semvec/pypi-packages huggingface dataset:
smiles-fp 0.1.2025.3.5
Multithreaded Tanimoto Similarity for RDKit Fingerprints.
Requires Python: <3.14,>=3.10

# smiles-fp

## Installation

```bash
# on Linux only
sudo apt install libfreetype-dev

git clone https://github.com/audivir/smiles-fp
cd smiles-fp

# set the correct RDKit version
python build_smiles_fp.py <smiles_fp_version> <rdkit_version>

pip install ".[dev]" # uv pip install ".[dev]"
pytest tests
```
esf-convert 1.1.0
Convert ECHO Single Form (ESF) XML files to Markdown or Word documents
Requires Python: >=3.10
Keywords: converter, docx, echo, esf, humanitarian, markdown, xml

# esf-convert

Convert ECHO Single Form (ESF) XML files to Markdown or Word documents.

## Web Interface

Use the converter directly in your browser - no installation required:

**[Launch Web App](https://brianmcdonald.github.io/esf-convert/)**

The web app runs entirely client-side using WebAssembly. Your files are never uploaded to any server.

## Installation

```bash
uv tool install esf-convert
```

## Usage

```bash
# Convert to Word document (default)
esf-convert form.xml

# Convert to Markdown
esf-convert form.xml -f md

# Specify output file (format auto-detected from extension)
esf-convert form.xml -o report.md

# Explicit format with custom output name
esf-convert form.xml -f md -o my-report
```

### Options

| Option | Description |
|--------|-------------|
| `-o, --output` | Output file path (default: input filename with .docx extension) |
| `-f, --format` | Output format: `docx` (Word, default) or `md` (Markdown). Auto-detected from output extension if not specified. |

## Features

- **Structured output** - Chapters, sections, and subsections with proper heading hierarchy
- **Field tips** - Form guidelines/tips displayed in italics below each field heading
- **Enum labels** - Numeric values (0, 1, 2) converted to human-readable labels (No, Yes, Partially, etc.)
- **Outcome indicators** - Section 7.2 indicators with definition, baseline, target, and progress values
- **Results** - Section 7.3 results with indicators and activities
- **Metadata** - Reference numbers, dates, document type (YAML frontmatter in Markdown, table in DOCX)

## Development

Requires Python 3.10+.
scavager 0.2.17
Proteomics post-search algorithm
Requires Python: >=3.9

Scavager - a proteomics post-search validation tool
---------------------------------------------------

The pepXML or MzIdentML files are required for basic operation of the script. Currently supported search engines:
IdentiPy, X!Tandem, Comet, MSFragger, MSGF+, Morpheus.

FASTA file is required for calculation of NSAF (label-free quantitation index), protein sequence coverage and amino acid statistics.

For MSGF+ and Morpheus search engines it is desirable to provide cleavage rules used in search
(these search engines do not report number of missed cleavages for peptides).

The output of Scavager contains:

- tab-separated table with unfiltered peptide-spectrum matches (ends with _PSMs_full.tsv)
- tab-separated table with identified peptide-spectrum matches at 1% PSM FDR (ends with _PSMs.tsv)
- tab-separated table with identified peptides at 1% peptide FDR (ends with _peptides.tsv)
- tab-separated table with identified proteins without grouping at 1% protein FDR (ends with _proteins.tsv)
- tab-separated table with identified protein groups at 1% protein FDR (ends with _protein_groups.tsv)
- PNG figure with PSM, peptide and protein features distributions

Citing Scavager
---------------
Ivanov et al. Scavager: A Versatile Postsearch Validation Algorithm for Shotgun Proteomics Based on Gradient Boosting. doi: 10.1002/pmic.201800280

Installation
------------
Using pip:

    pip install Scavager


Usage
-----
Algorithm can be run with following command (works with Python2.7/Python3+):

    scavager path_to_pepXML/MZID

OR

    scavager -h

Protein grouping using DirectMS1 results
----------------------------------------
Protein groups can be generated using parsimony principle combined with information from MS1 spectra:

    scavager path_to_pepXML/MZID -ms1 path_to_DirectMS1_proteins_full_noexclusion.tsv

Details on combination of parsimony principle and MS1 information are available at: https://github.com/markmipt/protein_inference_using_DirectMS1

Protein grouping for indistinguishable proteins
------------------------------------------------
By default, when multiple proteins have the same sets of peptides, the Scavager choose protein group leader using alphabetical order. However, it is possible
to choose group leader randomly by using "-sr" option. The same option can be used with MS1 spectra information if multiple proteins have both same sets of MS/MS identifications and DirectMS1 scores.


Links
-----

- GitHub repo & issue tracker: https://github.com/markmipt/scavager
- Mailing list: pyteomics@googlegroups.com, markmipt@gmail.com
craftlet 0.2.0
Add your description here
Requires Python: >=3.12

# CraftLet

**CraftLet** is a Python-based CLI (Command-Line Interface) tool designed to streamline project template management and initialization. It enables developers to quickly scaffold new projects from GitHub-hosted templates with minimal configuration overhead.

## Core Purpose

CraftLet automates the process of:
1. **Template Loading from GitHub** - Fetches project templates directly from GitHub repositories
2. **Dynamic Project Configuration** - Prompts users for configuration values based on a template configuration schema
3. **Environment Variable Generation** - Automatically creates `.env` files with configured environment variables
4. **Template Instantiation** - Extracts and customizes template files into a target directory

## Key Features

- **GitHub Integration** - Converts GitHub repository URLs to downloadable zip archives using the Codeload API
- **Interactive Configuration** - Uses Typer to create an interactive CLI that prompts users for required inputs
- **Template Configuration Schema** - Supports JSON-based `templateConfig.json` files that define:
  - User input prompts
  - Environment variable mappings
  - Nested configuration structures
- **Asynchronous Operations** - Leverages async/await for efficient HTTP requests using httpx
- **Project Initialization** - Extracts template files while preserving directory structure and optionally generates environment configuration

## Technology Stack

- **Framework** - Typer (modern CLI framework)
- **HTTP Client** - httpx (async HTTP client)
- **Python Version** - 3.12+
- **Dependencies** - typer, httpx

## Architecture

- **CLI Module** (`src/craftlet/cli/CraftLetCLI.py`) - Command registration and user-facing CLI commands
- **Feature Module** (`src/craftlet/feature/CraftLet.py`) - Core template loading and disk writing logic
- **Utils Module** (`src/craftlet/utils/`) - Helper functions for configuration building and URL mapping

## Target Use Case

CraftLet is ideal for project maintainers who want to distribute boilerplate code/templates, allowing end-users to clone and customize templates without manual file editing or complex setup steps.

## Installation

```bash
pip install -e .
```

## Usage

Load a template from GitHub:

```bash
craftlet load-template --github
```

When prompted, provide:
- The GitHub repository URL for the template
- The desired project name

Optionally generate an `.env` file:

```bash
craftlet load-template --github --generate-env
```