semvec/pypi-packages huggingface dataset:
l5_shoppinglist 20.2.1
gtk l5_shoppinglist
Requires Python: >=3.7

# l5_shoppinglist

gtk shoppinglist for librem5

### install and run

install via pip (preferred!)

`python3 -m pip install --user l5_shoppinglist`

and simply run it with:

`python3 -m l5_shoppinglist`

the convenient according desktop shortcut would be at:

`~/.local/share/applications/l5_shoppinglist.desktop`

with the following content:

```
[Desktop Entry]
Categories=Accessories
Comment=Libre shopping list
Exec=python3 -m l5_shoppinglist
Icon=software-installed-symbolic
Name=l5_shoppinglist
NoDisplay=false
Terminal=false
Type=Application
```


### privacy - user data:

as a privacy respecting app l5_shoppinglist makes no network connections.
no data gets collected or sent anywhere - the user is in full control.
the database is a simple json file which could be edited with a text editor.
it is stored at:

`~/.local/l5_shoppinglist/shoppinglist_db.json`


### running the application without pip:

clone the repository e.g. to `~/opt/`

it can be started via `python3 ~/opt/l5_shoppinglist/l5_shoppinglist/__main__.py`

to run it conveniently from the app drawer on librem5,
it is recommended add a `l5_shoppinglist.desktop` file to:

`~/.local/share/applications`

assuming the path of the `__main__.py` file is:

`/home/purism/opt/l5_shoppinglist/l5_shoppinglist/__main__.py`

the .desktop would need the following content:

```
[Desktop Entry]
Categories=Accessories
Comment=Libre shopping list
Exec=python3 /home/purism/opt/l5_shoppinglist/l5_shoppinglist/__main__.py
Icon=software-installed-symbolic
Name=l5_shoppinglist
NoDisplay=false
Terminal=false
Type=Application
```
punditkit 0.0.2
PunditKit: A GUI for Scikit-Learn Models

# PunditKit

Simplify. Visualise. Learn.

![Screenshot](Screenshot.jpg)


PunditKit is a free toolkit for exploratory data analysis and modelling of
tabular data - such as a database or a spreadsheet - with a simple interface,
visual diagnostics, and the choice of a large number of different models.

Included Features:
 - Machine learning models using [scikit-learn](https://scikit-learn.org)
 - Interpretable explanations of predictions using [lime](https://github.com/marcotcr/lime)
 - Exploratory data summaries for checking datasets
 - Model diagnostics for evaluating the effectiveness of different models
 - Feature importance: identify key drivers of the response
 - Partial dependence plots: understand relationships with a particular feature.

PunditKit is under active development. The goal is to develop an opinionated
modelling framework with best practice modelling and visualisation techniques.
If you encounter any problems or have any feature requests, please consider
raising an [issue](https://github.com/JackyP/punditkit/issues).

## Installation (via pip)
PunditKit is developed using Python. First download and install a Python 3.x
distribution such as [Anaconda](https://www.anaconda.com/distribution/#download-section)

PunditKit can then be installed using ``pip`` from the command line (if ``pip``
is added to PATH during installation) or using Anaconda prompt.

```
pip install punditkit
```

This adds the ``punditkit`` command.

## Modelling a dataset
Suppose you have a file called ``iris.csv`` that you would like to model.

To use punditkit on the dataset, run:

```
punditkit iris.csv
```


Currently only Comma Separated Values (CSV) datasets are supported. Datasets
within Excel spreadsheets can be analysed using punditkit, use Save As CSV from
within Excel to convert to the right format.
torch-specinv 0.1
A pytorch package for Spectrogram Inversion

# Spectrogram Inversion

Some useful spectrogram inversion tools written in PyTorch.

## Methods

### Griffin & Lim
Griffin, Daniel, and Jae Lim. "Signal estimation from modified short-time Fourier transform." IEEE Transactions on Acoustics, Speech, and Signal Processing 32, no. 2 (1984): 236-243.

### RTISI-LA
Zhu, Xinglei, Gerald T. Beauregard, and Lonce Wyse. "Real-time iterative spectrum inversion with look-ahead." In 2006 IEEE International Conference on Multimedia and Expo, pp. 229-232. IEEE, 2006.

### L-BFGS
Decorsière, Rémi, Peter L. Søndergaard, Ewen N. MacDonald, and Torsten Dau. "Inversion of auditory spectrograms, traditional spectrograms, and other envelope representations." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 1 (2014): 46-56.



## TODO
- [ ] Speed comparison on GPU. 
- [ ] Documentation.
- [ ] Examples.
nCoV 0.1.7
Get instant information about 2019-nCoV

# nCoV


Get instant information about 2019-nCoV 获取并记录新冠病毒实时数据


`nCoV` 是一个可以在命令行查询并记录实时新型冠状病毒疫情信息的爬虫应用。
可以选择查看全国总览，各省各市详细信息或全部信息并且可以选择是否以表格文件存储数据



### 使用方式 Usage

```
Usage: nCoV [OPTIONS]

Options:
  -o      Output the overall data of China
  -d      Output detailed data of all provinces and some cities
  -a      Output all data. overall --> detailed (Default)
  --help  Show this message and exit.
```


### 安装 Install

```
$ pip install nCoV
```


### License

[MIT](https://github.com/EthanGeekFan/nCoV/blob/master/LICENSE)