semvec/pypi-packages huggingface dataset:
redshifting 1.3
Redshift a radio source to a higher redshift
Keywords: python,redshifting,radio astronomy

# Redshfiting

Use this package to shift radio sources (AGN, Clusters, ...) from a given redshift to a higher redshift. \
This can all be done in image plane, by giving a fits image as input.

Example to move the Perseus cluster from redshift 0.018 to 1.318:

```
from redshifting import move_source
myfits = 'Perseus_HBA_full.fits'
move_source(input_fits=myfits, orig_z=0.018, dz=1.3, output_fits='shifted_'+myfits)
```

This package is also on pypi:
https://pypi.org/project/redshifting/0.1.0/
virt-linked-clone 1.1.2
Create a shallow clone of a qcow2-backed LibVirt virtual machine
Requires Python: >=3.8
Keywords: virtualization,libvirt,clone,qemu,kvm,qcow2

# Create a Shallow Clone of a QCOW2-Backed LibVirt Virtual Machine

```shell
pip install virt-linked-clone
```

```shell
usage: virt-linked-clone [-h] [--zsh-completion] [--version] [-c CONNECTION] source target

positional arguments:
  source                Virtual machine from which to create a clone where all writable
                        qcow2-backed drives are linked using copy-on-write. It must be defined
                        with libvirt and accessible via virsh commands.
  target                Name of the new virtual machine to define. Most of the settings of the
                        source image will be copied into the new libvirt domain. Defaults to
                        adding "-clone" to the source domain name.

options:
  -h, --help            show this help message and exit
  --zsh-completion      Print out the zsh autocompletion code for this utility and exit.
  --version             show program's version number and exit
  -c CONNECTION, --connection CONNECTION
                        LibVirt URI to use for connecting to the domain controller. Will honor the
                        value of the VIRSH_DEFAULT_CONNECT_URI environment variable. (default:
                        qemu:///session)
```
count-split 0.0.99
Count splitting for random sampled count matrices

# README #

### What is this repository for? ###

This is a python implementation of Anna Neufeld's paper's new approach to fixing the "double dipping" problem in doing DEG analysis on a test split between clusters that were defined using a training split. Make sure to cite them too even if you're using my python package!

Check out their paper here: https://arxiv.org/abs/2207.00554
And their R implementation here: https://anna-neufeld.github.io/countsplit/

### How do I get set up? ###

`python3 -m pip install count_split`

You can also install using the setup.py script in the distribution like so:
`python3 setup.py install`


### How do I run use this package? ###

This package assumes that the imput matrix is organized with samples in columns, and variables in rows.
For single-cell experiments, this is cells in columns and genes in rows. Make sure that this is the case, or transpose the matrix when calling the pertinent function
To keep memory use low, we do it peice-meal, breaking the columns into bins.
If you have memory issues, try decreaseing bin_size to something lower (default: bin_size=5000)

** If you've got a dense or sparse matrix:
* Note that if you're using scanpy/anndata, the hdf5 file will often have an "X" object, that is typically a sparse matrix. 

```
import numpy as np
from scipy.sparse import csc_matrix
from count_split.count_split import multi_split

in_mat = np.random.negative_binomial(.1, .1, size=(1000,5000))

mat1, mat2 = multi_split(in_mat, 
                percent_vect=[0.5, 0.5],
                bin_size = 5000)

## It also works for sparse matrices:
mat1, mat2 = multi_split(csc_matrix(in_mat), 
                percent_vect=[0.5, 0.5],
                bin_size = 5000)

```

** If you've got an hdf5 file with a dense matrix stored under a specified key (default key is "infile"), you can split that too
```
from count_split.count_split import split_mat_counts_h5
split_mat_counts_h5(in_mat_file, out_mat_file_1, out_mat_file_2, percent_1=0.5, bin_size=5000, key="infile")
```

### License ###
This package is available via the AGPLv3 license.

### Who do I talk to? ###

* Repo owner/admin: scottyler89+bitbucket@gmail.com
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# PirxcyPinger
Use This To Upload Files To PirxcyPinger

[![Downloads](https://pepy.tech/badge/PirxcyPinger)](https://pepy.tech/project/PirxcyPinger)
[![Downloads](https://pepy.tech/badge/pirxcypinger/week)](https://pepy.tech/project/pirxcypinger)
[![Downloads](https://pepy.tech/badge/pirxcypinger/month)](https://pepy.tech/project/pirxcypinger)
[![Requires: Python 3.x](https://img.shields.io/pypi/pyversions/PirxcyPinger.svg)](https://pypi.org/project/PirxcyPinger/)
[![Version: 1.0.0](https://img.shields.io/pypi/v/PirxcyPinger.svg)](https://pypi.org/project/PirxcyPinger/)

### Setup:
``pip3 install PirxcyPinger``

[Examples](https://github.com/PirxcyFinal/PirxcyPinger/tree/main/Examples "github.com/PirxcyFinal/PirxcyPinger/tree/main/Examples")
mysqlsockets 0.0.1
A module which helps you use your MYSQL database over local network by connecting another devices.
Keywords: python,socket,sockets,mysql,sql,database,server-client,sql remote user


# mysqlsockets


```cmd
pip install mysqlsockets
```

Developed by Vedant Barhate (c) 2022

You need basic knowledge of Python And MySQL to use this package.

This package is useful when you want to use your MySQL database from one device on another devices.


## Example
### Creating A Server

```python
from mysqlsockets import Server
server = Server(server_host='<IPv4 addr>', server_port=9999, db_host='localhost', db_user='root', db_pswd='<user-pswd>', db_name='<database name>')
server.startServer()
# When You Are Done
server.stopServer()
```

### Creating A Client
```python
from mysqlsockets import Client
client = Client(host='<IPv4 addr of device to join>', port=9999)
client.connectClient()
data = askQuery('<SQL-Query>')
print(data)
# When You Are Done
client.closeClient()
```