semvec/pypi-packages huggingface dataset:
explorecourses 1.0.6
A Python API for Stanford Explore Courses

# Stanford Explore Courses Python API #

This package is a Python API for Stanford's Explore Courses site, which enables users to search through Stanford's catalog of over **13,000 courses**.

## Installation ##
Type the following command in terminal to install:

`pip install explorecourses`

## Usage ##
Import the package's classes into your Python program:

`from explorecourses import *`

Import the filters module if you would like to use search filters:

`from explorecourses import filters`

Create a new CourseConnection:

`connect = CourseConnection()`

Query the Explore Courses database by department code:

`courses = connect.get_courses_by_department("MATH", year="2017-2018")`

Apply filters to your query:

`courses = connect.get_courses_by_query("all courses", filters.AUTUMN, filters.WAY_AII)`

## Sample Program ##
```python
from explorecourses import *
from explorecourses import filters

connect = CourseConnection()

# Print out all courses for 2017-2018.
year = "2017-2018"
for school in connect.get_schools(year):
    for dept in school.departments:
        courses = connect.get_courses_by_department(dept.code, year=year)
        for course in courses:
            print(course)

```


## Thanks ##
Thanks to Jim Sproch who wrote Stanford's Explore Courses Java API.
npVCC2016 1.0.2
npVCC2016: Python loader of npVCC2016 speech corpus

# `npVCC2016` - Python loader of npVCC2016Corpus

`npVCC2016` is a Python loader set for [npVCC2016 non-parallel speech corpus](https://github.com/tarepan/npVCC2016Corpus).  
For machine learning, corpus/dataset is indispensable - but troublesome - part.  
We need portable & flexible loader for streamline development.  
`npVCC2016` is the one!!  

## Demo

Python/PyTorch  

```bash
pip install npVCC2016
```

```python
from npVCC2016.PyTorch.dataset.waveform import NpVCC2016

dataset = NpVCC2016(".", True, True)

for datum in dataset:
    print("Yeah, data is acquired only two line of code!!")
    print(datum)
``` 

`npVCC2016` transparently download corpus, structure the data and provide standarized datasets.  
What you have to do is only importing it!
eqparse 0.0.2
reads SIM and HSR files produced by eQUEST simulations
Requires Python: >=3.6

# eqparse

Python processing for eQUEST SIM, HSR, and INP files

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.


### Installing

Use pip to install this package (you can always clone and manually import it as well):

```
pip install eqparse
```

to Load a SIM file, use LoadSim and do not use a file extension:

'''
mysim = eq.LoadSim('C:/myinputfile')
'''

This allows access to 'sim', 'hsr', and 'inp' modules (inp module is currently a work in progress. Support will be added to handle running simulations and creating ECMs.

Most reports can be accessed like this, returning Pandas Dataframes:


>mysim.sim.bepu()
>mysim.sim.ssa()


HSR files can be accessed like this:


>df = mysim.hsr.df



There are also some plotting functions, using Plotly, optimized for eQUEST HSR files:

>mysim.hsr.plot.line(df)
>mysim.hsr.plot.heatmap(df, 0) # < 0 refers to column number


## Authors

Michael Sweeney (github.com/michaelsweeney)

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

Thanks to Santosh Philip and his excellent Eppy project for inspiration!