semvec/pypi-packages huggingface dataset:
exconfig 0.2.0
Configuration

====================================================
exconfig: Create and configure input parameter files
====================================================

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About
-----

*exconfig* is a Python library for working with input parameter files. It
provides parameter description and validation, as well as ways of working
with various file formats.


Requirements
------------

*exonfig* requires Python 3.

Apart from Python, *exconfig* has a number of other requirements, all of which
can be obtained through either *pip* or *conda*, that will be automatically
installed when you install *exconfig*.

To see a full listing of the requirements, have a look at the project's
*requirements.txt* file.

If you are a developer of *exconfig* you will also want to install
additional dependencies for running *exconfig*'s tests to make sure
that things are working as they should. These dependencies are listed
in *requirements-testing.txt*.

Installation
------------

Stable Release
++++++++++++++

*exconfig*, and its dependencies, can be installed either with *pip*
or *conda*. Using *pip*::

    $ pip install exconfig

Using *conda*::

    $ conda install exconfig -c conda-forge

From Source
+++++++++++

After downloading the *exconfig* source code, run the following from
*exconfig*'s top-level folder (the one that contains *setup.py*) to
install *exconfig* into the current environment::

  $ pip install -e .
bpcs-conduit 0.0.9
Python SDK for BPCS Conduit data virtualization tool
Requires Python: >=3.6

# Introduction

This is a Python SDK to abstract the REST endpoints for the Blueprint Conduit product.

# Installation

NOTE: These steps aren't for Windows, but works for POSIX systems. Use whatever Windows uses in place of these instructions.

* Python3 is a prerequisite

* Start in a new directory, go to it:
```
mkdir conduit-sdk && cd conduit-sdk
```
* Install a virtualenv.
```
python3 -m venv .venv
```
* Go into the virtualenv.
```
. .venv/bin/activate
```
* Install bpcs-conduit
```
pip install bpcs-conduit
```
* Save this to a file called `test.py`:
```
from conduit_pkg.client import getDatabases
dbs = getDatabases()
for db in dbs:
    print(db)
```
* Execute with the CONDUIT_TOKEN, CONDUIT_SERVER
```
CONDUIT_SERVER=<server name> CONDUIT_TOKEN=<your token> python test.py
```
You should get something similar as output:
```
Database: file_blob
Database: redshift_redshift
Database: es_elasticsearch
Database: awss3_s3
```
vk-listener 1.0.1
Модуль для Накрутки Прослушиваний в Вашем Альбоме в Социальной Сети ВКонтакте.
Requires Python: >=3.0

<h1 align="center">VK-Listener</h1>

<h3>Модуль для Накрутки Прослушиваний в Вашем Альбоме в Социальной Сети ВКонтакте<h3>

<h4>1. Установите <a href="https://chromedriver.chromium.org/downloads">chromedriver.exe</a> Вашей версии Google Chrome<h3>
<h4>2. Перетащите <a href="https://chromedriver.chromium.org/downloads">chromedriver.exe</a> в C:\Users\User\AppData\Local\Google\Chrome<h4>
<h4>3. Если нет папки, то создай её<h4>

<h3>Простой пример кода с использованием библиотеки "VK-Listener"<h3>

```python
import vk-listener

listener.init('hide')

listener.authorization('Логин', 'Пароль')

if(listener.check()):
	print('Авторизация прошла успешно!')
else:
	print('Ошибка авторизации!')

listener.loop('Ссылка на Ваш альбом', 15)
```
vector-pkg-enderrayquaza 2.0.5
A package with for classes Vector (2d, 3d, 4d, Nd).
Requires Python: >=3.6

# Vector pkg
## Actual version : 2.0.2
### How to install
 ```shell
 pip install vector-pkg-enderrayquaza
 ```
 
##### Vector2d
```Python
import Vector.Vector2d as vec2
v = vec2.Vector2d(5, 7.5) #Creates a instance of Vector2d
print(v.x) #Shows his composant x >>> 5
print(v.y) #Idem                  >>> 7.5
print(v.st) #Shows his standard   >>> 9.013878188659973

u = vec2.Vector2d(-8, 0.25) #Creates an other instance of Vector2d
w = v+u #Makes a instance of Vector with composants (v.x + u.x; v.y + u.y)
print(w.x, w.y) # >>> -3 7.75
w = v-u #Idem but with a subtraction
print(w.x, w.y) # >>> 13 7.25

k = 5
w = v*k
print(w.x, w.y) # >>> 25, 37.5

#You can do this too
v += u
v -= u
v *= k

p = v**u #Calculates the scalar product of v and u          
print(p) # >>> -38.125
a = v%u  #Calculates the angle (in degrees) between v and u
print(a) # >>> 121.90015691773374
z = complex(v) #Makes a complex number equal at x+yi
print(z) # >>> (5+7.5j)
```
You can use the constant V0_ which is worth Vector2d(0, 0)

##### Vector3d
```Python
import Vector.Vector3d as vec3
v = vec3.Vector3d(5, 7.5, 0) #Creates a instance of Vector3d
print(v.x) #Shows his composant x >>> 5
print(v.y) #Idem                  >>> 7.5
print(v.z) #Idem                  >>> 0
print(v.st) #Shows his standard   >>> 9.013878188659973

u = vec3.Vector3d(-8, 0.25, 2) #Creates an other instance of Vector3d
w = v+u #Makes a instance of Vector with composants (v.x + u.x; v.y + u.y; v.z + u.z)
print(w.x, w.y, w.z) # >>> -3 7.75 2
w = v-u #Idem but with a subtraction
print(w.x, w.y, w.z) # >>> 13 7.25 -2

k = 5
w = v*k
print(w.x, w.y, w.z) # >>> 25, 37.5 0

#You can do this too
v += u
v -= u
v *= k

p = v**u #Calculates the scalar product of v and u          
print(p) # >>> -38.125
a = v%u  #Calculates the angle (in degrees) between v and u
print(a) # >>> 121.90015691773374
```
You can use the constant V0_ which is worth Vector3(0, 0, 0)

##### Vector4d
Idem that Vector3d and Vector2d but with a 4th composant t (for time).

##### VectorNd
```Python
import Vector.VectorNd as vecN
v = vecN.VectorNd([8, 6, 9, 2.7, -4])
```
The rest is like other Vector classes.
genderperformr 1.2
GenderPerformr
Requires Python: >=3.6

# GenderPerformr

## Intro
GenderPerformr is the model release from the paper `It’s going to be okay: Measuring Access to Support in Online Communities` by Zijian Wang and David Jurgens (in proceedings of EMNLP 2018).

It is the current state-of-the-art method that predicts gender from usernames based on a LSTM model built in PyTorch (as of Sept. 2018).

See the [project website](http://blablablab.si.umich.edu/projects/support) for full details, including contact information.
 
## Install 

### Use pip
If `pip` is installed, genderperformr could be installed directly from it:

	pip install genderperformr
### From raw
`git clone` the project and do:

	python setup.py install

### Dependencies
	python>=3.6.0
	torch>=0.4.1
	numpy
	unidecode


## Usage and Example

### `predict`
`predict` is the core method of this package, 
which takes a single username of a list of usernames, and returns a tuple of raw probabilities in `[0,1]` (0 - Male, 1 - Female), and labels (M - Male, N - Neutral, F - Female, empty string - others). 

### Simplest usage

You may directly import `genderperformr` and use the default predict method, e.g.:

    >>> import genderperformr
    >>> genderperformr.predict("AdamMcAdamson")
    (0.019139649, 'M')
    
### Construct from class
Alternatively, you may also construct the object from class, where you could customize the model path and device:
 
	>>> from genderperformr import GenderPerformr
	>>> gp = GenderPerformr()
	
	# Predict a single username
	>>> gp.predict("John")
	(0.087956183, 'M')
	
	# Predict a list of names
	>>> probs, labels = gp.predict(["BarryCA67", "pizzamagic", "KatieZ22"])
    >>> f"Raw probabilities are {probs}"
    Raw probabilities are [0.03398224 0.5439474 0.93964571]
    >>> f"Labels are {labels}"
    Labels are ['M', 'N', 'F']


More detail on how to construct the object is available in docstrings.

### Model using new data partition 
If you want to use the model described in Supplemental Material using the new data partition, you may construct the object via

    >>> gp = GenderPerformr(is_new_model=True)

All other usages remain the same.


## Citation
    @inproceedings{wang2018its,
           title={It's going to be okay: Measuring Access to Support in Online Communities},
           author={Wang, Zijian and Jurgens, David},
           booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)},
           year={2018}
    }
    
## Contact
Zijian Wang (zij<last_name>@stanford.edu)

David Jurgens (<last_name>@umich.edu)
epico-wyattk1093 0.1.1
Some epic utilities
Requires Python: >=3.6

# Epico

Just some epic python utilities


##  Functions
 -  epico.pause()
 -  epico.dir.delete(path, recursive=False)
 -  epico.dir.create(path)
 -  epico.file.create(filename, encoding=None)
 -  epico.file.append(filename, data, encoding=None)
 -  epico.file.write(filename, data, encoding=None)
 -  epico.file.delete(filename)
 -  epico.file.exists(filename)
 -  epico.file.read(filename, mode='s')