semvec/pypi-packages huggingface dataset:
markdown-print 0.0.1
Markdown Syntax Renderer
Requires Python: <3.13.0,>=3.12.0

# markdown-print

A thin wrapper around [Rich's](https://github.com/Textualize/rich) markdown
renderer with opinionated defaults for terminal display. Rich does all the
actual parsing and rendering, this package just configures it with:

- Nord-darker theme for code blocks with matching inline code styling
- Left-aligned headings with visible `#` prefix markers
- H1 rendered in a panel, other headings styled normally
- Paged output via `less -R` by default (preserves ANSI colors)
- Horizontally centered content within a 100-character width
- A `render_markdown()` function for programmatic use

I found myself continually copying this around or making one-off scripts
with these defaults, so I packaged it up in a repo so I can install it
as needed.

There's intentionally no config files nor are there a lot of options, it's
meant to represent how I prefer markdown rendered in the terminal.

<p align="center">
  <img src="./docs/img/mdp-render.png", alt="Markdown rendered in terminal with mdp" width="1100">
</p>

## Installation

```sh
uv tool install markdown-print
```

## Usage

```sh
mdp path/to/file.md
```

## Development

This project requires Python 3.12 and uses
[uv](https://github.com/astral-sh/uv) to manage dependencies.

You can create a virtual environment with all the necessary dependencies
by running:

```sh
uv sync --all-extras --dev
```

This will install all necessary dependencies and install this project
in editable mode.

You can activate the venv with:

```sh
. .venv/bin/activate
```
daselement-api 1.1.2
Das Element Python API
Requires Python: >=2.7
Keywords: das-element, api, asset-management

# Das Element Python API

Official Python API for the **Das Element** asset library software.

---

## 🧩 Installation

Install the package using `pip`:

```bash
pip install daselement-api
```

---

## ⚙️ Configuration

The API internally calls the command-line interface (CLI) version of _Das Element_.  
To ensure it works correctly, link the CLI executables by setting the following environment variables:

### Environment Variables

| Variable              | Description                             |
| --------------------- | --------------------------------------- |
| `DASELEMENT_CLI`      | Path to Das Element CLI executable      |
| `DASELEMENT_CLI_FULL` | Path to Das Element CLI full executable |

```bash
export DASELEMENT_CLI=/path/to/das-element
export DASELEMENT_CLI_FULL=/path/to/das-element-full
```

### Alternative Configuration

Directly define the executable paths in your Python script.
Overwrite the values of `daselement_api/manager.py` like this:

```python
from daselement_api import manager as de_manager

de_manager.EXECUTABLE_CLI = '/path/to/das-element-cli_2.2.2_lin'
de_manager.EXECUTABLE_CLI_FULL = '/path/to/das-element-cli-full_2.2.2_lin'
```

---

## 🧠 Example Usage

```python
from daselement_api import api as de

libraries = de.get_libraries()

for library, config in libraries.items():
    print(library)
    print(config)
```

---

## 📚 Documentation

For the full API reference and examples, visit:  
👉 [**docu.api.das-element.com**](http://docu.api.das-element.com)

---

## 🧾 License

This project is distributed under the terms of the license provided with **Das Element**.
metaloci 1.3.3
METALoci: spatially auto-correlated signals in 3D genomes

# METALoci

#### Spatially auto-correlated signals in 3D genomes.

METALoci relies on spatial autocorrelation analysis, classically employed in geostatistics, to describe how the variation of a variable depends on space at a global and local scales (e.g., identifying contamination hotspots within a city). METALoci repurposes this type of analysis to quantify spatial genome hubs of similar epigenetic properties. Briefly, the overall flowchart of METALoci consists of four steps:

* First, a genome-wide Hi-C normalized matrix is taken as input and the top interactions selected.

* Second, the selected interactions are used to build a graph layout (equivalent to a physical map) using the Kamada-Kawai algorithm with nodes representing bins in the Hi-C matrix and the 2D distance between the nodes being inversely proportional to their normalized Hi-C interaction frequency.

* Third, epigenetic/genomic signals, measured as coverage per genomic bin (e.g., ChIP-Seq signal for H3K27ac), are next mapped into the nodes of the graph layout.

* The fourth and final step involves the use of a measure of autocorrelation (specifically, the Local Moran’s I or LMI) to identify nodes and their neighborhoods with an enrichment of similar epigenetic/genomic signals.

METALoci is compatible with .cool, .mcool and .hic Hi-C formats; and with .bed signal files. The signal used in METALoci
may be any numerical signal (as long as it is in a .bed file, with the location of such signal).

#### Have a look at the [documentation](https://metaloci.readthedocs.io)!

## Install metaloci from PyPI:

```bash
conda create -n metaloci -c bioconda python==3.12 bedtools
conda activate metaloci
pip install metaloci
```

Do you have trouble compiling dependencies? Perhaps you need an upgraded version of libcurl.

In Ubuntu, you can do this with:

```bash
sudo apt install -y libcurl4-openssl-dev
```

If you are experiencing any unexpected results with METALoci (e.g. your signal after binning is 0 for every bin), we 
suggest to update the version of **awk** you are using. The recommended version is 5.1.0 or newer.

In Ubuntu, you can install it with:

```bash
sudo apt install -y gawk
```

## Contributors

METALoci is currently being developed at the [MarciusLab](http://www.marciuslab.org) by Leo Zuber, Iago Maceda and
Marc A. Marti-Renom, with the original contribution of Juan Antonio Rodríguez as well as other members of the lab.