semvec/pypi-packages huggingface dataset:
alshain 0.1.2
Interface library for Alshain Oy devices
Requires Python: >=3.11

# Alshain
Python library for Alshain Oy devices

## Quick start

```python

# Pica photometer readout
import alshain
import serial
import sys

# Serial port as first command line parameter
com = serial.Serial( sys.argv[1], alshain.BAUDRATE, timeout = 0.25 )

dev = alshain.Pica( com, address = 1 )

# Enable photometer source
dev.write( alshain.Pica.Parameters.PULSE_ENABLE, 1 )

# Read results
print( dev.read( alshain.Pica.Parameters.RESULT ) )

```

```python

# Strix SMU
import alshain
import serial
import sys

# Serial port as first command line parameter
com = serial.Serial( sys.argv[1], alshain.BAUDRATE, timeout = 5.0 )

smu = alshain.Strix( com, address = 1 )

voltage, current = smu.measure()

```

```python

# Sylphium eco laser driver
import alshain
import serial
import sys

# Serial port as first command line parameter
com = serial.Serial( sys.argv[1], alshain.BAUDRATE, timeout = 0.25 )

laser = alshain.Sylphium( com, address = 1 )

# Set driver operating range to 3A
laser.write( alshain.Sylphium.Parameters.IRANGE, alshain.Sylphium.Options.IRANGE_3A )

# Set laser diode drive voltage to 12 V
laser.write( alshain.Sylphium.Parameters.V_SET, 12.0 )

# Set diode current limit to 250mA
laser.write( alshain.Sylphium.Parameters.I_LIMIT, 250e-3 )

# Enable laser output
laser.enable_output( True )

# Use either external or internal modulation to drive the diode

# Disable laser
laser.enable_output( False )
```
bossanova 0.1.0.dev11
Bridging statistical cultures with some jazz
Requires Python: >=3.11
Keywords: analysis, multi-level-modeling, regression, statistics

# Bossanova

Unified mixed-effects modeling in Python. JAX + NumPy backends. R/lme4 parity.

## Installation

```bash
uv add bossanova
```

or

```bash
pip install bossanova
```

## Features

- **One unified entry point** — `model()` handles linear, generalized, and mixed-effects models
- **R-style formulas** — `y ~ x + (1|group)`, `factor()`, `center()`, `scale()`
- **Multiple inference strategies** — asymptotic, bootstrap, permutation, cross-validation
- **Marginal effects** — `.explore()` for estimated marginal means, slopes, and contrasts
- **JAX acceleration** — automatic acceleration when JAX is available, NumPy fallback
- **R parity** — matches lme4/emmeans within f64 tolerance

## Quick Example

```python
from bossanova import model, load_dataset

df = load_dataset("sleepstudy")

# Fit a mixed model
m = model("Reaction ~ Days + (1 + Days | Subject)", data=df).fit()
m.params

# Perform bootstrapped parameter inference
m.infer(how='boot')

# Explore marginal effects
m.explore("Days")
m.effects
```
Flask-BabelPlus 2.4.1
Adds i18n/l10n support to Flask applications
Requires Python: >=3.12
Keywords: babel, extension, flask

Flask-BabelPlus
===============

[![Build Status](https://travis-ci.org/sh4nks/flask-babelplus.svg?branch=master)](https://travis-ci.org/sh4nks/flask-babelplus)
[![Coverage Status](https://coveralls.io/repos/github/sh4nks/flask-babelplus/badge.svg?branch=master)](https://coveralls.io/github/sh4nks/flask-babelplus?branch=master)
[![PyPI Version](https://img.shields.io/pypi/v/Flask-BabelPlus.svg)](https://pypi.python.org/pypi/Flask-BabelPlus)
[![Documentation Status](https://readthedocs.org/projects/flask-babelplus/badge/?version=latest)](https://flask-babelplus.readthedocs.io/en/latest/?badge=latest)

Adds i18n/l10n support to Flask applications with the help of the
[Babel](https://github.com/python-babel/babel) library.

This is a fork of Flask-BabelEx which in turn is a fork of the official
Flask-Babel extension. It is API compatible with both forks.

It comes with following additional features:

1. It is possible to use multiple language catalogs in one Flask application;
2. Localization domains: your extension can package localization file(s) and
   use them if necessary;
3. Does not reload localizations for each request.

The main difference to Flask-BabelEx is, that you can pass the
localization ``Domain`` in the extensions initialization process.

```python
# Flask-BabelPlus
babel.init_app(app=app, default_domain=FlaskBBDomain(app))
```


Links
=====

* [Documentation](https://flask-babelplus.readthedocs.io)
* [Flask-BabelEx](https://github.com/mrjoes/flask-babelex)
* [Original Flask-Babel Extension](https://github.com/python-babel/Flask-Babel)
tj-data-normalizer 0.1.5
CLI tool to normalize tabular data and generate schemas
Requires Python: >=3.9

# Data Normalizer

Data Normalizer is a Python CLI tool for cleaning and normalizing tabular data from CSV or Excel files.

It profiles columns, infers data types, normalizes values (including mixed date formats), and generates:
- Cleaned CSV output
- JSON records
- SQL `CREATE TABLE` schemas
- JSON Schema definitions
- A lightweight profiling report

The tool is designed to be deterministic, scriptable, and easy to integrate into data pipelines.

## Usage
Run the CLI on a CSV or Excel file:

```bash
data-normalizer path/to/file.csv [OPTIONS]

output/<input_name>OUT_<YYYY-MM-DD>/

If `--out` is provided, outputs are written to:

output/<out>/<input_name>OUT_<YYYY-MM-DD>/

Each run folder contains:
- Normalized CSV data
- JSON records
- SQL schema
- JSON schema
- Profiling report

## Why this project exists

Real-world tabular data is often messy, inconsistent, and hard to load directly into databases.
This tool provides a repeatable way to normalize datasets and generate reliable schemas before ingestion.

### CLI Options

input_path  
Path to the input `.csv` or `.xlsx/.xls` file.

--sheet  
Excel sheet name or index (only for Excel files).  
Examples: Sheet1, 0

--preview-rows  
Number of rows to preview in the terminal (0–50).  
Default: 5

--table  
SQL table name used in the generated CREATE TABLE statement.  
Default: normalized_data

--show-schema / --no-show-schema  
Print generated SQL and JSON schemas to the terminal.  
Default: disabled

--export  
Write output files to disk.  
If not provided, no files are written.

--out  
Subfolder inside `./output` used to group runs.  
Example: results  
Outputs will be written to:  
output/results/<input_name>OUT_<YYYY-MM-DD>/

--overwrite / --no-overwrite  
Control whether existing run folders can be reused.  
Default: overwrite enabled

### Overwrite behavior

By default, outputs overwrite files in the run folder if it already exists.  
Use --no-overwrite to prevent replacing existing results.  
If the output folder already exists and overwrite is disabled, the command exits with an error.
architect-py 5.9.8
Python SDK for the Architect trading platform and brokerage.
Requires Python: <4,>=3.10

# [![Architect](https://avatars.githubusercontent.com/u/116864654?s=29&v=2)](https://architect.co) architect_py 
[![PyPI version](https://img.shields.io/pypi/v/architect-py.svg)](https://pypi.org/project/architect-py/)

A fully-featured Python SDK for trading on [Architect](https://architect.co).

Just some of the features of this SDK: symbology, portfolio management, order entry, advanced algos, and marketdata subscriptions.

Also, it is compatible with Jupyter notebooks! Check the [examples for an example notebook](examples/jupyter_example.ipynb).

## Installation

- pip: `pip install architect-py`
- poetry: `poetry add architect-py`
- uv: `uv add architect-py`

## API keys for the brokerage

API keys/secrets for the brokerage can be generated on the [user account page](https://app.architect.co/user/account).

## Method catalog 

Go to [FUNCTIONS.md](FUNCTIONS.md) file to see a catalog of methods.

## Examples

Go to the [Examples](./examples) to see examples of a variety of common use cases.  To run a specific example, use e.g. `python -m examples.orderflow_streaming`.

## Documentation 

See the [Getting started with Python](https://docs.architect.co/getting-started-with-python) guide for more information.

## Imports

In general, most types in the package can be imported from the top-level; in rare cases, some types may come from GraphQL and need to be imported from `architect_py.graphql_client.fragments`.

```python
from architect_py import *  # includes both AsyncClient and Client
from architect_py.graphql_client.fragments import (
    ExecutionInfoFields,
    ProductInfoFields,
)
```

Using an LLM or an IDE with code completion like VSCode or PyCharm can be very helpful.
synapse 2.233.1
Synapse Intelligence Analysis Framework
Requires Python: >=3.11

Synapse
=======

Synapse is a versatile central intelligence system created to support analyst teams in every stage of the intelligence life cycle.

|codecov|_ |circleci|_ |rtd|_

Learn More About Synapse
------------------------

* Visit our website_ to learn more about Synapse. 
* Watch the `Synapse Guided Tour`_!


Installation & Documentation
----------------------------

* Synapse is available as a Python Package on pypi_ and as a Docker image on dockerhub_.

  * The Getting Started guide_ will help get you up and running!

* You can also use synapse_quickstart_ to get up and running quickly on Windows, Mac, or Linux and start using Synapse!
* User Guides and API Documentation can be found here_.

Connect With Us
---------------

* Join the Synapse user community on Slack_
* Follow us on Twitter_.
* Follow us on LinkedIn_.

.. |circleci| image:: https://circleci.com/gh/vertexproject/synapse/tree/master.svg?style=svg
..  _circleci: https://v.vtx.lk/synapse-circleci

.. |codecov| image:: https://codecov.io/gh/vertexproject/synapse/branch/master/graph/badge.svg?branch=master
.. _codecov: https://v.vtx.lk/synapse-codecov

.. |rtd| image:: https://readthedocs.com/projects/vertex-synapse/badge/?version=latest
.. _rtd: https://v.vtx.lk/synapse-docs

.. _website: https://v.vtx.lk/vertex

.. _Synapse Guided Tour: https://v.vtx.lk/synapse-tour

.. _pypi: https://v.vtx.lk/synapse-pypi

.. _dockerhub: https://v.vtx.lk/synapse-dockerhub

.. _guide: https://synapse.docs.vertex.link/en/latest/synapse/quickstart.html

.. _synapse_quickstart: https://github.com/vertexproject/synapse-quickstart

.. _here: https://v.vtx.lk/synapse-docs

.. _slack: https://v.vtx.lk/join-slack

.. _Twitter: https://v.vtx.lk/twitter

.. _LinkedIn: https://www.linkedin.com/company/vertexproject
jupyterhealth-client 0.2.0
JupyterHealth client library
Requires Python: >=3.10

# JupyterHealth client

A Python client library for the JupyterHealth exchange.

Install:

```
pip install jupyterhealth-client
```

Use:

```python
from jupyterhealth_client import JupyterHealthClient

client = JupyterHealthClient()
```

For more information on the client API, see [the docs](https://jupyterhealth-client.readthedocs.io/).
bettercode 0.2.0
Code for BetterCodeBetterScience book
Requires Python: >=3.12

## Better Code, Better Science

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18603015.svg)](https://doi.org/10.5281/zenodo.18603015)
![Tests](.github/badges/tests.svg)

[Better Code, Better Science](https://bettercodebetterscience.github.io/book/) is an open-source book on building better code for science using AI.  

Author: [Russ Poldrack](http://poldrack.github.io)

This repo contains the Python module and notebooks used for examples in the book.

The code in this repo is licensed according to the MIT License.
strictdoc 0.17.1
StrictDoc is open-source software for technical documentation and requirements management.
Requires Python: >=3.10

# StrictDoc

The documentation is hosted on Read the Docs:
[StrictDoc documentation](https://strictdoc.readthedocs.io/en/stable/).

For a quick overview of the project, check out the
[StrictDoc project](https://github.com/strictdoc-project/strictdoc/blob/main/about/StrictDoc.pdf)
slide deck.