semvec/pypi-packages huggingface dataset:
eegfmri-denoising 0.1.0
Add your description here
Requires Python: >=3.11

# EEG-fMRI Denoising

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

---
## Installation
```
pip install eegfmri_denoising
```
or
```
uv install eegfmri_denoising
```
---
## Example Usage
TBC
topozarr 0.0.4
Lightweight multiscale zarr
Requires Python: >=3.11

# topozarr - lightweight multiscale zarr pyramids

Python library to create multiscale zarr pyramids for usage with [zarr-layer](https://zarr-layer.demo.carbonplan.org/).
Attempts to follow the WIP [zarr-multiscales spec](https://github.com/zarr-conventions/multiscales).

**Warning: experimental**

## Usage

#### Installation

```bash
uv add topozarr
# or
pip install topozarr
```

#### Example
```python
# !pip install netcdf4 pooch 
import xarray as xr
import xproj # for crs assignment
from topozarr.coarsen import create_pyramid

# Load the air_temp tutorial xarray dataset
ds = xr.tutorial.open_dataset('air_temperature', chunks={})
ds = ds.proj.assign_crs(spatial_ref_crs={"EPSG":4326})
print(ds)
```

```python
pyramid = create_pyramid(
    ds, 
    levels=2, 
    x_dim="lon", 
    y_dim="lat")
print(pyramid.encoding)
print(pyramid.dt)
```

```python
# Optional: Write to Zarr
# !pip install obstore zarr
from obstore.store import from_url
from zarr.storage import ObjectStore


store = from_url(url = "<add_your_bucket_url>", region="<add_your_region>")
zstore = ObjectStore(store) 
pyramid.dt.to_zarr(zstore, mode="w", encoding = pyramid.encoding, zarr_format=3)
```

```python
# Optional: Write to Icechunk
# !pip install icechunk 
import icechunk

storage = icechunk.s3_storage(bucket="<add_your_bucket_name>", prefix="<add_your_prefix>", from_env=True)
repo = icechunk.Repository.create(storage)
session = repo.writable_session("main")

store = from_url(url = "<add_your_bucket_url>", region="<add_your_region>")
zstore = ObjectStore(store) 
pyramid.dt.to_zarr(session.store, mode="w", encoding = pyramid.encoding, consolidated=False)
```



## Development

This project uses `uv` for dependency management, `pytest` and `hypothesis` for testing and `ruff` for linting. 


### Sync development environment 

```python
uv sync --all-extras
```


### Run linter

```python
uv run pre-commit run all-files
```

### Run tests
```
uv run pytest tests/
```
mcp-server-decoder 0.1.0
MCP server for Decoder - static call graph analysis for Python
Requires Python: >=3.11

# mcp-server-decoder

MCP server for [Decoder](https://github.com/maryamtb/decoder), a static call graph analysis for Python codebases. Provides a pre-indexed topoloy for LLMs to use while searching codebases. Unlike `grep`, focused on text matches, decoder navigates call graphs and reduces token usage, while improving latency

### With decoder:
![with-decoder-mcp](./with-decoder.png)

### Without decoder:
![wo-decoder-mcp](./without-decoder.png)

## Usage

## MCP Server

```bash
cd your-project
pip install mcp-server-decoder
decoder index .
claude mcp add decoder -- mcp-server-decoder
```

Verify it's connected. In Claude, run:

```bash
/mcp
# then, "MCP Status"
```

You should see `decoder` listed with its available tools.

### Tools Available

| Tool | Parameters | Description |
|------|------------|-------------|
| `decoder_callers` | `name` | Find what calls a function |
| `decoder_callees` | `name` | Find what a function calls |
| `decoder_trace` | `name`, `max_depth?` | Trace full call tree |
| `decoder_find` | `query`, `type?` | Search for symbols |
| `decoder_stats` | - | View index statistics |
skabelon 1.1.1
Create complex document templates using Jinja2
Requires Python: >=3.13

# skabelon

Skabelon ([sɡ̊ɑb̥əˈloːˀn]), from the Danish word for "template", is a python package to create complex document
templates. It wraps the [Jinja2](https://jinja.palletsprojects.com/en/3.1.x/) library and enriches its default filters
with sensible options for converting Python types to Markdown and LaTeX (e.g. for lists and tables).

## Usage

### Filters

Filters (i.e. Jinja2 functions) can be called like any filter within a Jinja2 template, and are designed to be used
directly with corresponding Python types.

#### Example: *_list

For example, both `latex_list` and `markdown_list` accept a list of strings
(or any other type that can be represented as a string) and transform them into appropriate templated strings during
rendering.

Assume that our Python list looks like this: `foo = ["foo", "bar", "baz"]`. In a Markdown Jinja2 template, applying the
filter could like this:

```
# My rendered list

This example renders a markdown list:

{{ foo | markdown_list }}
```

Which would result in the following rendered output:

```markdown
# My rendered list

This example renders a markdown list:

- foo
- bar
- baz
```

For LaTeX templates, the template and outlook would look like this:

```
\section{My rendered list}

This example renders a \LaTeX list:

{{ foo | latex_list }}
```

Which accordingly would result in the following rendered output:

```latex
\section{My rendered list}

This example renders a \LaTeX list:

\begin{itemize}
\item foo
\item bar
\item baz
\end{itemize}
```

Both rendered outputs could then be processed further with their respective toolchains, e.g. a Markdown renderer, or a
LaTeX toolchain matching the overall templated contents (e.g., LuaLaTeX).

## Development

This project is written in python3. It uses `pipenv` for dependency management, `pytest` for testing, and `black` for
formatting.
matminer 0.10.0
matminer is a library that contains tools for data mining in Materials Science
Requires Python: >=3.11
Keywords: data mining, materials science, scientific tools

# <img alt="matminer" src="docs_rst/_static/matminer_logo_small.png" width="300">

[![testing](https://github.com/hackingmaterials/matminer/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/hackingmaterials/matminer/actions?query=workflow%3Atesting+branch%3Amain)
![python](https://img.shields.io/badge/Python-3.11+-blue.svg?logo=python&amp;logoColor=white)

matminer is a library for performing data mining in the field of materials science.

- **[Website (including documentation)](https://hackingmaterials.github.io/matminer/)**
- **[Examples Repository](https://github.com/hackingmaterials/matminer_examples)**
- **[Help/Support Forum](https://matsci.org/c/matminer/16)**
- **[Source Repository](https://github.com/hackingmaterials/matminer)**

matminer supports Python 3.11+.

#### Related packages:

- If you like matminer, you might also try [automatminer](https://github.com/hackingmaterials/automatminer).
- If you are interested in furthering development of datasets in matminer, you may be interested in [matbench](https://github.com/hackingmaterials/matbench).
- If you are looking for figrecipes, it is now in its [own repo](https://github.com/hackingmaterials/figrecipes).


#### Citation

If you find matminer useful, please encourage its development by citing the following paper in your research:
```
Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N. E. R., Bajaj, S., Wang, Q.,
Montoya, J. H., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson,
K., Snyder, G. J., Foster, I., Jain, A., Matminer: An open source toolkit for
materials data mining. Comput. Mater. Sci. 152, 60-69 (2018).
```

Matminer helps users apply methods and data sets developed by the community. Please also cite the original sources, as this will add clarity to your article and credit the original authors:

- If you use one or more **datasets** accessed through matminer, check the dataset metadata info for relevant citations on the original datasets.
- If you use one or more **data retrieval methods**, check ``citations()`` method of the data retrieval class. This method will provide a list of BibTeX-formatted citations for that featurizer, making it easy to keep track of and cite the original publications.
- If you use one or more **featurizers**, please take advantage of the ```citations()``` function present for every featurizer in matminer.