semvec/pypi-packages huggingface dataset:
gwas-norm 0.4.2b0
Normalise GWAS studies and map associations to rsIDs.
Requires Python: <3.14,>=3.9

# Getting Started with GWAS Normalisation

__version__: `0.4.2b0`

The gwas-norm package is a toolkit to normalise GWAS summary statistics flat files. It aims to handle some of the most common scenarios and produce a flat file with:

1. A standardised column order
2. Uniform genome assembly
3. Indel noramlisation, variant validation
4. Latest variant IDs
5. Fixing out of range p-values
6. Basic functional annotations

There is [online](https://cfinan.gitlab.io/gwas-norm/index.html) documentation for gwas-norm.

## Installing the Python package

gwas-norm can be installed via PyPi or Conda.

PyPi:
```
pip install gwas-norm
```

Conda:

```
conda install -c conda-forge -c bioconda -c cfin gwas-norm
```

 It is recommended to use Python version 3.13, but should work on earlier versions, although it has not been tested extensively on them. If in doubt, you can install then clone the repositary and run `pytest ./tests`.

## Next steps...

There is a [TLDR](); that should get you started. For University College London (UCL) users there is a [UCL specific TLDR;]()
hf-mem 0.4.4
A CLI to estimate inference memory requirements for Hugging Face models, written in Python.
Requires Python: >=3.10

<img src="https://github.com/user-attachments/assets/509a8244-8a91-4051-b337-41b7b2fe0e2f" />

---

> [!WARNING]
> `hf-mem` is still experimental and therefore subject to major changes across releases, so please keep in mind that breaking changes may occur until v1.0.0.

`hf-mem` is a CLI to estimate inference memory requirements for Hugging Face models, written in Python. `hf-mem` is lightweight, only depends on `httpx`, as it pulls the [Safetensors](https://github.com/huggingface/safetensors) metadata via [HTTP Range requests](https://developer.mozilla.org/en-US/docs/Web/HTTP/Guides/Range_requests). It's recommended to run with [`uv`](https://github.com/astral-sh/uv) for a better experience.

`hf-mem` lets you estimate the inference requirements to run any model from the Hugging Face Hub, including [Transformers](https://github.com/huggingface/transformers), [Diffusers](https://github.com/huggingface/diffusers) and [Sentence Transformers](https://github.com/huggingface/sentence-transformers) models, as well as any model that contains [Safetensors](https://github.com/huggingface/safetensors) compatible weights.

Read more information about `hf-mem` in [this short-form post](https://alvarobartt.com/hf-mem).

## Usage

### Transformers

```bash
uvx hf-mem --model-id MiniMaxAI/MiniMax-M2
```

<img src="https://github.com/user-attachments/assets/530f8b14-a415-4fd6-9054-bcd81cafae09" />

### Diffusers

```bash
uvx hf-mem --model-id Qwen/Qwen-Image
```

<img src="https://github.com/user-attachments/assets/cd4234ec-bdcc-4db4-8b01-0ac9b5cd390c" />

### Sentence Transformers

```bash
uvx hf-mem --model-id google/embeddinggemma-300m
```

<img src="https://github.com/user-attachments/assets/2844582f-6207-415a-bc6c-27569a5eb262" />

## Experimental

By enabling the `--experimental` flag, you can enable the KV Cache memory estimation for LLMs (`...ForCausalLM`) and VLMs (`...ForConditionalGeneration`), even including a custom `--max-model-len` (defaults to the `config.json` default), `--batch-size` (defaults to 1), and the `--kv-cache-dtype` (defaults to `auto` which means it uses the default data type set in `config.json` under `torch_dtype` or `dtype`, or rather from `quantization_config` when applicable).

```bash
uvx hf-mem --model-id MiniMaxAI/MiniMax-M2 --experimental
```

<img src="https://github.com/user-attachments/assets/247113cf-59a7-4f76-a8df-735e292558a0" />

## (Optional) Agent Skills

Optionally, you can add `hf-mem` as an agent skill, which allows the underlying coding agent to discover and use it when provided as a [`SKILL.md`](.skills/hf-mem/SKILL.md).

More information can be found at [Anthropic Agent Skills and how to use them](https://github.com/anthropics/skills).

## References

- [Safetensors Metadata parsing](https://huggingface.co/docs/safetensors/en/metadata_parsing)
- [usgraphics - TR-100 Machine Report](https://github.com/usgraphics/usgc-machine-report)
Topsis-Akshat-102303730 1.0.0
A Python package for TOPSIS multi-criteria decision analysis
Requires Python: >=3.6

# TOPSIS Python Package

**TOPSIS** (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method.

## Installation

```bash
pip install Topsis-Akshat-102303730
```

## Usage

### Command Line

```bash
topsis <input_file> <weights> <impacts> <output_file>
```

### Parameters

- **input_file**: Path to input CSV file
- **weights**: Comma-separated weights (e.g., "1,1,1,2")
- **impacts**: Comma-separated impacts, either + or - (e.g., "+,+,-,+")
- **output_file**: Path to output CSV file

### Example

```bash
topsis data.csv "1,1,1,2" "+,+,-,+" result.csv
```

### Input File Format

The input CSV file should have:
- First column: Object/item names
- Remaining columns: Criteria values (numeric)

Example:
```
Model,Price,Storage,Camera,Looks
M1,250,16,12,5
M2,200,16,8,3
M3,300,32,16,4
```

### Output

The output file will contain:
- All original columns
- **Topsis Score**: Calculated TOPSIS score
- **Rank**: Rank based on TOPSIS score

## Requirements

- Python >= 3.6
- pandas >= 1.0.0
- numpy >= 1.18.0

## License

MIT License

## Author

Akshat Kakkar

## Links

- GitHub: https://github.com/akshatkakkar1/-UCS654
societal-costs-pipeline-2026-02-04-1200 0.1.0
Societal costs pipeline CLI.
Requires Python: >=3.9

# societal_costs

Workspace for the societal costs study.

## Crates

- `crates/costs_core` — core domain types, aggregation, pricing, and validation.
- `crates/costs_models` — two-part, Tweedie, and log-normal models + selection tools.
- `crates/costs_matching` — matching + balance diagnostics.
- `crates/costs_cohort` — cohort assignment + diagnostics.
- `crates/costs_components` — register-derived component builders (LMDB volume so far).
- `crates/costs_pipeline` — configuration, orchestration, and report outputs.

## LMDB example

See `crates/costs_pipeline/README.md` for the LMDB ingestion example.

## Pipeline config

Start from `pipeline.example.toml` and update the register file paths to match
your local dataset layout. Most SAS7BDAT paths accept either a single file
or a directory (all `.sas7bdat` files are loaded, sorted by name). You can
also set `base_path` in the config to prefix all relative register paths.
If you use contact-based SCD classification, set
`components.lpr.scd_inpatient_contact_types` (or flip
`components.lpr.scd_allow_outpatient = true` to allow outpatient contacts).

Pipeline outputs include consolidated QC under `qc_summary.csv/json`, plus
step-specific outputs such as `lpr_episode_qc.csv`, `matching_outcome_qc.csv`,
`matching_parent_qc.csv`, and `population_public_stats.csv`.

For terminal progress and timing, set:
- `pipeline.progress = true`
- `pipeline.log_level = "info"` (or `debug`)

## Additional docs

- `crates/costs_core/README.md` — core domain and aggregation usage.
- `crates/costs_models/README.md` — model APIs and examples.
- `crates/costs_models/examples/README.md` — runnable model examples.
- `crates/costs_components/README.md` — LMDB note (missing price fields).
- `crates/costs_pipeline/README.md` — pipeline usage and LMDB example.