {jul},
pages = {579–588},
numpages = {10},
keywords = {vector images, minimal perfect hash, sparse data, adaptive textures, multidimensional hashing, 3D-parameterized textures}
}
```
hxrate 1.0.1
HX Rate Fitting
Requires Python: >=3.9

HXRATE

This package contains modules for hydrogen deuterium exchange (hdx) mass spectrometry data analysis. It contains:

1. backexchange: for backexchange calculations
2. ratefit: for fitting hdx rates
3. openfe: for calculating opening free energy values from hdx rates
4. hxdata: contains methods for reading and writing data
5. methods: contains helper methods

For usage, please refer to the following repository:
https://github.com/sugyandixit/hxrate_pipeline

Under scripts/hxrate/ there are python scripts that use HXRATE package for rate fitting and opening free energy calculations

You could also use hxrate_pipeline repository for distributed computing in High Performance Computing clusters
(Please refer to README in hxrate_pipeline repository)

Have fun fitting!
climate-learn 1.0.0
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Requires Python: >=3.8

<h1 align="center">ClimateLearn</h1>

[![Documentation Status](https://readthedocs.org/projects/climatelearn/badge/?version=latest)](https://climatelearn.readthedocs.io/en/latest/?badge=latest)
[![CI Build Status](https://github.com/aditya-grover/climate-learn/actions/workflows/ci.yaml/badge.svg)](https://github.com/aditya-grover/climate-learn/actions/workflows/ci.yaml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing)

**ClimateLearn** is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods. For further context on our past motivation and future plans, check out our announcement [blog post](https://aditya-grover.github.io/blog/2023/climate-learn/).

## Usage

[**Python 3.8+**](https://www.python.org/) is required. The xESMF package has to be installed separately since one of its dependencies, ESMpy, is available only through Conda.
```
conda install -c conda-forge xesmf
pip install climate-learn
```

### Quickstart
We have a quickstart notebook in the `notebooks` folder titled `Quickstart.ipynb`. It is intended for use in Google Colab and can be launched by clicking the Google Colab badge above or this link: https://colab.research.google.com/drive/1LcecQLgLtwaHOwbvJAxw9UjCxfM0RMrX?usp=sharing.

We also previewed some key features of ClimateLearn at a spotlight tutorial in the "Tackling Climate Change with Machine Learning" Workshop at the Neural Information Processing Systems 2022 Conference. The slides and recorded talk can be found on [Climate Change AI's website](https://www.climatechange.ai/papers/neurips2022/114).

### Documentation
Find us on [ReadTheDocs](https://climatelearn.readthedocs.io/).

## About Us
ClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by [Professor Aditya Grover](https://aditya-grover.github.io).

## Contributing
Contributions are welcome! See our [contributing guide](https://github.com/aditya-grover/climate-learn/blob/main/CONTRIBUTING.md).

## Citing ClimateLearn
If you use ClimateLearn, please see the [`CITATION.cff`](https://github.com/aditya-grover/climate-learn/blob/main/CITATION.cff) file or use the citation prompt provided by GitHub in the sidebar.
rcdss 0.9
RIPE NCC CDS Scanner
Requires Python: >=3.6

RIPE NCC CDS scanner
====================

This utility implements support for automated DNSSEC delegation
trust maintenance for the reverse DNS zones delegated by RIPE database.
It implements scanning for CDS records according to [RFC
7344](https://tools.ietf.org/html/rfc7344) and [RFC
8078](https://tools.ietf.org/html/rfc8078).

Only trust anchor update and remove is supported. Bootstrapping from
insecure to secure is not supported.

It reads a [dump of DOMAIN objects](https://ftp.ripe.net/ripe/dbase/split/ripe.db.domain.gz) from the RIPE database. Only domain objects
containing `ds-rdata:` attributes are processed.

CDS records are scanned using default resolver of the host, which MUST be
DNSSEC-aware and SHOULD perform DNSSEC-validation. The utility outputs RPSL-like
file listing objects that should be modified in the RIPE database. Since the
scanner works with *dummyfied* objects, output cannot be directly pushed into
the Database.  Instead, it has to be used as a diff-file for a GET-modify-PUT
operation on the database.

DNSSEC algorithm support
------------------------

The utility does all the special validations mandated by RFC 7344. These are
done using [dnspython](https://www.dnspython.org/). Since these validations
provide similar level of security to standard DNSSEC validation process,
validation in the DNS resolver is not required.

Therefore, the list of supported algorithms is same as the list of supported
DNSSEC algorithms of `dnspython`.

Installation and usage
----------------------

This package can be installed using [`pip`](https://pypi.org/project/pip/),
preferably into its own
[`virtualenv`](https://docs.python.org/3/tutorial/venv.html).

    $ python3 -m venv rcdss-venv
    $ source rcdss-venv/bin/activate
    (rcdss-venv)$ pip install rcdss
    (rcdss-venv)$ rcdss --help