semvec/pypi-packages huggingface dataset:
pyturbo-sf 1.0.7
Python Turbulence Unleashed: Rapid Binning Operator
Requires Python: >=3.11

# PyTurbo
---
[[License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[[Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/downloads/)
[[Documentation](https://img.shields.io/badge/documentation-latest-blue)](https://github.com/aayouche/pyturbo_sf)

<p align="center">
<img src="docs/pyturbo_logo.png" alt="PyTurbo Logo" width="400"/>
</p>

# Overview
---
PyTurbo_SF is a Python package for efficient structure function calculations in 1D, 2D, and 3D data. The package provides optimized implementations for analyzing turbulent flows and other spatially or temporally varying fields. With advanced bootstrapping techniques and adaptive binning, PyTurbo_SF can handle large datasets while maintaining statistical accuracy.

# Features
---
- Fast structure function calculations in 1D, 2D, and 3D
- Optimized memory usage for large datasets
- Advanced bootstrapping with adaptive sampling indices
- Multiple structure function types: longitudinal, transverse, scalar, and combined
- Isotropic averaging for 2D and 3D data
- Parallel processing for improved performance
- Automatic convergence detection based on a standard error threshold (in physical units)
- Comprehensive statistical analysis

**For detailed documentation and examples, see the [PyTurbo_SF documentation](https://pyturbo-sf.readthedocs.io).**

# Installation
---
The easiest method to install PyTurbo_SF is with [pip](https://pip.pypa.io/):

```console
$ pip install pyturbo_sf
```

You can also fork/clone this repository to your local machine and install it locally with pip as well:

```console
$ pip install .
```
clabe 1.2.23
Validate and generate the control digit of a CLABE in Mexico

# CLABE

[![test](https://github.com/cuenca-mx/clabe-python/workflows/test/badge.svg)](https://github.com/cuenca-mx/clabe-python/actions?query=workflow%3Atest)
[![codecov](https://codecov.io/gh/cuenca-mx/clabe-python/branch/main/graph/badge.svg)](https://codecov.io/gh/cuenca-mx/clabe-python)
[![PyPI](https://img.shields.io/pypi/v/clabe.svg)](https://pypi.org/project/clabe/)
[![Downloads](https://pepy.tech/badge/clabe)](https://pepy.tech/project/clabe)

Librería para validar y calcular un número CLABE basado en
https://es.wikipedia.org/wiki/CLABE

## Requerimientos

Python 3.6 o superior.

## Instalación

Se puede instalar desde Pypi usando

```
pip install clabe
```

## Pruebas

Para ejecutar las pruebas

```
$ make test
```

## Uso básico

Obtener el dígito de control de un número CLABE

```python
import clabe
clabe.compute_control_digit('00200000000000000')
```

Para validar si un número CLABE es válido

```python
import clabe
clabe.validate_clabe('002000000000000008')
```

Para obtener el banco a partir de 3 dígitos

```python
import clabe
clabe.get_bank_name('002')
```

Para generar nuevo válido CLABES

```python
import clabe
clabe.generate_new_clabes(10, '002123456')
```

## Para agregar un nuevo banco

A partir de la versión 2.0.0, el paquete se actualizará a **Pydantic v2**, lo que significa que las versiones anteriores ya no recibirán soporte.

Sin embargo, hemos añadido una función para agregar bancos adicionales a la lista, en caso de que sea necesario. Esto se puede hacer sin necesidad de crear un PR. Para agregar un banco, simplemente llama a la siguiente función con el código de Banxico y el nombre del banco:

```python
import clabe
clabe.add_bank('12345', 'New Bank')
```

Para eliminar un banco

```python
import clabe
clabe.remove_bank('12345')
```
pyautotrace 0.0.7
Python bindings for AutoTrace.
Requires Python: >=3.10
Keywords: autotrace, bitmap, graphics, tracing, vector

# PyAutoTrace

Python bindings for [AutoTrace](https://github.com/autotrace/autotrace).

## Installation

Install PyAutoTrace using your package manager of choice.

```shell
uv add pyautotrace[standard]
```

```shell
python -m pip install pyautotrace[standard]
```

## Usage

```python
import numpy as np
from autotrace import Bitmap, VectorFormat
from PIL import Image

# Load an image.
image = np.array(Image.open("image.jpeg").convert("RGB"))

# Create a bitmap.
bitmap = Bitmap(image)

# Trace the bitmap.
vector = bitmap.trace()

# Save the vector as an SVG.
vector.save("image.svg")

# Get an SVG as a byte string.
svg = vector.encode(VectorFormat.SVG)
```

## Building

See the [contribution guide](https://pyautotrace.lemonyte.com/contributing/#building-from-source) for instructions to build PyAutoTrace from source.

## TODO

- Tests

## License

This project is licensed under the [LGPLv2.1](LICENSE.txt) license.

See the [documentation](https://pyautotrace.lemonyte.com/#license) for details about the licenses of upstream and included code.
lamp 0.1.0.post1
Laser-plasma Analysis for Multiple Platforms
Requires Python: >=3.9

<img src="./docs/imgs/LAMPLogo.png" width="300" />

# *L*aser-plasma *A*nalysis for *M*ultiple *P*latforms

LAMP is a python-based API to facilitate and streamline the analysis of laser-plasma experiment data.

## Installation

```
pip install lamp
```

Or to upgrade to the latest version;

```
pip install lamp --upgrade
```

For a list of required packages please see the [documentation](https://github.com/brendankettle/LAMP/blob/main/docs/UserGuide.md).

## Using the API
For help on getting started and understanding the full capabilities of LAMP, please see the [user guide](https://github.com/brendankettle/LAMP/blob/main/docs/UserGuide.md).

## Reporting bugs
Please report any problems using the [github issue tracker](https://github.com/brendankettle/LAMP/issues).

## Mailing list
To stay up to date with LAMP developments and get notified of releases, please email Brendan requesting subscription to the mailing list.
cdef-pil-090126-2315 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`