semvec/pypi-packages huggingface dataset:
deltachat-rpc-client 2.43.0
Python client for Delta Chat core JSON-RPC interface
Requires Python: >=3.10

# Delta Chat RPC python client

RPC client connects to standalone Delta Chat RPC server `deltachat-rpc-server`
and provides asynchronous interface to it.

## Getting started

To use Delta Chat RPC client, first build a `deltachat-rpc-server` with `cargo build -p deltachat-rpc-server`
or download a prebuilt release.
Install it anywhere in your `PATH`.

[Create a virtual environment](https://docs.python.org/3/library/venv.html)
if you don't have one already and activate it.
```
$ python -m venv env
$ . env/bin/activate
```

Install `deltachat-rpc-client` from source:
```
$ cd deltachat-rpc-client
$ pip install .
```

## Testing

1. Build `deltachat-rpc-server` with `cargo build -p deltachat-rpc-server`.
2. Install tox `pip install -U tox`
3. Run `CHATMAIL_DOMAIN=nine.testrun.org PATH="../target/debug:$PATH" tox`.

Additional arguments to `tox` are passed to pytest, e.g. `tox -- -s` does not capture test output.


## Activating current checkout of deltachat-rpc-client and -server for development 

Go to root repository directory and run: 
```
$ scripts/make-rpc-testenv.sh 
$ source venv/bin/activate 
```

## Using in REPL

Setup a development environment:
```
$ tox --devenv env
$ . env/bin/activate
```

```
$ python
>>> from deltachat_rpc_client import *
>>> rpc = Rpc()
>>> rpc.start()
>>> dc = DeltaChat(rpc)
>>> system_info = dc.get_system_info()
>>> system_info["level"]
'awesome'
>>> rpc.close()
```
modelarchive-py 1.1.0
modelarchive - Functionality around ModelArchive.org & ModelCIF.
Requires Python: >=3.10.12

# modelarchive - The ModelArchive Python package

https://modelarchive.org/

`modelarchive` collects all Python functionality developed for ModelArchive
in a single package - essentially the “Everything ModelArchive” toolkit.

Yes, the name is a bit long, but `ma` alone felt too short ;)
You can conveniently import it as:

```python
import modelarchive as ma
```

The package is organised into submodules covering different areas of
functionality. Currently available:

- `modelcif`: help with editing ModelCIF files, translating from legacy PDB
               format and extending existing ModelCIf-formatted files


## Installation

From [PyPI](https://pypi.org/):
```bash
pip install modelarchive-py
```

From source:
```bash
git clone https://git.scicore.unibas.ch/schwede/python-modelarchive
cd python-modelarchive
pip install .
```

For development:
- to see code changes immediately effective in your environment:
```bash
pip install -e .
```

## Versioning/ requirements.txt

`modelarchive` follows [Semantic Versioning](https://semver.org), so you can
safely depend on `modelarchive-py>=<MAJOR VERSION>.0.0` to get updates
without breaking changes.


## Developing ModelArchive

- best install the package to get all requirements resolved
- run pytest as `python -m pytest [-s]`
- in the `doc/` subdirectory, run `make html` to create the documentation
- in the `doc/` subdirectory run `make doctest` to run Doctests


<!--  LocalWords:  modelarchive ModelArchive submodules modelcif PDB ModelCIf
 -->
<!--  LocalWords:  PyPi cd pytest subdirectory html doctest Doctests
 -->
gdpnum 0.1.2
Numeric computation of the privacy parameter in Gaussian Differential Privacy
Requires Python: >=3.10

# gdpnum

[![pypi](https://img.shields.io/pypi/v/gdpnum)](https://pypi.org/project/gdpnum)
[![CI](https://github.com/interpretable-dp/gdpnum/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/interpretable-dp/gdpnum/actions/workflows/ci.yml)
[![arXiv](https://img.shields.io/badge/arXiv-2503.10945-b31b1b.svg)](https://arxiv.org/abs/2503.10945)

Library for numerically computing the correct, non-asymptotic Gaussian Differential Privacy (GDP) guarantees for DP-SGD or similar privacy-preserving algorithms.
Correct, non-asymptotic GDP accurately and faithfully represents the privacy guarantees of many practical algorithms with just a single _mu_ parameter, enabling correct comparisons (unlike fixed epsilon/delta), and accurate conversions to interpretable notions of risk. See Gomez et al. (2026)[^1].

[^1]: [Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning](https://arxiv.org/abs/2503.10945). IEEE SatML 2026.

If you make use of the library or methods, please cite:
```bibtex
@inproceedings{gomez2026gaussian,
  title={Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning},
  author = {Gomez, Juan Felipe and Kulynych, Bogdan and Kaissis, Georgios and du Pin Calmon, Flavio and Hayes, Jamie and Balle, Borja and Honkela, Antti},
  booktitle={2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)},
  year={2026},
  organization={IEEE}
}
```

## Quickstart

You can install the library with pip:
```
pip install gdpnum
```


### DP-SGD
To analyze DP-SGD, you can use:

```python
import gdpnum

mu, regret = gdpnum.dpsgd.get_mu_and_regret_for_dpsgd(
    noise_multiplier=9.4,
    sample_rate=0.328,
    num_steps=2000
)
# (1.5685621993129137, 0.0010208130697719753)
```

We get the numerically computed GDP mu parameter, and regret which
shows the goodness-of-fit of the GDP.

The library also includes an [Opacus-compatible](https://opacus.ai/api/accounting/iaccountant.html) accountant interface:
```python
import gdpnum

acct = gdpnum.dpsgd.CTDAccountant()
acct.step(noise_multiplier=9.4, sample_rate=0.328)
acct.get_mu_and_regret()
```

### General mechanisms
For general mechanisms, the library relies on the privacy loss distribution
objects from the `dp_accounting` package:

```python
import gdpnum

pld = ...

converter = gdpnum.PLDConverter(pld)
mu, regret = converter.get_mu_and_regret()
```

See an example for the US Census TopDown mechanism in the notebooks folder.

To get the tabulation of the trade-off curve function at recommended values of alpha from the paper,
simply run:
```
converter.get_beta()
```