semvec/pypi-packages huggingface dataset:
glue-genes 1.2.3
Multidimensional data visualization for genomics
Requires Python: >=3.8

![glue_genes_logo_stacked](https://user-images.githubusercontent.com/3639698/137145077-2c2c9011-68bd-4770-9d58-494bf7632a33.png)


The glue-genes meta-package
===========================

[glue genes](https://github.com/gluesolutions/glue-genes) is a custom version of the [glue software](https://glueviz.org) for the 
visual exploration of genomics data, developed in partnership with [The Jackson Laboratory](https://jax.org).

### Features

glue genes provides all the core features of glue:

* Interactive, linked statistical graphics
* A user interface for 'glueing' together multiple datasets with complicated relationships
* A highly scriptable and extendable environment

In addition, glue genes provides:

* Data loaders for genomics data file formats, including multiresolution images and transcriptomics data.
* Viewers including: 2D Heatmap, Small Multiples, QTL Viewer
* Menubar plug-ins to facilitate analysis of single-cell and spatial transcriptomics data

### Install

See [the glue genes documentation](https://glue-genes.readthedocs.io/en/latest/how-to/installation.html) for installation help.

We recommend installing into a new virtual environment. To install the current stable version use:

`pip install glue-genes[qt6]`

or 

`pip install glue-genes[qt5]`

depending on which version of Qt you would like to use.

To install the latest development version we recommend the following procedure in a new virtual environment:

`pip install "glue-genes[qt6]@git+https://git@github.com/gluesolutions/glue-genes.git"`

### Usage

After installation, glue (with the glue genes plug-ins loaded) can be invoked at the terminal:
`glue`


### Development

glue genes is under active development. Please file all bug reports as github issues.
TFInterpy 1.1.3
TFInterpy is a Python package for spatial interpolation. A high-performance version of several interpolation algorithms is implemented based on TensorFlow. Including parallelizable IDW and Kriging algorithms. So far, tfinterpy is the **fastest open source Kriging** algorithm, which can reduce the operation time of large-scale interpolation tasks by an order of magnitude.

## TFInterpy
TFInterpy is a Python package for spatial interpolation. A high-performance version of several interpolation algorithms is implemented based on TensorFlow. Including parallelizable IDW and Kriging algorithms. So far, tfinterpy is the **fastest open source Kriging** algorithm, which can reduce the operation time of large-scale interpolation tasks by an order of magnitude

## Link to our paper
[TFInterpy: A high-performance spatial interpolation Python package](https://www.sciencedirect.com/science/article/pii/S2352711022001479)
<br>
(https://doi.org/10.1016/j.softx.2022.101229)

### Performance comparison (unit: second)

| Grid size | GeostatsPy-OK | PyKrige-OK | TFInterpy-OK | TFInterpy-TFOK(GPU) | TFInterpy-TFOK(CPU) |
| :-----: | :-----: | :-----: | :-----: | :-----: | :-----: |
| 1x10<sup>4<sup/> | 23.977 | 1.258 | 0.828 | 2.070 | 0.979 |
| 1x10<sup>5<sup/> | 230.299 | 12.264 | 8.140 | 6.239 | 2.067 |
| 1x10<sup>6<sup/> | 2011.351 | 121.711 | 82.397 | 45.737 | 11.683 |
| 1x10<sup>7<sup/> | 2784.843 | 1250.980 | 849.974 | 452.567 | 112.331 |

### Screenshots
Snapshot of GUI tool.
![Snapshot of GUI tool](./figs/OK3D.jpg)

#### Requirements

**Minimum usage requirements:** Python 3+, Numpy, SciPy

**TensorFlow based algorithm:** TensorFlow 2

**GSLIB file support:** Pandas

**3D visualization:** VTK  

**GUI Tool:** PyQT5

-----

# Usage

#### All examples are stored on the [github homepage](https://github.com/czwchenzhun/tfinterpy)

#### Install tfinterpy
```
pip install tfinterpy
```

#### Then install dependencies

**Full dependencies** : (To avoid package version issues, the specific version numbers tested in Python3.9 are listed here)
```
pip install matplotlib==3.9.4
pip install numpy==2.0.2
pip install pandas==2.2.3
pip install PyQt5==5.15.11
pip install scipy==1.13.1
pip install tensorflow==2.18.0
pip install vtk==9.4.1
```

**Notice！** You may do not need to install all dependencies
- If you only need to use the most basic interpolation algorithm, install the following package. (see "examples/" for usage)
    ```
    pip install numpy==2.0.2
    pip install scipy==1.13.1
    ```
- If you need to use TensorFlow-based interpolation algorithms, you need to install tensorflow. (see "examples/tf" for usage)
    ```
    pip install tensorflow==2.18.0
    ```
    or (Use GPU for computing)
    ```
    pip install tensorflow-gpu==2.18.0
    ```
- If you need to use the built-in GUI tools (see "examples/gui" for usage) provided, please install full dependencies as above list.

netcdf4 also needs to be installed to **run the examples** in the examples folder:
```
pip install netCDF4==1.7.2
```
actinia-openapi-python-client 0.0.5
Actinia OpenAPI Python Client
Keywords: OpenAPI, Actinia, OpenPlains, GRASS GIS

     # actinia-openapi-python-client

    ## Actinia - The GRASS GIS REST API

    **Actinia** is an open source REST API for scalable, distributed, high
    performance processing of geographical data that uses GRASS GIS for
    computational tasks.

    It provides a REST API to process satellite images, time series of satellite
    images, arbitrary raster data with geographical relations and vector data.

    The REST interface allows to access, manage and manipulate the GRASS GIS
    database via HTTP GET,PUT,POST and DELETE requests and to process raster,
    vector and time series data located in a persistent GRASS GIS database.
    **Actinia** allows the processing of cloud based data, for example all Landsat
    4-8 scenes as well as all Sentinel2A scenes in an ephemeral databases. The
    computational results of ephemeral processing are available via object storage
    as GeoTIFF files.
chinese-holidays 0.1.10
Query information about Chinese holidays
Requires Python: >=3.10

# Chinese-Holidays MCP server

Query information about Chinese holidays

### Tools

1. `get-all-holidays`: Get all holidays for a year
2. `is-holiday`: Check if a date is a holiday

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

* Development/Unpublished Servers Configuration

  ```json
  {
    "mcpServers": {
      "chinese-holidays": {
        "command": "uv",
        "args": [
          "--directory",
          "the absolute path of chinese-holidays",
          "run",
          "chinese-holidays"
        ]
      }
    }
  }
  ```

* Published Servers Configuration

  ```json
  {
    "mcpServers": {
      "chinese-holidays": {
        "command": "uvx",
        "args": [
          "chinese-holidays"
        ],
        "env": {
          "PYTHONIOENCODING": "utf-8"
        }
      }
    }
  }
  ```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
pyfcf 0.1.1
Package to create standardized, publication quality matplotlib figures
Requires Python: >=3.7

README
======
`pyfcf` is a package to create standardized, publication quality matplotlib figures. It is partcularly useful for creating figures where the axis are a specfic size, allowing standardization between figures in e.g., papers, charts, or posters.

# Installation

Requirements:

- maptlotlib

To install:

```
  pip install pyfcf
```

# Examples

Initialize basic `matplotlib` settings:

```
pyfcf.setup_matplotlib()
```

See `examples/` folder.

# Development

## Tests

```
python -m unittest discover -s tests
```

## Build

You need additional package `build` `wheel` and `twine`. Make sure that everything is up to date and then

```
  pip install --upgrade build wheel twine
  python3 -m build
  python3 -m twine upload --repository testpypi dist/*
```