semvec/pypi-packages huggingface dataset:
mnemosyne-journal 3.0.1
A simple encrypted journaling application that is named after the ancient Greek goddess of memories.
Requires Python: >=3.13
Keywords: journaling

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# Mnemosyne Journaling

A simple encrypted journaling application. Named after the ancient Greek goddess of memories.

## Usage

This app exposes a command line utility at `siru-mnemosyne`. Use `siru-mnemosyne --help` to get more information about available command arguments. Or simply run `siru-mnemosyne` to use the app in an interactive manner.

As for the command name, it is rather long, but this avoids having namespace issues with other apps as it is using a preexisting name. It is always possible to alias this to something quicker to type on the target system. For Windows look into the `doskey` command, and for Unix/Linux/MacOS look into the `alias` command.

## Encryption

The user entered password is hashed using the Argon2 hashing algorithm ([2015 Password Hashing Competition Winner and current recomendation (last checked 2025-10-22)](https://www.password-hashing.net/)). The hashing parameters used are 64MiB or RAM, 3 iterations, and a parralelism of 4.

The actual encryption is done using aes256 bit, with a 15 bit nonce. The aes mode used is OCB (offset codebook) which include both encryption and authentication (using a MAC tag) of the encrypted jouranl entry.

## Storage

The journal entries are stored in the user home folder under a `.mnemosyne/entries` subdirectory. These are plain text files containing the various encrypted pieces of information. This allows for portability with other software and prevents so called "lock-in," as all the information needed for decryption (apart from the password) is stored directly with each journal entry.

## Documentation

See full code documentation at the wiki section of the repository. This is located at [wiki](https://codeberg.org/siru/mnemosyne-journal/wiki).

## Lastly

To follow in the footsteps of one of my professors, if you actually read this far, please send me a simple picture of a cute otter, and I will be pleasantly surprised that people would actually read all of this.
eICU-MEDS 0.0.2
An ETL pipeline to extract the eICU dataset into the MEDS format.
Requires Python: >=3.11

# eICU MEDS Extraction ETL

[![PyPI - Version](https://img.shields.io/pypi/v/eICU-MEDS)](https://pypi.org/project/eICU-MEDS/)
[![Documentation Status](https://readthedocs.org/projects/etl-meds/badge/?version=latest)](https://etl-meds.readthedocs.io/en/stable/?badge=stable)
![Static Badge](https://img.shields.io/badge/MEDS-0.3.3-blue)
[![codecov](https://codecov.io/gh/Medical-Event-Data-Standard/eICU_MEDS/graph/badge.svg?token=RW6JXHNT0W)](https://codecov.io/gh/Medical-Event-Data-Standard/eICU_MEDS)
[![tests](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/actions/workflows/tests.yaml/badge.svg)](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/actions/workflows/tests.yml)
[![code-quality](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/actions/workflows/code-quality-main.yaml/badge.svg)](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/actions/workflows/code-quality-main.yaml)
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[![license](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](https://github.com/Medical-Event-Data-Standard/eICU_MEDS#license)
[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/pulls)
[![contributors](https://img.shields.io/github/contributors/Medical-Event-Data-Standard/eICU_MEDS.svg)](https://github.com/Medical-Event-Data-Standard/eICU_MEDS/graphs/contributors)

This repository contains the code for downloading the
[eICU dataset](https://physionet.org/content/eicu-crd/2.0/) from PhysioNet and transforming it into the
[Medical Event Data Standard (MEDS)](https://medical-event-data-standard.org/) format.

```bash
pip install eICU-MEDS # use `pip install -e .` for local installation in editing mode
export DATASET_DOWNLOAD_USERNAME=$PHYSIONET_USERNAME
export DATASET_DOWNLOAD_PASSWORD=$PHYSIONET_PASSWORD
MEDS_extract-eICU root_output_dir=data/eicu_meds do_download=False
```

## MEDS-transforms settings

If you want to convert a large dataset, you can use parallelization with MEDS-transforms
(the MEDS-transformation step that takes the longest).

Using local parallelization with the `hydra-joblib-launcher` package, you can set the number of workers:

```
pip install hydra-joblib-launcher --upgrade
```

Then, you can set the number of workers as environment variable:

```bash
export N_WORKERS=8
```

Moreover, you can set the number of subjects per shard to balance the parallelization overhead based on how many
subjects you have in your dataset:

```bash
export N_SUBJECTS_PER_SHARD=100000
```
visionsim 0.1.0a2
Towards a Sensor-Realistic World Simulator
Requires Python: >=3.9

# VisionSIM: Towards a Sensor-Realistic World Simulator

A modular and extensible framework that realistically emulates many different sensor types, alongside rich pixel-perfect ground truth annotations across low-, mid-, and high-level scene characteristics, as well as intrinsic and extrinsic camera properties.

*Warning:* This project is under heavy development and still considered unstable.
taml 1.1.0
YAML but with tabs
Requires Python: >=3.10

# TAML

YAML but with tabs

## Installation

```bash
pip install taml
```

## Usage

see [ruamel.yaml](https://yaml.readthedocs.io/en/latest/) and replace

```python
from ruamel.yaml import YAML
yaml=YAML()
```

with

```python
from taml import taml
```