semvec/pypi-packages huggingface dataset:
auto-play-in-mpv 0.1.2
Automatically redirect to mpv player when playing online video
Requires Python: >=3.8
Keywords: mpv, video

# Auto play in MPV

Automatically redirect to mpv player when playing online video.

## Installation / Usage

1. install auto-play-in-mpv: `pipx install auto-play-in-mpv`, start as `apim`.
2. install tampermonkey in your browser
3. [install the script from greasyfork](https://greasyfork.org/zh-CN/scripts/505562-auto-play-in-mpv)
4. just click a video from supported sites.

## mpv config

Note that this is just [my config example](https://github.com/lxl66566/nixos-config/tree/main/config/mpv/profile.conf). You may need to change some settings.

## Autostart server

It's better to start the websocket server at startup.

- On NixOS: Add this to your `configuration.nix`
  ```nix
  systemd.user.services.apim = {
    enable = true;
    description = "Open online video in local mpv player";
    script = "~/.local/bin/apim";
    wantedBy = [ "default.target" ];
    path = with pkgs; [
      mpv
      yt-dlp
      kdePackages.kwallet
    ];
  };
  ```

## Supported sites

- [x] bilibili (video, live)
- [x] youtube

## TODO

- [ ] auto start
- [ ] fix behavior on youtube

## License

MIT
SMOPCA 0.1.1
A novel spatially aware multi-omics dimension reduction method
Requires Python: >=3.10.0

# Spatial Multi-Omics PCA

Spatial Multi-Omics PCA (SMOPCA) is a novel dimension reduction method to integrate multi-modal data and extract low-dimensional representations with preserved spatial dependencies among spots.

![](https://github.com/cmhimself/SMOPCA/blob/main/img/fig1.jpg?raw=true)

## Installation

SMOPCA can be installed directly from PyPI using the following command:
```
pip install SMOPCA
```

## Run SMOPCA

1. Prepare input data
   - SMOPCA accepts gene expression and protein/atac data matrices. Each modality of the data is preprocessed and normalized  separately. Initially, genes and proteins with zero counts were filtered out. Subsequently, the count matrix was normalized based on library size, followed by log-transformation and scaling to achieve unit variance and zero mean. ATAC reads are mapped to gene regions and the peak matrix is collapsed into a gene activity matrix, adhering to the established protocol from the Satija lab. The gene activity matrix was preprocessed and normalized using the same method as applied to mRNA data. Finally, we recommend to save the data into a hdf5 file.
   - Note that SMOPCA takes input matrices with columns corresponding to cells or spots.
2. Specify model hyperparameters and Model training
   - the dimensionality of the latent factors (default 20)
   - the kernel type (default matern kernel)
   - the length_scale parameter (We set length_scale=0.25 for UMAP coordinates and length_scale=5 for simulated and real spatial coordinates.)
   - For the rest of the parameters, see more in tutorials.
3. Downstream analysis
   - Visualization
   - Clustering analysis
   - Differential expression analysis
   - and many other tasks

## Datasets

Sample datasets are provided in ./data folder. The rest of the datasets used in this study are available at https://drive.google.com/drive/folders/11RfeF_yrdSGRtXZnzPlfxojM5V4ezMVr?usp=drive_link
mirax-profileinfo-extractor 0.2.4
A simple library that extracts profile information from a MIRAX file.
Requires Python: >=3.7

## Mirax profile info extractor

This simple lib helps extract extra information from Mirax files that are not provided by other open source libraries like
[Openslide](https://github.com/openslide/openslide).

## How to install?

`pip install mirax_profileinfo_extractor` or with conda `conda install -c ekami mirax_profileinfo_extractor`.

## How to build?

Run `make` and find the built package in the `dist` folder.

## How to use?

Add the lib to your project from pypi and call the function `get_mirax_profile_info` passing the path to the file as parameter.

```python
from mirax_profileinfo_extractor.extractor import get_mirax_profile_info

mirax_info = get_mirax_profile_info('path/to/file.mrxs')
print(mirax_info)
```

## License

MIT License

Copyright (c) [2023] [Pathologywatch]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.