semvec/pypi-packages huggingface dataset:
pywhatsup 0.0.1
Python WhatsUp Gold API wrapper
Requires Python: >=3.6

# pywhatsup
![PyPI - Downloads](https://img.shields.io/pypi/dm/pywhatsup) ![GitHub](https://img.shields.io/github/license/peterbaumert/pywhatsup) ![PyPI](https://img.shields.io/pypi/v/pywhatsup)

Still work in progress :)

## usage
```
from pywhatsup import api

whatsup = api("https://whats.up.gold:9644", "user", "password")

dev = whatsup.devices(id=422)
interfaces = dev.interfaces.all()
int = dev.interfaces.get("1502")
poll = dev.config.get("polling")
```

# Changelog
<!--
    Placeholder for the next version (at the beginning of the line):
    ## **WORK IN PROGRESS**
-->

## 0.0.1 (2022-06-01)
* first working version
flows_get_brightest 2.1.1
Helper script for getting the brightest star in a Hawk-I (or other)
Requires Python: >=3.10

[![Python package](https://github.com/SNflows/flows-tools/actions/workflows/python-package.yml/badge.svg)](https://github.com/SNflows/flows-tools/actions/workflows/python-package.yml)

# flows-tools   
Observer tools for the FLOWS pipeline. Contains python packages, scripts, and notebooks for the following tasks:
 - Pointing, OB generation helper with VLT HAWK-I, finder chart maker.
 - Image cutouts
 - Template subtraction
 - Fixing fits image headers of multi-extension fits files
 - Hubble Diagrams
 - Lightcurve fitting of Type Ia SN lightcurves

---
## Available Packages:
 - **flows_get_brightest**: The VLT HAWK-I pointing script that will create a finding chart for HAWK-I 
H-band images given a flows target, and optional rotation (position angle) and telescope offsets.
Uses `tendrils` to query objects. Optionally plots a finding chart. 
Easily extensible to use with other instruments.

### install with:
``pip install flows_get_brightest``   
**NOTE**: Installation requires `python >= 3.10` due to `tendrils` dependency.  
### Usage:
Installs a user script `get_brightest` that can be run from the command line.
``get_brightest --help``.

### test with: 
``pip install flows_get_brightest[test]``    
``pytest``

Note: user script only tested to work on Linux and Mac OS.

---
## Available scripts:
 - fixlcogt.py A script for fixing LCOGT headers not preserved by other scripts such as imagematch.
 - cutouts.py A flexible script for making image cutouts while preserving original headers and extension and applying 
the same cutout to all image extensions present in the fits file.

## Available notebooks:
 - Subtraction.ipynb Example notebook for doing manual subtractions following @sholmbo's method.
 - image_cutout.ipynb Example notebook that was the inspiration for cutouts.py.
 - hubble diagram notebook.
 - lightcurve fitting notebook.
spaceway 2.3.0
Space Way is arcade game about space, in which you must overcome the space path by flying around obstacles
Requires Python: >=3.6.0

<div align="center">

## Space Way

Arcade game about space, in which you must overcome the space path by flying around obstacles 

![](https://img.shields.io/pypi/v/spaceway) ![](https://img.shields.io/github/release-date/YariKartoshe4ka/Space-Way) ![](https://img.shields.io/pypi/dm/spaceway) <br>
![](https://codecov.io/gh/YariKartoshe4ka/Space-Way/branch/develop/graph/badge.svg?token=PQGAWK18ZN) ![](https://img.shields.io/github/issues-raw/YariKartoshe4ka/Space-Way/help%20wanted)

<img src="https://raw.githubusercontent.com/YariKartoshe4ka/Space-Way/master/docs/icons/icon.svg" width=128 />

</div>


### Installation

###### Compiled (Windows 10+)

1. Download Space Way binary from [latest releases](https://github.com/YariKartoshe4ka/Space-Way/releases/latest) (ends on **.exe**)
2. Launch program and play!

###### Compiled (Android 5.0+)

1. Download Space Way package file from [latest releases](https://github.com/YariKartoshe4ka/Space-Way/releases/latest) (ends on **.apk**)
2. Launch it and install Space Way
3. Run game with shortcut and play!

###### Via pip (All platforms)

1. Install Space Way from PyPI packages
```sh
python -m pip install spaceway
```
2. Run it with shortcut or from terminal
```sh
spaceway
```

### Future

I am not a professional game developer (this is my first game), and I do not know if it makes sense to develop it, but if there is activity on the repository, then I'm going to do:

1. Music and sprites update 
2. Add more obstacles

[and many other things...](https://github.com/YariKartoshe4ka/Space-Way/blob/master/docs/TODO.md)


### Thanks to

- [Arthur Karimullin](https://github.com/KarimullinArthur) for his original and wonderful 8-bit sounds


### Contribution

If you want to contribute to this repo, check out [TODO.md](https://github.com/YariKartoshe4ka/Space-Way/blob/master/docs/TODO.md) and check out what you can do<br>
I am currently looking for artists to evaluate and rework sprites<br>
You can also look at the issue marked as *"help wanted"* and help to solve them<br>
I welcome information about bugs, ideas and suggestions, always open for issue and pull requests<br>


### Screenshots

|                               |                               |
|-------------------------------|-------------------------------|
|![](https://raw.githubusercontent.com/YariKartoshe4ka/Space-Way/master/docs/screenshots/lobby.png)|![](https://raw.githubusercontent.com/YariKartoshe4ka/Space-Way/master/docs/screenshots/jump.png) |
|![](https://raw.githubusercontent.com/YariKartoshe4ka/Space-Way/master/docs/screenshots/boost.png)|![](https://raw.githubusercontent.com/YariKartoshe4ka/Space-Way/master/docs/screenshots/end.png)  |
scmappy 0.2
Cell annotation function
Requires Python: >=3.5

# SCMAPpy

Python version of scmap as described from the original paper of [Kilesev et al. (2017)](https://www.nature.com/articles/nmeth.4644)
It integrates with scanpy objects. 

On top of the original algorithm, we implement a few additional functions.

## Usage

The purpose of this package is to map the annotations of a reference single-cell dataset (`reference`) into a target dataset (`target`). The package is implemented to be used with [AnnData objects](https://scanpy.readthedocs.io/en/latest/usage-principles.html#anndata) and [Scanpy environment](https://scanpy.readthedocs.io/en/latest/generated/scanpy.queries.enrich.html#scanpy-queries-enrich).

We implement three functions to do this pipeline of annotation.

 - `common_genes`: Function to find the common genes between both datasets.
 - `scmap_annotate`: Function to anotate the target dataset that implements the original scmap function.
 - `scmap_projection`: Function to project the target dataset into a representation of the reference dataset.
## Example

Consider a `reference` and a `target` datasets. The genes are annotated in `.var["Gene_names"]`. The annotations are in `reference.obs["Annotation"]`

The first step for the annotation is to find the common genes between both datasets.

```
reference,target = common_genes(reference,target,"Gene_names",remove_unmached=True)
```

Before proceeding to the mapping, we need to select that are going to be used for the mapping, as using the hole genome will have the curse of dimensionality. There are different flavors for selecting the genes that do not require this step, but the most typical is to use the highly varying genes. For that you have to first run the `scanpy.pp.highly_varying_genes` algorithm in the reference dataset.

```
scanpy.pp.highly_varying_genes(reference)
```

With the common genes detected and the genes on interest selected, we can proceed to map the data.

```
scmap_annotate(reference,target,"Gene_names","Annotation",algorithm_flavor="centroid",gene_selection_flavor="HVGs",similarity_threshold=.7, key_added="scmap_annotation")
```

That is it. By default there will be a new column added in `target.obs` with the annotations.

In the case that we would also to visualize where the annotated cells where projected in a representation of the data, we can use the projection function. Consider that we have a UMAP representation in `reference.obsm["X_umap"]`. We would do,

```
scmap_projection(reference,target,"Gene_names","X_umap",algorithm_flavor="centroid",gene_selection_flavor="HVGs",key_added="scmap_annotation")
```

and this will add a `target.obsm["scmap_annotation"]` with the projected cells.