semvec/pypi-packages huggingface dataset:
geobipy 2.3.2
McMC inversion of airborne electromagnetic data
Requires Python: >=3.10
Keywords: inversion, bayesian

############################################################
Welcome to GeoBIPy: Geophysical Bayesian Inference in Python
############################################################

This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
derive posterior distributions of subsurface and measured data properties.
The current implementation is applied to time and frequency domain electromagnetic data.
Application outside of these data types is in development.

Citation
~~~~~~~~

Foks, N. L., and Minsley, B. J. 2020. GeoBIPy - Geophysical Bayesian Inference in Python. 10.5066/P9K3YH9O

Background scientific references
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Minsley, B. J., Foks, N. L., and Bedrosian, P. A. 2020. Quantifying model structural uncertainty using airborne electromagnetic data. Geophys. J. Int. 224, 1, 590–607. https://doi.org/10.1093/gji/ggaa393

Minsley, B. J. 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophys. J. Int. 187, 252–272. 10.1111/j.1365-246X.2011.05165.x

`Documentation is here! <https://doi-usgs.github.io/geobipy/>`_

This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval by the U.S. Geological Survey (USGS). No warranty, expressed or implied, is made by the USGS or the U.S. Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.
markdup 0.0.25
Fast, accurate BAM deduplication with intelligent UMI detection and correct fragment detection
Requires Python: >=3.10
Keywords: bioinformatics, bam, deduplication, umi, sequencing, genomics

# MarkDup

[![Pypi Releases](https://img.shields.io/pypi/v/markdup.svg)](https://pypi.python.org/pypi/markdup)
[![Downloads](https://img.shields.io/pepy/dt/markdup)](https://pepy.tech/project/markdup)
[![Development Status](https://img.shields.io/badge/status-alpha-orange.svg)](https://github.com/y9c/markdup)

**A fast, accurate BAM deduplication tool with intelligent UMI detection and correct fragment detection.**

## Why MarkDup?

MarkDup solves critical issues in BAM deduplication:

- **Correct fragment detection:** Proper strand-aware coordinate handling.
- **High performance:** Optimized algorithms and parallel processing.
- **Smart UMI clustering:** Frequency-aware algorithms to prevent over-merging.
- **Automatic detection:** Handles both UMI and non-UMI data seamlessly.

## Quick Start

```bash
# Installation
pip install markdup

# Basic usage (auto-detects everything)
markdup -i input.bam -o output.bam

# With multiple threads
markdup -i input.bam -o output.bam --threads 8

# Force coordinate-based (no UMIs)
markdup -i input.bam -o output.bam --no-umi
```

## Usage

For a full list of options and their descriptions, run:

```bash
markdup --help
```

### Examples

#### Basic Deduplication

```bash
# Auto-detect UMIs and process
markdup -i input.bam -o output.bam

# Force coordinate-based method (no UMIs)
markdup -i input.bam -o output.bam --no-umi
```

#### Advanced Options

```bash
# Custom UMI settings
markdup -i input.bam -o output.bam --umi-tag UB --max-dist-frac 0.15

# Start-only positioning (useful for ChIP-seq)
markdup -i input.bam -o output.bam --start-only

# Keep duplicates and mark them
markdup -i input.bam -o output.bam --keep-duplicates
```

## Output Format

MarkDup adds BAM tags to track deduplication:

| Tag  | Description                             |
| ---- | --------------------------------------- |
| `cn` | Cluster name (chr:start-end:strand:UMI) |
| `cs` | Cluster size (number of reads)          |

## Documentation

- [Installation Guide](docs/installation.md)
- [Usage Guide](docs.usage.md)
- [Algorithm Details](docs/algorithm.md)

## License

MIT License - see [LICENSE](LICENSE) for details.

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ismycomputeronfire 0.4
A command-line tool to check if your computer is on fire.

<div align="center">
    <h1>Is My Computer On Fire?</h1>                                                                                                                            
    <p><strong>A command-line tool to check if your computer is on fire.</strong></p>
</div>

## Installation
```
pip install ismycomputeronfire
```
## Usage

### Command-Line Tool
```
ismycomputeronfire --chrome_tabs=30
```

If your computer is on fire, it will print a nice ASCII art fire. Otherwise, it will print "No.".

### Library
You can also use this package in your own Python code:
```python
from ismycomputeronfire import check

stats = {
    "npm_modules": 10,
    "git_conflicts": 21
}

check(**stats)
```
## Rules
The tool uses a set of rules to determine if your computer is on fire. The following arguments are available:
 * `--chrome_tabs`                                                                                   
 * `--npm_packages`                                                                                  
 * `--slack_messages`                                                                                
 * `--docker_containers`                                                                             
 * `--days_to_release`                                                                               
 * `--jira_tickets_assigned`                                                                         
 * `--git_conflicts`                                                                                 
 * `--servers_running`
lynxkite-mcp 0.2.0
A command-line tool and MCP server for running LynxKite workspaces.
Requires Python: >=3.11

# LynxKite MCP

An MCP server for running a LynxKite workspace. The boxes with no inputs, such as
"Import CSV" are considered the inputs of the workspace, and the boxes with no outputs,
such as "View table" are considered its outputs. The model using the MCP server
will be able to modify the settings in the input boxes and access the results in the
output boxes.

In the MCP client, configure a command such as:

```
lynxkite-mcp 'examples/NetworkX demo.lynxkite.json'
```

This will take care of running the workspace. No need for a running LynxKite instance.