semvec/pypi-packages huggingface dataset:
simplebeep-leberwurscht 0.0.1
Platform-independent beep
Requires Python: >=3.6

Platform-independent beep function, easily installable with pip.

Provides a `simplebeep.beep(pitch, duration)` function and a `simplebeep.play_ndarray(array, sample_rate)` function.

For Linux, it uses gstreamer, which is usually already installed. On Windows / Mac the package depends on simpleaudio, which is automatically installed as a dependency when installing with `pip install simplebeep`.
DicomRTTools 0.0.2
Tools for reading dicom files, RT structures, and dose files, as well as tools for converting numpy prediction masks back to an RT structure

# This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures
## Highly recommend to go through the jupyter notebook Data_Curation_and_Predictions_to_RT
## Data_Curation_and_Predictions_to_RT has three main parts
### 1) Identify RT structures and names in multiple patients
### 2) Creating associations file and turning dicom into nifti/numpy files
### 3) Turning predictions into RT structures
#### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
##### Please consider using the .write_parallel if you have many patients
##### Ring update allows for multiple rings to be represented correctly
![multiple_rings.png](./Images/multiple_rings.png)

Various utilities created to help with the interpretation of dicom images/RT Structures

RT Structure and dicom conversion to numpy arrays

This code is designed to receive an input path to a folder which contains both dicom images and a single RT structure file

For example, assume a folder exists with dicom files and an RT structure located at 'C:\users\brianmanderson\Patient_1\CT1\' with the roi 'Liver'

Assume there are 100 images, the generated data will be:
Dicom_Image.ArrayDicom is the image numpy array in the format [# images, rows, cols]

You can then call it to return a mask based on contour names called DicomImage.get_mask(), this takes in a list of Contour Names

You can see the available contour names with

Example:

    from Image_Array_And_Mask_From_Dicom_RT import Dicom_to_Imagestack
    Dicom_reader = Dicom_to_Imagestack(get_images_mask=False)
    path = 'C:\users\brianmanderson\Patients\'
    Dicom_reader.down_folder(path)
    # See all rois in the folders
    for roi in Dicom_reader.all_rois:
        print(roi)
    
    
    Contour_Names = ['Liver']
    associations = {'Liver_BMA_Program4':'Liver','Liver':'Liver'}
    path = 'C:\users\brianmanderson\Patients\Patient_1\CT_1\'
    Dicom_reader = Dicom_to_Imagestack(get_images_mask=True, Contour_Names=Contour_Names, associations=associations)
    
    Dicom_reader.Make_Contour_From_directory(path)
    image = DicomImage.ArrayDicom
    mask = DicomImage.mask

    pred = np.zeros([mask.shape[0],mask.shape[1],mask.shape[2],2]) # prediction needs to be [# images, rows, cols, # classes]
    pred[:,200:300,200:300,1] = 1
    
    output_path= os.path.join('.','Output')
    Dicom_reader.with_annotations(pred,output_path,ROI_Names=['test'])
    
    '''
    Write the images and annotations as niftii files in parallel!
    '''
    Dicom_Reader.write_parallel(out_path=export_path,excel_file=os.path.join('.','MRN_Path_To_Iteration.xlsx'))
pygov-br-mj 0.1.0
A short description for your project.

.. image:: https://travis-ci.org/tenhodito/pygov-br.svg?branch=dev
    :target: https://travis-ci.org/tenhodito/pygov-br
.. image:: https://landscape.io/github/tenhodito/pygov-br/dev/landscape.svg?style=flat
   :target: https://landscape.io/github/tenhodito/pygov-br/dev
   :alt: Code Health
.. image:: https://coveralls.io/repos/github/tenhodito/pygov-br/badge.svg?branch=dev
    :target: https://coveralls.io/github/tenhodito/pygov-br?branch=dev


Pygov-br é uma lib que tem o objetivo de unificar as API's de dados abertos
governamentais brasileiros. Atualmente, a pygov-br tem suporte para:

* Webservices da Câmara dos Deputados

Instalação
----------

::

    pip install pygov-br

Utilização
----------

::

    from pygov_br.camara_deputados import cd
    cd.deputies.all()
osu-cplayer 1.0.4
fast and featured command line osu! song player built on mpv
Requires Python: >=3.6

# osu-cplayer
Fast, compact, and featured command line osu! song player built on mpv.
![screenshot](https://github.com/eshrh/osu-cplayer/raw/master/2020-04-23-161205_1920x1080_scrot.png)

## Installation
1. Install osu-cplayer via pip: `pip install osu-cplayer`
2. Install [mpv](https://mpv.io/installation/)
3. On first run, enter the absolute paths of your songs folder, collections.db file and osu!.db file
4. These settings get saved in your home folder as `~/.osupaths` or equivalent.

I've tested this on gnu/linux and macos, and it works. Windows is untested though, I'd appreciate someone trying it out.

## Usage
+ Previous/Next Song: `left`/`right`
+ Move Selection: `up`/`down`, `k`/`j`, left click, or scroll
+ Play song: `enter` or double click
+ Toggle play/pause: `p`
+ Restart song and play: `r`
+ Quit: `q` or `esc`
+ Add song to queue: `a`
+ Clear queue: `A`
+ Shuffle: `s`
+ Sort alphabetically(default): `S`
+ Sort by date added: `d`
+ Filter: `:` or mouse followed by filter term
	+ Reset filter: `esc`
	+ Exit text box: any special key or mouse
+ Filter by Collection: `c` or mouse, followed by collection name, and `enter`
+ Open this help page: `?`

## Other
osu!.db reading code is taken from [OsuDbReader](https://github.com/Awlexus/PyOsuDBReader/)

## Dependencies
+ Python 3.6+
+ [python-mpv](https://github.com/jaseg/python-mpv)
+ [urwid](https://github.com/urwid/urwid/wiki/Installation-instructions)
+ [tinytag](https://github.com/devsnd/tinytag)
nokia1830pss 1.0.3
A SSH library for Nokia 1830-PSS NEs

# A ssh library for CLI/root shell on Nokia 1830-PSS Network Elements

## Install

pip install nokia1830pss

## Usage

### CLI interaction
```
from pss1830ssh.pss1830cli import PSS1830Cli

cli = PSS1830Cli('10.0.0.1', 22, 'admin', 'admin')
cli.open()
cli.execute('show version')
cli.execute('show card inv *')
cli.close()
```

### Root interaction
```
from pss1830ssh.pss1830root import PSS1830Root

# Execute command on the master shelf, active EC
root = PSS1830Root('10.0.0.1', 22, 'root', 'root')
root.open()
root.execute('ps -ef')

# Execute command on standby EC of the master shelf
root.login_to_stdby()
root.execute('ps -ef')
root.logout_from_stdby()

# Execute command on the active EC of a shelf without knowing which one
root.login_to_shelf(shelf=2)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on the active EC of a shelf assuming the active EC is EC 2/1
root.login_to_shelf(shelf=2, ec=1)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on a specific EC of a shelf
root.login_to_shelf(shelf=3, ec=18, act=False)
root.execute('ps -ef')
root.logout_from_shelf()

# Execute command on a slot
root.login_to_slot(shelf=1, slot=3)
root.execute('ps -ef')
root.logout_from_slot()

root.close()
```