semvec/pypi-packages huggingface dataset:
SPLayout 0.5.16
Silicon Photonics Design Tools for GDSII Files.

SPLayout
========

|GitHub repository| |GitHub license|

SPLayout (**S**\ilicon **P**\hotonics **Layout** Design Tools) is a silicon photonics structures design package. It provides commonly used silicon photonics structures for fast integration and pixelated blocks for inverse design.

The GDSII streaming is based on gdspy(https://github.com/heitzmann/gdspy) and FDTD simulation is executed on Ansys Lumerical.


Dependency
----------

-  Python 3.6+
-  gdspy
-  scipy
-  numpy
-  (Ansys Lumerical for FDTDSimulation and MODESimulation)

Installation
------------

use pip:

::

    pip install splayout

or download from the source and build/install with:

::

    python setup.py install

Documentation
-------------

The documentation can be found
`here <https://splayout.readthedocs.io/en/latest/>`__.


`History (Click Here) <https://github.com/Hideousmon/SPLayout/tree/main/history.md>`__
-------------------------------------------------------------------------------------------


.. |GitHub repository| image:: https://img.shields.io/badge/github-SPLayout-blue
   :target: https://github.com/Hideousmon/SPLayout
.. |GitHub license| image:: https://img.shields.io/badge/lisence-GNU--3.0-green
   :target: https://github.com/Hideousmon/SPLayout/blob/main/LICENSE
kalshi-python 2.1.4
Kalshi Trading API
Requires Python: >=3.9
Keywords: OpenAPI, OpenAPI-Generator, Kalshi Trading API

# Kalshi Trading API
Complete API for the Kalshi trading platform including all handlers for SDK generation

This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:

- API version: 2.0.0
- Package version: 2.1.4
- Generator version: 7.15.0
- Build package: org.openapitools.codegen.languages.PythonClientCodegen
For more information, please visit [https://kalshi.com](https://kalshi.com)

## Requirements.

Python 3.9+

## Installation & Usage
```python
pip install kalshi-python
import kalshi_python
```

## Getting Started

Please follow the [installation procedure](#installation--usage) and then run the following:

```python
from kalshi_python import Configuration, KalshiClient

# Configure the client
config = Configuration(
    host="https://api.elections.kalshi.com/trade-api/v2"
)

# For authenticated requests
# Read private key from file
with open("path/to/private_key.pem", "r") as f:
    private_key = f.read()

config.api_key_id = "your-api-key-id"
config.private_key_pem = private_key

# Initialize the client
client = KalshiClient(config)

# Make API calls
balance = client.get_balance()
print(f"Balance: ${balance.balance / 100:.2f}")
```

## Documentation

Full documentation is available at [docs.kalshi.com](https://docs.kalshi.com)
pysmoother 0.0.7
Fit natural cubic polynomial splines, local regression and weighted smoothing splines to fit generalized additive models
Requires Python: >=3.0

##pysmoother allows you to - 

1) Fit Univariate Natural Cubic Polynomial Splines. Allowing you to smooth a predictor and measure its effect on a target variable. 
Refer - Page 151, Equation 5.9 ,The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition February 2009. Trevor Hastie, Robert Tibshirani, Jerome Friedman

2) Fit Generalized Additive Models for p predictors with one target variable y. 
Refer - Page 298, Algorithm 9.1- The Backfitting Algorithm For Additive Model ,The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition February 2009. Trevor Hastie, Robert Tibshirani, Jerome Friedman

3) Fit the Local Scoring Algorithm for the Additive Logistic Regression Model.
Refer - Page 300, Algorithm 9.2, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition February 2009. Trevor Hastie, Robert Tibshirani, Jerome Friedman

4) Fit Local Regression Models to with p predictors. Allowing one to perform smoothing. 
Refer - Page 200, Local Regression in Rp, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition February 2009. Trevor Hastie, Robert Tibshirani, Jerome Friedman




Code Demo - 

1) Fit Univariate Natural Cubic Polynomial Splines

ss = SMOOTHING_SPLINES(X[:,i],Y,penalty = 10)
ss.make_expanded_basis()
ss.make_integral_matrix()
ss.generate_coefficients(plot=True)


2) Fit Generalized Additive Models for p predictors with one target variable y. 

smoothing_type = {0:'s',1:'1',2:'s'} ## l = Loess, s = Smoothing Splines
gam = GAM(X,Y,penalty=4,smoothing_type=smoothing_type)
print(gam.fit_additive_models(iterations=15))
print(gam.fitted_func)
print(gam.alpha)
print(gam.resid)

##Your Final Fitted Curve will be gam.alpha + gam.fitted_func
plt.plot(Y)
plt.plot(np.sum(gam.fitted_func,axis=1))
plt.show()


3a) Fit a Weighted Smoothing Spline
wss = WEIGHTED_SMOOTHING_SPLINE(X[:,i],Y,penalty=10,weight=np.ones(len(X)))
wss.make_expanded_basis()
wss.make_integral_matrix()
wss.generate_coefficients(plot=True)

3b) Fit the Local Scoring Algorithm for the Additive Logistic Regression Model.
LGAM = ADDITIVE_LOGISTIC_REGRESSION_MODEL(X,Y,penalty=10)
LGAM.model_fit(iterations=10)
anonupload 1.4.4
upload and download to anonfiles server
Requires Python: >=3
Keywords: anonfile, anonfile-api, anonfile-cli, anonymous, upload

# 📁 anonfiles-script

🚀 An python tool to upload file to anonymus sever. Supports multiple files.

 [![Publish package](https://github.com/jakbin/anonupload/actions/workflows/publish.yml/badge.svg)](https://github.com/jakbin/anonupload/actions/workflows/publish.yml)
 [![PyPI version](https://badge.fury.io/py/anonupload.svg)](https://pypi.org/project/anonupload/)
 [![Downloads](https://pepy.tech/badge/anonupload/month)](https://pepy.tech/project/anonupload)
 [![Downloads](https://static.pepy.tech/personalized-badge/anonupload?period=total&units=international_system&left_color=green&right_color=blue&left_text=Total%20Downloads)](https://pepy.tech/project/anonupload)
 ![GitHub Contributors](https://img.shields.io/github/contributors/jakbin/anonupload)
 ![GitHub commit activity](https://img.shields.io/github/commit-activity/m/jakbin/anonupload)
 ![GitHub last commit](https://img.shields.io/github/last-commit/jakbin/anonupload)
 ![Python 3.6](https://img.shields.io/badge/python-3.6-yellow.svg)


## ✨ Features
- 📊 Progress bar for upload/download tracking
- 💾 Upload URLs are automatically saved to a file
- 🏷️ Rename files before uploading to server
- 📦 Support for multiple file uploads
- ⬇️ Direct download functionality


## 🚀 Installation

```sh
pip3 install anonupload
```

## 💻 Usage 
```sh
anon up {path-to-file_1} {path-to-file_2} ...  # 📤 upload files to anonfile server
anon d {url1} {url2} ...                       # 📥 download and upload directly to anonfiles 
```

# 🔧 API Reference

The anonupload client provides a comprehensive API for programmatic file upload and download operations.


### anonupload.upload(filename)

```py
from anonupload import upload

upload(filename)
```

```py
from anonupload import changefile_and_upload
changefile_and_upload([file1, file2])
```

### anonupload.download(url)

```py
from anonupload import download

download(url)
```

### anonupload.downloads([url1, url2])

```py
from anonupload import downloads

downloads([url1, url2])
```
envedseted 0.2.0
Advanced environment variable manager with PATH support
Requires Python: >=3.7

# envedseted

**EnvedSeted** is a cross-platform Python package and CLI tool for **managing environment variables**, including **advanced PATH management**.  
It allows you to **set, get, delete, list, backup, and restore** environment variables for both **user and system scopes**.  

---

## Features

- Set, update, or delete environment variables (`user` or `system` scope).  
- List all environment variables clearly.  
- Full **PATH management**:
  - Add directories to PATH (start or end).  
  - Remove directories from PATH.  
  - List PATH entries.  
  - Backup and restore PATH.  
- Cross-platform: **Windows, Linux, macOS**.  
- Python API and CLI support.  
- Optional backup of environment variables before changes.  

---

## Installation

```bash
# Install via pip from PyPI
pip install envedseted
```
psr-demo 0.10.1
Demo project for python-semantic-release
Requires Python: >=3.11
Keywords: semantic version, semantic release, ci/cd, convetional commits, python-semantic-release

# Python Semantic Release Demo

Project showcasing a possible setup to utilize [python-semamtic-release][psr] (PSR) with [uv][uv] and [ruff][ruff].

To build this has cost me more time than anticipated. Before I started with this demo,
I already had a profound understanding of the semantic version [specification][semver], of [conventional commits][conv],
and some experience with CI/CD ppelines.
From there it took me 30 hours to read through PSR's documentation, understand their uv project setup guide and to create this pipeline.

This pipeline is not quite what I would want to use everywhere, but it will do for simple projects.
Now it is time to create an app which would create this pipeline in a freshly initiated project.
I shall call it [ci-starter][ci-starter]. Hopefully, the pipelines produced by the ci-starter will evolve into something more sophisticated.

[psr]: https://python-semantic-release.readthedocs.io/en/latest/index.html
[uv]: https://docs.astral.sh/uv
[ruff]: https://docs.astral.sh/ruff
[semver]: https://semver.org
[ci-starter]: https://github.com/fleetingbytes/ci-starter
teraflopai-data 0.1.1
A petabyte scale data processing framework for AI models using Ray.
Requires Python: >=3.10

# teraflopai-data

A petabyte scale data processing framework for AI models using Daft + Ray.

## Installation
```python
pip install teraflopai-data
```

## Community


## Examples

### Pipeline
```python
import daft

from teraflopai_data.components.text.embedding import SentenceTransformersEmbed
from teraflopai_data.components.text.fineweb_edu import FinewebEduClassifier
from teraflopai_data.pipeline import Pipeline

df = daft.from_pydict(
    {
        "text": [
            "My mother told me",
            "Someday I will buy",
            "Galleys with good oars",
            "Sail to distant shores",
        ],
    }
)

classifier = FinewebEduClassifier(
    input_column="text",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)

embedder = SentenceTransformersEmbed(
    input_column="text",
    model_name="all-MiniLM-L6-v2",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)

pipeline = Pipeline(
    ops=[classifier, embedder],
)

df = pipeline(df)
df.show()
```

### Text
```python
import daft

from teraflopai_data.components.text.fineweb_edu import FinewebEduClassifier

df = daft.from_pydict(
    {
        "text": [
            "My mother told me",
            "Someday I will buy",
            "Galleys with good oars",
            "Sail to distant shores",
        ],
    }
)

classifier = FinewebEduClassifier(
    input_column="text",
    batch_size=4,
    concurrency=1,
    num_cpus=6,
    num_gpus=1,
)
df = classifier(df)
df.show()
```

### Image
```python
import daft
from daft import col

from teraflopai_data.components.image.image_hashing import ImageHasher

df = daft.from_pydict(
    {
        "urls": [
            "https://live.staticflickr.com/65535/53671838774_03ba68d203_o.jpg",
            "https://live.staticflickr.com/65535/53671700073_2c9441422e_o.jpg",
            "https://live.staticflickr.com/65535/53670606332_1ea5f2ce68_o.jpg",
            "https://live.staticflickr.com/65535/53671838039_b97411a441_o.jpg",
            "https://live.staticflickr.com/65535/53671698613_0230f8af3c_o.jpg",
        ],
    }
)

hasher = ImageHasher(
    input_column="image",
    hashing_algorithm="wavelet",
    concurrency=1,
    num_cpus=6,
)

df = df.with_column("image_bytes", col("urls").url.download(on_error="null"))
df = df.with_column("image", col("image_bytes").image.decode())
df = hasher(df)
df = df.drop_duplicates("image_hash")
df.show()
```