semvec/pypi-packages huggingface dataset:
TorchFly 0.0.1
Pytorch Fast Development Kit

# TorchFly

TorchFly is a PyTorch Fast Development Kit. The purpose is to learn the pipelines of the SOTA algorithms in Deep Learning areas like CV, NLP and RL. The utilities provided in this kit will shorten the time needed to rebuild some basic functions. Now, the kit is mainly for personal use, but will be updated from time to time. 

## Installation

### Installing via pip:
Installing is simple using `pip`.

   ```bash
   pip install torchfly
   ```
   
### Installing from source
You can clone the repository.
    ```bash
    git clone https://github.com/qywu/TorchFly.git
    ```

### Docker
It is recommended to run on Nvidia Docker for better performance.

    ```bash
    FROM nvcr.io/nvidia/pytorch:18.12.1-py3
    RUN apt-get update
    
    RUN pip install torchfly
    ```
 
 ## TODOS
 
1. Custom Bucket Sampler

2. Custom Beam Search

 
 ## Code References
 
 [FastAI](https://github.com/fastai)
 
 [AllenNLP](https://github.com/allenai/allennlp/)
 
 [Pytorch BERT](https://github.com/huggingface/pytorch-pretrained-BERT)
volunteer-scheduler 2019.5.9
Volunteer workshift scheduler
Requires Python: >=3.5
Keywords: schedule scheduling helpline workshift ortools

# Volunteer workshift scheduler for a helpline
Code written in Python finds the best possible schedule for volunteers while incorporating constrains and special requirements using **[CP-SAT Solver](https://developers.google.com/optimization/cp/cp_solver)** (Constraint Programming - boolean SATisfiability problem Solver).

Java version is in development.

### The problem
Arranging volunteers for a helpline service for the period of one month.

There are three types of work that could be done on the same day:
- Phone shift: Every day.
- Chat shift: Mondays and Wednesdays.
- Observer shift: Any day.

### Constraints:
Properties of each volunteer:
- Available days.
- Function: doing phone, chat, both or observation for training purposes.
- Maximum amount of weekend shifts.
- Whether their shifts have to be on separate weeks.
- Whether they welcome observers.
- Whether they want to work alone.
- Whether they cannot yet work alone.
- List of people they do not want to work with.

Each volunteer has to have 4 days between their shifts.

### Priorities:
1. Have minimum one volunteer for each day.
2. Fill chat shifts.
3. Have a second volunteer even on phone days.
4. Employ observers on days when there is only phone shift.

### Objective:
 Maximize filled shifts.

<hr>

## Installation

Requires 64-bit Python 3.7

<code>pip install helpline-scheduler</code>

## Usage
1. Select your language in config.py: EN/HU/CN

   Alternatively you can create your XX language extension by creating the corresponding data_XX.csv and language_XX.py files.
2. Export corresponding data/data_XX.csv file into a spreadsheet, fill in your data, then export back into this file.
3. Execute the program from its directory:

<code>python schedule.py</code>

## Output

1. Prints results into the terminal and saves it into schedule_YYYY_MM____YYYYMMDD_hhmm.txt.
2. Saves results into schedule_YYYY_MM____YYYYMMDD_hhmm.csv which can be conveniently imported into a spreadsheet.
python-test-pip 0.1
A quote provider package

# pyquotes

[![Issues](https://img.shields.io/github/issues-closed/oss2019/pyquotes.svg?style=flat-square)](https://github.com/oss2019/pyquotes/issues) [![Pull Requests](https://img.shields.io/github/issues-pr-closed/oss2019/pyquotes.svg?style=flat-square)](https://github.com/oss2019/pyquotes/pulls) [![License](https://img.shields.io/apm/l/vim-mode.svg?style=flat-square)](https://github.com/oss2019/pyquotes/blob/master/LICENSE) [![Gitter](https://img.shields.io/badge/chat-on%20gitter-ff006f.svg?style=flat-square)](https://gitter.im/oss2019/pyquotes)

This is a API in python3 to get quotes.

## Plan
### Phase 1
The idea is to use scrap the quotes from [BrainyQuote](https://www.brainyquote.com/). We will use [beautifulsoup4](https://pypi.org/project/beautifulsoup4/) for this.<br />
To get started with this you can watch a small tutorial [here](https://www.youtube.com/watch?v=ng2o98k983k).<br />
Functionalities to be supported are:
* Get quotes from particular topic, categories.
* Get quotes from particular person.

### Phase 2
Convert this into a pip package.

### Phase 3
Use what we have built till now to make a twitter bot which will tweet quotes everyday.

## Contribution Guidelines
We would love contributions from everyone. Some general guidelines are:
* Write neat code with proper comments.
* Follow PEP8 coding style.
* Write tests wherever necessary.
* Write descriptive commit messages.

For any more information contact [akshatkarani](https://github.com/akshatkarani)

## Legal
Please refer to the [licence](https://github.com/oss2019/pyquotes/blob/master/LICENSE)
BIDS-JSON-consolidation 0.0.1
A simple package for consolidating BIDS formatted JSON data
Requires Python: >=2.7, <4

# JSON Phenos to Dict - Package

This pacakge is specific for BIDS formatted data with JSON files.
Create a CSV or obtain a python dictionary for your desired subjects and phenotypes.

You can use this tool in the command line or in a python session.
If you specifically want a dictionary, import that package and invoke 'main' in a python session. You will be prompted for arguments: desired phenotypes, the filepath 'base' for your JSON files, and the name of the (optional) output csv.

For help, enter 'python consolidate\_json\_phenos.py -h' in the command line for the list of options.

More can be found here: [Marco-Nardin-GitHub](https://github.com/marconardin/csv-json-csv)
jacotei-api 1.0.0
SDK for consumption of JáCotei Api

# jacotei-api-sdk-python3

## Exemplos ##

Publicacao de 1000 ofertas

```Python 
#!/usr/bin/python3

import json

from jacotei_api_v1 import *
from jacotei_api_v1.api import *
from jacotei_api_v1.model import *
from jacotei_api_v1.client import ApiClient;

apiClient = client.ApiClient({	
                'client_id':'xxxxx',
				'access_token':'xxxx'},
				'https://webapisb.jacotei.com.br/publisher/v1')

offers_api = OffersApi.OffersApi(apiClient)
offer_request = OfferRequest.OfferRequest()
offer_request.offers = []

for i in range(0,  1000):
    offer = Offer.Offer()
    offer.sku = i
    offer.available = True;
    offer.title = 'Produto '+str(i)
    offer.link = 'http://aasdfaf.com/asdflasdf'
    offer.images = ['http://aasdfaf.com/asdflasdf.jpg']
    offer.categories = ['Categoria','Subcategoria','Sub subcategoria']
    price = OfferPrice.OfferPrice()
    price.price = 1.99
    price.installment = 1
    price.installment_value = 1.99
    offer.prices  = [price]
    offer_request.offers .append(offer)

try:
    offers_result = offers_api.update_offers_using_post(offers = offer_request)
    print (vars(offers_result))
except BaseException as e:
    print(e.msg)

```
cpuprofile 1.0.1
A simple Python package to profile CPU speed by computing the Fibonacci sequence

# CPU Profile

This is a simple package to measure CPU speed by calculating the time it takes to
compute Fibonacci numbers. It is useful in contexts where CPU speed is unknown or can
fluctuate, such as cloud function environments, and can serve as a rough measure of how
long subsequent compute-intensive code will take to run.

## Installation

```sh
pip install cpuprofile
```

## Usage

```python
from cpuprofile import profile_cpu

# Calculate the CPU speed by computing Fibonacci numbers.

elapsed_time = profile_cpu()

# elapsed_time is ~0.18 (seconds) on a 2.5 GHz Intel Core i7 2015 Macbook Pro.

# Or, a number can be specified to calculate the nth Fibonacci (default: 28).

elapsed_time = profile_cpu(15)  # 15th Fibonacci; takes around 0.0006 seconds
elapsed_time = profile_cpu(35)  # 35th Fibonacci; takes about 5 seconds
```

## Credit

The minimalist approach of recursively calculating Fibonacci numbers as a way to profile
the CPU is inspired by:

- Booth, J. (2015).
  [Not so incognito: Exploiting resource-based side channels in javascript engines](http://jombooth.com/static/thesis.pdf)
  (Undergraduate thesis, Harvard University).
pyfancyplots 0.1
A python plotting script containing Luke Olson's styling preferences

## PyFancyPlots
Does your advisor enjoy fancy plots?  Is his name Luke?  Then this plotting script is for you!

## Overview
Python plotting tool with default options to make plots easy to read.  This
script uses matplotlib and Seaborn.  

## Testimonials
"That plot looks great." -- Luke

## Plotting Examples
Import `plot.py' for default plotting parameters to be set.  A few important
methods:
    - The method add_anchored_legend will add a multicolumn legend above your
      plot.  Examples of this are in the plots below.
    - The method save_plot will save your figure with settings preferred by Luke
    - The methods stacked_barblot and partially_stacked_barplot create Seaborn
      barplots by stacking your data (or some of it for you).  

## Example Line Plot
Default and Luke-recommended settings are displayed in a simple line and scatter
plot below.  Make sure to call add_luke_options() and save_plot(...) to add
these settings to your figures. 
![](figures/line_plot.png)

## Standard and Stacked BarPlots
The standard and stacked bar plots (available in methods barplot(...) and
stacked_barplot(...)) take a typical list of x_data and either a
single list or list of lists for y_data.  If y_data is a list of lists, a list
of corresponding labels is also required.  Each list of y_data will be plotted
at all x_data positions, as shown below.
![](figures/barplot.png)
![](figures/stacked_barplot.png)

## Partially Stacked BarPlot
The previous standard and stacked barplots can also be combined with method
partially_stacked_barplot(...).  This method takes x_data as usual, and then a
list of y_data.  The list for y_data contains both y_data lists and lists of
lists.  E.g. y_data = [y0, y1, [y2, y3], y4, [y5, y6]] will plot y0, y1, and y4
as normal barplots, while y3 will be stacked on top of y2, and y6 will also be
stacked on y5.  Correpsonding labels should be in the same format as y_data.
![](figures/partially_stacked_barplot.png)