semvec/pypi-packages huggingface dataset:
capturegridbot 0.2.0
Simple automation tool for capture grid
Requires Python: >=3.5, <4
Keywords: capturegrid

# Capture Grid Bot

Simple application to automate common use  cases with [CaptureGrid4](https://www.kuvacode.com/)

## Installation

The actual application can be installed or upgraded with the following command:

    sudo pip3 install -U capturegridbot --system

## CaptureGrid4 Setup

The easiest way to CaptureGrid4 should be installed via snap. However, due to the strict limitations of the snap 
sandboxing, it is expected that you have installed it with `--devmode`. Otherwise downloading of files might not
work correctly.

Given this, it is best to reinstall the application to make sure that the

    sudo snap remove capturegrid4; sudo snap install capturegrid4 --devmode
terminal-preroll 1.0
Stream Preroll Animation for Python

# Terminal Preroll Animation in Python
![a screenshot of the script in action](sample.png)

`terminal-preroll` is a small, configurable python module for displaying a visually-pleasing, simplistic terminal animation in the style of a "loading screen", intended for use with small games or as a pre-roll animation for a livestream or video; thus the name. The script requires and accepts only three pieces of information:
- the location of a file telling it how to draw its banner;
- the location of a file telling it where to find text for the loading screen itself (the "hype" file), and;
- the amount of time, in minutes, you want it to last for.

The banner and hype files are simple json files, easily-edited, and the script itself handles sizing itself dynamically for your terminal window by use of the python package `blessings`, with colours provided by `termcolor`.

## How to install
If you're running python3 on most systems, you can install the package with the following command:

```shell script
pip3 install terminal-preroll
```

You will also need to either download (from this repo) or create a banner file and hype file.

## How to run
Assuming you have your banner and hype files, you can run the script very easily from the command line:

```shell script
python3 -m terminal-preroll -t 15 -b banner.json -s hype.json
```

Where:
- `-t` is an argument giving the time-to-run in minutes
- `-b` is an argument giving the path to the banner object
- `-s` is an argument giving the path to the hype object.

### Modifying the Banner Object
The banner file is a json object, of the rough structure below:
- a "banner_strings" attribute, itself an array of strings, each representing one line of the banner.
- a "line_colors" attribute, an array of strings giving a valid terminal color to each line. If there are less entries in this array than lines in banner_strings, the default terminal colour is used.

**Note:** Much like python, the json standard requires two particular characters be "escaped" by adding an extra `\` before them in the strings in banner_strings. This is important if you are using ASCII art, such as in the example below. In your ASCII art, replace `\` with `\\` and `"` with `\"` to account for this.

### Modifying the Hype Object
The hype object is very simple - it's just an object of arrays. The "name" of each array is used as the value of `phase` as in the example screenshot, while the strings in each phase's array are scrolled on the bottom as they "complete".

The script uses the total number of lines in this object and the time the animation is expected to last to determine how often to scroll the screen and how quickly to update the "current item" loading bar.
seoul 0.2.0
Seoul public data API client library.

seoul-py
========

Python API client library for `Seoul Open Data Plaza (서울 열린데이터 광장)`__.

.. __: http://data.seoul.go.kr


Examples
--------

Realtime subway position
````````````````````````

.. code-block:: python

   >>> from seoul import Client
   >>> seoul = Client()
   >>> trains = seoul.get_subway_realtime_position('6호선')
   >>> for train in trains:
   ...     print(f"{train.subway_name} {train.number}번 열차 @ {train.station_name}")
   ... 
   6호선 6265번 열차 @ 화랑대(서울여대입구)
   6호선 6271번 열차 @ 이태원
   6호선 6277번 열차 @ 불광


Realtime city air quality
`````````````````````````

.. code-block:: python

   >>> from seoul import Client
   >>> seoul = Client()
   >>> measurements = seoul.get_air_realtime_city()
   >>> for m in measurements:
   ...     print(f"AQI @ {m.region_name} {m.station_name}: {m.index_value}")
   ...
   ... AQI @ 도심권 중구: 55.0
   ... AQI @ 도심권 종로구: 55.0
   ... AQI @ 도심권 용산구: 53.0
   ... AQI @ 서북권 은평구: 51.0
   ... AQI @ 서북권 서대문구: 43.0


Supported APIs
--------------

- `서울시 지하철 실시간 열차 위치정보`__
- `서울시 권역별 실시간 대기환경 현황`__

.. __: http://data.seoul.go.kr/dataList/OA-12601/A/1/datasetView.do
.. __: http://data.seoul.go.kr/dataList/OA-2219/S/1/datasetView.do
reliability-stability-calc 0.0.4
Calculation for relibability and stability three data sets

# Reliability Stability

## Description

reliability-stability is a simple python package, which takes a csv or other pandas compatable data file and gives the reliability and stability of three columns for test-retest reliability and stability. 

[![nimh-mbdu](https://circleci.com/gh/nimh-mbdu/data-reliability-stability.svg?style=shield)](https://app.circleci.com/pipelines/github/nimh-mbdu/data-reliability-stability)

The reliability and stability calculations, as well as an assumption test, are based on the 1969 paper from Heise, D. R. ['Separating Reliability and Stability in Test-Retest Correlation'](https://doi.org/10.2307/2092790).

## Installation

pip install reliability stability:

```
>>> pip install reliability-stability-calc
```

## Usage

reliability stability is a python package with fuctions defined to: 
1. calculate correlation between two data columns (calc_correlation)
2. calculate the test-retest reliability between three data columns (calc_reliability)
3. calculate the test-retest stability between three data columns (calc_stability)
4. perform an assumption test with a fourth column (assumption_test)
5. implement a bootstap test to test assumptions (bootstrap_assumption_test)

Refer to the [documentation](https://github.com/nimh-mbdu/data-reliability-stability/wiki) for more examples and narrative guides.

## Citations

Heise, D. R. (1969). Separating reliability and stability in test-retest correlation. American Sociological Review, 34(1), 93–101. https://doi.org/10.2307/2092790