semvec/pypi-packages huggingface dataset:
pandare 1.8.83
Python Interface to PANDA
Requires Python: >=3.6

pandare: PANDA's Python Interface - PyPI Release
========
Overview
--
`pandare` is a Python 3 interface to PANDA. With pandare (also called PyPANDA), you can quickly develop plugins
to analyze behavior of a running system, record a system and analyze replays, or do
nearly anything you can do using PANDA's C/C++ APIs.

Autogenerated documentation is available at [docs.panda.re](https://docs.panda.re).

Installation
--
Note the PANDARE package provided on pypi only a binary distribution and will certainly only work on `x86_64` hosts as pre-built PANDA libraries from Ubuntu 20.04 are included.

Prior to using pandare **you must install some dependencies**, these are fully described in the code's dependencies folder, i.e., [the list for ubuntu:20.04 is here](https://github.com/panda-re/panda/blob/dev/panda/dependencies/ubuntu_20.04_base.txt). On our development systems, we typically find we just need to install:
```
apt install libvdeplug-dev libpng16-16 libsdl2-2.0-0
```

Then simply install with `pip3 install pandare`. From there, you'll be able to use PANDA with:


```
from pandare import Panda
panda = Panda(generic='i386')
...
```
loady 1.9.0
🛍 Load Python code and data from git 🛍
Requires Python: >=3.10

# loady

Dynamically load Python libraries, JSON and raw text from git repositories and
the web.

# Basic usage

    import loady

    # Permanently extend sys path with this github repo.
    loady.sys_path.extend('//git/github.com/timedata-org/simple'):

    # Temporarily extend sys path with this github repo.
    with loady.sys_path.extender('//git/github.com/timedata-org/simple'):
        # Do stuff.

    # Get raw data from URLs.
    result = loady.data.load(
        'https://github.com/timedata-org/simple/blob/master/simple.json')

    # Actually gets the raw data from this URL:
    # https://raw.githubusercontent.com/timedata-org/simple/master/simple.json
    # and reads it as JSON.

    #  Loads and compiles Python code from that URL.
    result = loady.code.load(
        'https://github.com/timedata-org/simple/blob/master/test.py')
societal-costs-pipeline-2026-02-03-1700 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.

## 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.