semvec/pypi-packages huggingface dataset:
societal-costs-pipeline-2026-02-04-1300 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.
File filtering uses `study.data_*` and optional `study.baseline_*` /
`study.followup_*` windows to avoid reading out-of-scope files.
Event registers (VNDS/DOD/DODSAARS/DODSAASG) are **not** year-filtered.
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.
caste 0.1.2
Opinionated hardware classification library.
Requires Python: >=3.8
Keywords: hardware, classification, system-info, gpu, cpu

# caste - T-Shirt sizes for hardware

Opinionated hardware classification library for Python.

`caste` is a deliberately simple library that inspects a user’s machine and assigns it to a small, practical "hardware caste" (like Mini, User, or Developer). This helps you set sensible defaults for your application based on the user's hardware without manually parsing RAM, CPU, and GPU stats.

## Installation

```bash
pip install caste
```

## Quick Start

```python
import caste

# Get a simple one-word label for the machine
print(caste.detect_caste_word())  # e.g., "User"

# Get the label and the reason for the classification
label, reason = caste.detect_caste()
print(f"Caste: {label} (Reason: {reason})")

# Get raw hardware facts if you want to perform your own logic
facts = caste.detect_hw_facts()
print(f"RAM: {facts['ram_bytes'] / 1024**3:.1f} GB")
print(f"Discrete GPU: {facts['has_discrete_gpu']}")
```

## Hardware Castes

- **Mini** — Microcomputers, embedded systems, classic/legacy PCs.
- **User** — Standard consumer-level machines.
- **Developer** — High-end personal computers.
- **Workstation** — Professional content-creation or high-end gaming machines.
- **Rig** — Dedicated servers or specialised compute systems.

## API Reference

`detect_hw_facts()` returns a dictionary with:

- `ram_bytes` (int)
- `physical_cores` (int)
- `logical_threads` (int)
- `gpu_kind` (0=None, 1=Integrated, 2=Unified, 3=Discrete)
- `vram_bytes` (int)
- `has_discrete_gpu` (bool)
- `is_apple_silicon` (bool)
- `is_intel_arc` (bool)

## Why use caste?

Modern applications often launch with no idea of the underlying hardware capability. If defaults are tuned only for a developer’s machine, it can lead to sluggish experiences on low-end hardware or not showing off your apps's best capabilities on high-end systems. `caste` provides a standardized way to bridge this gap.

There is also a C++ library and an executable for shell scripts and build systems. See the Github repo for details.

## License

MIT
agox 3.11.1
Atomistic Global Optimziation X is a framework for structure optimization in materials science.
Requires Python: >=3.10

# Atomistic Global Optimization X (AGOX)

![logo](docs/source/logos/logo-carbon.gif)


AGOX is a package for global optimization of atomic system using e.g. the energy 
calculated from density functional theory as the objective function. AGOX interfaces 
with the Atomistic Simulation Environment (ASE) and as such supports any of 
calculators in ASE as objectives for optimization. 

AGOX is built to be flexible, consisting of modules that can be put together to 
create an optimization algorithm, from simple random searches to Bayesian searches 
guided by a surrogate model and many more. 

Check out the documentation at 

https://agox.gitlab.io/agox/

## Installation

AGOX can be installed from PyPI 

```
pip install agox
```

Or from source by cloning the repo.

## Contributions & Issues 

Feel free to make a fork and submit a merge request! 

If you have an issue or a question please post it on the issue board! 

## Authors 

The main AGOX framework has been written by 
* Mads-Peter Verner Christiansen
* Nikolaj Rønne
* Bjørk Hammer

with inspiration and help from current and previous members of the Hammer group at Aarhus University, Denmark.
Parts of the code have been contributed by

* Andreas Slavensky (Complementary Energy Generator)
* Florian Brix (Symmetry generators)
* Joe Pitfield (Replica-exchange)
* Jon Eunan Quinlivan Dominguez (Grand canonical)

## License

AGOX is released under the GPLv3 license.