semvec/pypi-packages huggingface dataset:
tilty-dashboard 1.2.0
A live dashboard for the tilty based on Flask-SocketIO

Tilty Dashboard
===============

[![Coverage Status](https://coveralls.io/repos/github/myoung34/tilty-dashboard/badge.svg)](https://coveralls.io/github/myoung34/tilty-dashboard)
[![PyPI version](https://img.shields.io/pypi/v/tilty-dashboard.svg)](https://pypi.python.org/pypi/tilty-dashboard/)

This is a minimalistic websocket based dashboard for the Tilt hydrometer.

It's fed by sqlite via [tilty](https://github.com/myoung34/tilty)

![](images/dash.gif)

## Installation And Running ##

### Docker ###

```
# assume $(pwd)/data contains tilt.sqlite that is being written to with tilty
$ docker run -it data:/etc/tilty -p 5000:5000 myoung34/tilty-dashboard:latest
# now hit http://localhost:5000
```

### Pip ###

```
$ pip install tilty-dashboard

$ cat <<EOF >/etc/tilty/prod.config
[webapp]
  host=0.0.0.0
  port=5000
  database_uri="sqlite:////etc/tilty/tilt.sqlite"
EOF

$ cat <<EOF >/etc/tilty/prod.configspec
[webapp]
  host=string(default=127.0.0.1)
  port=string(default=5000)
  database_uri=string(default="sqlite:////etc/tilty/tilt.sqlite")
EOF

$ curl -sL https://raw.githubusercontent.com/myoung34/tilty-dashboard/master/config/prod_gunicorn.py >/etc/tilty/gunicorn.py

$ cd /usr/local/lib/python3.7/dist-packages

$ gunicorn --worker-class eventlet -w 1 -c /etc/tilty/gunicorn.py tilty_dashboard:app
```

## Local Development ##

Seed the database:

```
$ make seed
```

Run it in docker:

```
$ docker-compose up
```

Then just hit http://localhost:5000
pypi-auto-release 0.0.2
Automatic releases for PyPi
Keywords: pypi,python,automation,cicd


# pypi-auto-release
Sample auto release repository to PyPi using Github actions

Contains files for automatic deployment to PyPi using Github Actions and Github tags

Blog post explaining the implementation https://medium.com/@VersuS_/automate-pypi-releases-with-github-actions-4c5a9cfe947d

PyPi project https://pypi.org/project/pypi-auto-release/
eis-cream-cmd 0.0.1
data visualization tool for echem data
Requires Python: >=3.6

# Machine Learning on EIS

## Project Objectives:


### Current Functionality:
Our results are presented in the user interface called echem_pred. This UI has the following functionalities:
* Data visualization:
- [x] EIS plotting
- [x] dqdv
- [x] Battery cycling plotting

* Machine Learning:
Our machine learning has two different modes: online training and offline training. Online training includes.... while offline training uses developed....
- [ ] Offline data training
- [ ] Online data training 
    - [ ] Decision Tree
    - [ ]   
    - [ ]   

## Modules Overview:

* echem_pred.py: Main file for the user interface. Including three main parts: main window, data visualization (window 2), and machine learning (window 3).

## Software Tutorial:

After cloning this repo annd installing the requirements, the Echem Visualizer App can be used to interact the xx functions to visualize cycling data. 

To start the app please run....:

After running, the main page should look like this:



## Installation:
### Environment:
(What's the required environment)

### Instruction:
(How to install)

## Acknowledgement:
eis-cream 0.0.1
data visualization tool for echem data
Requires Python: >=3.6

# Machine Learning on EIS

## Project Objectives:


### Current Functionality:
Our results are presented in the user interface called echem_pred. This UI has the following functionalities:
* Data visualization:
- [x] EIS plotting
- [x] dqdv
- [x] Battery cycling plotting

* Machine Learning:
Our machine learning has two different modes: online training and offline training. Online training includes.... while offline training uses developed....
- [ ] Offline data training
- [ ] Online data training 
    - [ ] Decision Tree
    - [ ]   
    - [ ]   

## Modules Overview:

* echem_pred.py: Main file for the user interface. Including three main parts: main window, data visualization (window 2), and machine learning (window 3).

## Software Tutorial:

After cloning this repo annd installing the requirements, the Echem Visualizer App can be used to interact the xx functions to visualize cycling data. 

To start the app please run....:

After running, the main page should look like this:



## Installation:
### Environment:
(What's the required environment)

### Instruction:
(How to install)

## Acknowledgement:
twexxor-string-repeater 1.0.4
Repeat huge string patterns quickly up to a precise number count.
Keywords: algorithm,big,bitwise,characters,compatibility,concatenate,fast,fastest,large,nth,pattern,polyfill,precision,repeating,simple,text,unicode

``` console
pip install twexxor-string-repeater
```
``` python
import twexxor_string_repeater
twexxor_string_repeater.twexxorStringRepeater('1', 10) # '1111111111'
twexxor_string_repeater.twexxorStringRepeater(1, 10) # '1111111111'
twexxor_string_repeater.twexxorStringRepeater('1', 0) # ''
twexxor_string_repeater.twexxorStringRepeater('twexxor', 2) # 'twexxortwexxor'
twexxor_string_repeater.twexxorStringRepeater('twexxor', '2') # False
twexxor_string_repeater.twexxorStringRepeater('xx', 'twexxor') # False
twexxor_string_repeater.twexxorStringRepeater('1', -2); # False
```