semvec/pypi-packages huggingface dataset:
alpacloud 0.1.1
Metapackage for all my alpacloud tools
Requires Python: >=3.11
Keywords: metapackage

# Alpacloud

Alpacloud is my little cloud. This repo contains the tools that I use for building it.

## Tools

- alpacloud-ansible-builder : a tool to automatically build and install Ansible Collections

## Collections

- lilatomic.api : api helpers, like a better way of making HTTP Requests
- lilatomic.azcli : invoke the azure-cli from ansible
vima-spatial 0.1.6
variational inference-based microniche analysis
Requires Python: >=3.12.3

# vima
Variational inference-based microniche analysis is a method for conducting case-control analysis on multi-sample spatial molecular datasets. `vima` can be applied to any spatially resolved molecular technology, is well powered even at the modest sample sizes typical of research cohorts, and avoids traditional, parameter-intensive preprocessing steps such as cell segmentation or clustering of cells into discrete cell types. It works by treating each spatial sample as an image and using a variational autoencoder to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of "microniches" – small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then uses rigorous statistics to identify microniches whose abundance correlates with case-control status.

## installation
To use `vima`, you can either install it directly from the [Python Package Index](https://pypi.org/) by running, e.g.,

`pip install vima-spatial`

or if you'd like to manipulate the source code you can clone this repository and add it to your `PYTHONPATH`.

Note that the package requires a working installation of `pytorch`, and it may be beneficial to first install `pytorch`, verify it works properly, and then install `vima`.

For data preprocessing the current version of the package also requires a working `R` environment with the [`harmony` package](https://github.com/immunogenomics/harmony) and [`arrow` package](https://arrow.apache.org/docs/r/) installed. You can create such an environment easily with [`conda`](https://docs.conda.io/en/latest/) by running

`conda create -n Renv -c conda-forge r-base r-harmony r-arrow`

To use `vima` you'll need the path to the `Rscript` executable in this `R` environment, which you can get by running `which Rscript` with the `R` environment active.

## demo
Take a look at our [demo](https://github.com/yakirr/vima/blob/main/demo/demo_IHC.ipynb) to see how to get started with an example analysis. We plan to put up demos for other data modalities in the future.

## citation
If you use `vima`, please cite:

[Y. Reshef, et al. Powerful and accurate case-control analysis of spatial molecular data. bioRxiv. https://doi.org/10.1101/2025.02.07.637149v1](https://www.biorxiv.org/content/10.1101/2025.02.07.637149v2).
ketacli 1.4.4
KetaDB Client
Requires Python: >=3.8

# ketadb 工具

## 示例

```shell
ketacli login --name <username> --endpoint http://localhost:9000 --token <yourtoken>

ketacli logout

# 枚举所有仪表盘
ketacli list dashboard --fields id,app,updateTime --sort updateTime --order asc --prefix test 

# 描述资源的字段
ketacli describe dashboard 

# 插入一条数据

ketacli insert --repo test01 '[{"a":1}]'

# 查询一条数据

ketacli search 'repo=test01 | limit 10' 

# 指定返回格式，支持json、csv、html、latex等

ketacli search 'repo=test01 | limit 10' --format json

#  创建一个资源

ketacli create repos test_repo --file file.json

# 删除一个资源
ketacli delete repos -n test_repo

# 查看可操作的资源列表
ketacli rs

# 查看具体资源操作方法
ketacli rs --type repos

# watch 资源变化
ketacli list repo --watch

ketacli search '''search2 repo="*"|stats count() as cnt by repo,sourcetype |sort by cnt |limit 10''' -w --interval 1

# 下载资源
ketacli download <assetType> [-e ectra] [--base_path path]

# 示例：下载 App
ketacli download apps --base_path apps -e app_name=keta_docs

# 下载任意资源（普通 json 接口将会保存 json 文件）
ketacli download repo -e name=_internal

# 安装 app
ketacli update apps -o install -e name=aws,version=1.0.1

# 卸载 app
ketacli update apps -o uninstall -e name=aws

# 重装/升级 app
ketacli update apps -o upgrade -e name=aws,version=1.0.1

# 终端画图
ketacli plot 'search2 start="-60m" repo="*" | timechart span="1m" count() as cnt by repo ' --y_field cnt --x_field _time --group_field repo --type line

# 内置仪表盘
ketacli dashboard -c infra-host  # 目前内置了infra-host audit k8s-monitor monitor四个仪表盘


```

# 自定义仪表盘
可参考示例仪表盘的 yaml 文件配置进行配置: [charts](ketacli/charts/)


## 自动补全

在你的 .bashrc / .zshrc 中加入以下语句

```shell
#zshrc 中不存在的话也需要添加
autoload -U +X bashcompinit && bashcompinit

eval "$(register-python-argcomplete ketacli)"
```