semvec/pypi-packages huggingface dataset:
bfg-logutil 0.2.2
A python logging library that writes to console, local file and datadog
Requires Python: >=3.10

# logutil

A simple logging library that can replace builtin logging and other structural logging libaries. logutil writes logs to:
- Console
- Local file
- Datadog

## Basic Usage

```python
from logutil import log

log.info("hello", a=1, b="foo")
```

By default, logs appear in:
- Your console
- Append to `/tmp/app.log`

## Configuration

You can customize logutil by calling `init_logger`:

```python
from logutil import init_logger

init_logger(
    # Turn console logging on/off
    console=True,  
    
    # Change file logging settings
    file={
        "enabled": True,
        "path": "/tmp/app.log"
    },
    
    # Enable Datadog logging
    datadog={
        "api_key": "your_api_key",
        "site": "US3",
        "service": "your_service_name",
        "tags": {"env": "dev"}
    }
)
```

## Datadog Configuration

To use Datadog logging, you must provide:
1. Your Datadog API key
2. The Datadog site you use

Valid Datadog sites:
- US1
- US3
- US5
- EU1
- US1-FED

You can find your site in the URL when you log in to the Datadog web portal.
comprehend-telemetry 0.1.1
Integration of comprehend.dev with OpenTelemetry in Python
Requires Python: >=3.8

# comprehend-telemetry

OpenTelemetry integration for [comprehend.dev](https://comprehend.dev) - automatically capture and analyze your Python application's architecture and performance.

## Installation

```bash
pip install comprehend-telemetry
```

## Quick Start

Add the ComprehendDevSpanProcessor to your existing OpenTelemetry setup:

```python
import os
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.resources import Resource
from comprehend_telemetry import ComprehendDevSpanProcessor

# Set up OpenTelemetry with your service information
resource = Resource.create({
    "service.name": "my-python-service",
    "service.namespace": "production",
    "deployment.environment": "prod"
})

tracer_provider = TracerProvider(resource=resource)

# Add comprehend.dev telemetry processor
if os.getenv("COMPREHEND_OBSERVATIONS_TOKEN"):
    comprehend_processor = ComprehendDevSpanProcessor(
        organization='comprehend',  # Use your organization name
        token=os.getenv("COMPREHEND_OBSERVATIONS_TOKEN"),
        debug=True  # Optional: enable debug logging
    )
    tracer_provider.add_span_processor(comprehend_processor)

# Set as global tracer provider
trace.set_tracer_provider(tracer_provider)
```

## Configuration

Set your comprehend.dev ingestion token as an environment variable:

```bash
export COMPREHEND_OBSERVATIONS_TOKEN=your-ingestion-token-here
```

## What it captures

This integration automatically captures:

- **HTTP Routes** - API endpoints and their usage patterns
- **Database Operations** - SQL queries with table operations analysis
- **Service Dependencies** - HTTP client calls to external services
- **Performance Metrics** - Request durations, response codes, error rates
- **Service Architecture** - Automatically maps your service relationships

## Requirements

- Python 3.8+
- OpenTelemetry SDK
- An existing OpenTelemetry instrumentation setup

## Framework Support

Works with any Python framework that supports OpenTelemetry auto-instrumentation:

- FastAPI
- Django
- Flask
- SQLAlchemy
- Requests
- HTTPx
- And more...

## Learn More

- Visit [comprehend.dev](https://comprehend.dev) for documentation and to get your ingestion token
- [OpenTelemetry Python Documentation](https://opentelemetry-python.readthedocs.io/)

## Development

See [DEVELOPMENT.md](DEVELOPMENT.md) for development setup and release instructions.
chinta 1.0.2
Tex Repository Word Counter
Requires Python: >=3
Keywords: latex, tex, thesis

# Chinta

"[Chinta](https://en.wikipedia.org/wiki/Chinta_(mentation))" in Hindu philosophy refers to mentation i.e. mental activity, especially thinking.

This tool tracks the word count of a TeX project in a git repository with respect to time

# Instructions
1. Install `chinta` via pip
```bash
python3 -m pip install chinta
```

1. Run `chinta` and tell it where to find the repo you want to analyze, as well as the name of the default branch.
The name of the default branch is probably either `"master"` or `"main"`
```bash
chinta "path/to/repo" "main"
```

1. Look at the cool graph!
xlranker 0.2.1
PPI-XL Ranking Tool
Requires Python: >=3.10

<div align="center">
<img src="docs/images/logo.svg" alt="Logo" width="80" height="80">
<h1 style="margin-top: -1pt; margin-bottom: 0pt">XLRanker</h1>
<!-- Badges -->
<a target="_blank" style="margin-top: 0pt" href="https://colab.research.google.com/github/bzhanglab/xlranker/blob/master/notebooks/xlranker_example.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
</div><br>

XLRanker is a tool to rank and prioritize protein to protein cross linking data. The tool uses parsimonious selection as well as an XGBoost model to select the best protein pairs to represent ambiguous peptides.

## Installation

You can install `xlranker` using pip:

```bash
pip install xlranker
```

## Usage

To analyze a TSV file containing a network of peptide sequences run:

```
xlranker start peptide_network.tsv
```

Please view the [documentation](https://bzhanglab.github.io/xlranker/latest/) for detailed usage instructions and examples.

## Example Notebook

For a quick start, you can check out the example notebook in the `notebooks` directory or [launch a google colab notebook](https://colab.research.google.com/github/bzhanglab/xlranker/blob/master/notebooks/xlranker_example.ipynb) to see how to use the package interactively.