semvec/pypi-packages huggingface dataset:
mlsherpa 0.1.1
A lightweight AutoML framework
License: MIT
Requires Python: >=3.7

# AutoML

**AutoML** is a lightweight AutoML library to automatically choose the best algorithm for your dataset.

## Features

- Supports classification and regression
- Auto model selection
- Performance graphs

## Usage

```python
from automl import AutoML
model = AutoML(df, target='target_column')
model.run()
```

## Data Cleaning

```python
from automl.preprocessing import DataCleaner

cleaner = DataCleaner()
df_cleaned = cleaner.clean(df)
```

## Install

```bash
pip install -r requirements.txt
```
interaction-logger 0.3.3
A package for logging user interactions with a django distributed system.
License: MIT
Requires Python: >=3.6.3
Keywords: django, logging, user activity, audit trail, monitoring

# Interaction Logger

A Django middleware for logging user interactions with your application.

## Features

- Logs user activity including request and response details
- Asynchronous logging to avoid impacting request-response cycle
- Works in both ASGI and WSGI environments
- Supports custom fields for tracking additional information

## Installation

```bash
pip install interaction-logger
```

## Configuration

Add the middleware to your Django settings:

```python
MIDDLEWARE = [
    # ... other middleware
    'interaction_logger.middleware.UserActivityMiddleware',
    # ... other middleware
]
```

## Usage

### Basic Usage

Once configured, the middleware will automatically log user activity for all requests except those to `/admin/`, `/static/`, and `/media/`.

### Custom Fields

You can add custom fields to track additional information. This is useful for tracking business-specific data or for debugging purposes.

```python
from interaction_logger.middleware import UserActivityMiddleware

# Track the user's browser information
def get_browser_info(request):
    user_agent = request.META.get('HTTP_USER_AGENT', '')
    if 'Chrome' in user_agent:
        return 'Chrome'
    elif 'Firefox' in user_agent:
        return 'Firefox'
    else:
        return 'Other'

UserActivityMiddleware.add_custom_field('browser', get_browser_info)

# Track a query parameter
UserActivityMiddleware.add_custom_field('source', lambda request: request.GET.get('source'))
```

The `add_custom_field` method takes two arguments:
1. `field_name`: The name of the custom field to track
2. `field_value_getter`: A function that takes a request object and returns the value to track

The function should handle any exceptions internally to avoid breaking the middleware.

See the [examples directory](examples/) for more examples of custom fields.

## Viewing Logs

User activity logs are stored in the database and can be viewed in the Django admin interface.

## License

MIT
prometheus-pushgateway-pytest 0.1.0
Pytest plugin to export test metrics to Prometheus Pushgateway
License: MIT
Requires Python: >=3.9

# prometheus_pushgateway_pytest

A lightweight **pytest plugin** to export test execution metrics to **Prometheus Pushgateway**.

## 🚀 Features

- Automatically tracks:
  - Test pass/fail status
  - Test duration
  - Test execution counts
  - Histogram and summary of durations
- Pushes metrics to Prometheus Pushgateway after each test


## 🧪 Installation

```bash
pip install prometheus_pushgateway_pytest
```

- Variables
    - `PUSH_GW_URL`: To set push gateway URL. Default is: `localhost:9091`
    -  `JOB_NAME` : To set job name. Default is:  `pytest_execution_result_job`
automl-kit 0.1.3
A lightweight AutoML framework
License: MIT
Requires Python: >=3.7

# AutoML

**AutoML** is a lightweight AutoML library to automatically choose the best algorithm for your dataset.

## Features

- Supports classification and regression
- Auto model selection
- Performance graphs

## Usage

```python
from automl import AutoML
model = AutoML(df, target='target_column')
model.run()
```

## Data Cleaning

```python
from automl.preprocessing import DataCleaner

cleaner = DataCleaner()
df_cleaned = cleaner.clean(df)
```

## Install

```bash
pip install -r requirements.txt
```
eacy 1.0.1
This is a simple python package to parse, collect, and collate parameters relevant to the Habitable Worlds Observatory Exploratory Analytic Cases into an object-oriented python framework.
License: MIT
Requires Python: >=3.10

# Take it EACy!

This is a simple python package to parse, collect, and collate parameters relevant to the Habitable Worlds Observatory Exploratory Analytic Cases into an object-oriented python framework. 

## Installation
`pip install eacy`

## Requirements:
HWO Sci-Eng-Interface should be downloaded/installed: https://github.com/HWO-GOMAP-Working-Groups/Sci-Eng-Interface
An environment variable should be set:
`export SCI_ENG_DIR=/Users/mhcurrie/science/packages/Sci-Eng-Interface/hwo_sci_eng`
llama-index-llms-featherlessai 0.1.0
llama-index llms featherlessai integration
License: MIT
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: Featherless AI

## Installation

To install the required package, run:

```shell
pip install llama-index-llms-featherlessai
```

## Setup

1. Set your Featherless AI API key as an environment variable. Visit https://www.featherless.ai/ and sign up to get an API key.

```shell
import os

os.environ["FEATHERLESS_API_KEY"] = "you_api_key"
```

## Basic Usage

### Generate Completions

```python
from llama_index.llms.featherlessai import FeatherlessLLM

llm = FeatherlessLLM(
    model="meta-llama/Llama-3.3-70B-Instruct", api_key="your api key"
)

resp = llm.complete("Is 9.9 or 9.11 bigger?")
print(resp)
```

### Chat Responses

To send a chat message and receive a response, create a list of `ChatMessage` instances and use the `chat` method:

```python
from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.chat(messages)
print(resp)
```
image-lowpass-filters 0.1.2
Ideal, Butterworth and Gaussian frequency‐domain filters for PyTorch
License: MIT
Requires Python: >=3.7

# Low-pass filters


Assume there is an image in spatial domain $f(u, v)\in\mathbb{R}^{m\times n}$, 
and its representation in shifted frequency domain $F(u, v)$, 
therefore the low-pass filtering is $H(u,v)*F(u, v)$, where

## Ideal

$$H(u, v)=\begin{cases}
    1, D(u, v) < D_0 \\
    0, D(u, v) > D_0
\end{cases}$$

where $D(u,v)$ is the distance to the matrix center for each pixel, and $D_0$ is the cutoff frequency.

## Butterworth

$$H(u, v)=\frac{1}{1+[D(u, v)/D_0]^{2n}}$$

## Gaussian

$$H(u, v)=e^{-D^2(u, v)/2{D_0}^2}$$

# Usage

Install from pypi:

```
pip install image-lowpass-filters
```

examples:

```python
import torch
from image_lowpass_filters import ideal_bandpass, butterworth, gaussian

cutoff = 20

img_tensor = torch.randn((1, 3, 224, 224))

img_lowpass = ideal_bandpass(img_tensor, cutoff)

img_lowpass = butterworth(img_tensor, cutoff, 10)

img_lowpass = gaussian(img_tensor, cutoff)
```