semvec/pypi-packages huggingface dataset:
noos-ci 0.0.4
Shared workflows across CI/CD pipelines
License: MIT
Requires Python: >=3.7,<4.0

[![CircleCI](https://circleci.com/gh/noosenergy/noos-deploy.svg?style=svg&circle-token=68d1a71e4f53ab1a1f33110e9a8c24bd3300a8ba)](https://circleci.com/gh/noosenergy/noos-deploy)

# Noos Deploy

Software development kit for sharing workflows across CI/CD pipelines.

Such a project aims to enforce parity between development and CI/CD workflows, ensuring reproducability in local and remote containers (e.g. executable versions, command line calls, environment variables...).

## Installation

Install the package from the [PyPi repository](https://pypi.org/project/noos-ci/):

    $ pip install noos-ci

To enable shell completion, execute the following command (e.g. `zsh`),

    $ noosci --print-completion-script=zsh

And copy/paste its `stdout` into your shell config.

```bash
# NOOSCI completion script

_complete_noosci() {
    collection_arg=''
    if [[ "${words}" =~ "(-c|--collection) [^ ]+" ]]; then
        collection_arg=$MATCH
    fi
    reply=( $(noosci ${=collection_arg} --complete -- ${words}) )
}

compctl -K _complete_noosci + -f noosci
```

## Usage as a command line tool

The `noos-ci` package installs a CLI binary, for managing common CI/CD tasks.

From the terminal,

```
$ noosci

Usage: noosci [--core-opts] <subcommand> [--subcommand-opts] ...

Subcommands:

  docker.build       Build Docker image locally.
  docker.login       Login to Docker remote registry (AWS ECR or Dockerhub).
  docker.push        Push Docker image to a remote registry.
  git.config         Setup git credentials with a Github token.
  helm.install       Provision local Helm client (Chart Museum Plugin).
  helm.lint          Check compliance of Helm charts / values.
  helm.login         Login to Helm remote registry (Chart Museum).
  helm.push          Push Helm chart to a remote registry.
  helm.test          Test local deployment in Minikube.
  local.dotenv       Create local dotenv file.
  python.clean       Clean project from temp files / dirs.
  python.coverage    Run coverage test report.
  python.format      Auto-format source code.
  python.lint        Run python linters.
  python.package     Build project wheel distribution.
  python.release     Publish wheel distribution to PyPi.
  python.test        Run pytest with optional grouped tests.
  terraform.run      Run a plan in Terraform cloud.
  terraform.update   Update variable in Terraform cloud.
```

## Development

On Mac OSX, make sure [poetry](https://python-poetry.org/) has been installed and pre-configured,

    $ brew install poetry

This project is shipped with a Makefile, which is ready to do basic common tasks.

```
$ make

help                           Display this auto-generated help message
clean                          Clean project from temp files / dirs
format                         Run auto-formatting linters
lint                           Run python linters
test                           Run pytest with all tests
package                        Build project wheel distribution
release                        Publish wheel distribution to PyPi
```
chime-frb-constants 2020.12
CHIME/FRB Constants
License: MIT
Requires Python: >=3.6


![chime-frb-constants](https://github.com/CHIMEFRB/frb-constants/workflows/chime-frb-constants/badge.svg)
[![Coverage Status](https://coveralls.io/repos/github/CHIMEFRB/frb-constants/badge.svg?branch=master&t=2TpXqG)](https://coveralls.io/github/CHIMEFRB/frb-constants?branch=master)
![pypi-version](https://img.shields.io/pypi/v/chime-frb-constants)
![black-formatting](https://img.shields.io/badge/code%20style-black-000000.svg)
# CHIME/FRB Constants
A pure-python, lightweight and zero dependency package to access constants used in the CHIME/FRB software projects.

## Installation
```
pip install chime-frb-constants
```

### Optional
If `scipy` is installed in the python environment, `frb-constants` will also expose the physical constants, otherwise it is set to `None`
```python
import chime_frb_constants as constants

constants.phy_const
```

## Usage
```python
import chime_frb_constants as constants

print(constants.K_DM)
4149.377593360996

print(constants.phys_const.speed_of_light)
299792458.0
```

## Changelog

### 2020.07
  - Updated `CHANNEL_BANDWIDTH_MHZ`
  - Fixed errors with `FREQ`
  - Added optional physical constants from `scipy`

### 2020.06.3
  - Fixed error with `CHANNEL_BANDWIDTH_MHZ`
  - Change to `SAMPLING_TIME_MS`
  - New constant `SAMPLING_TIME_S`

### 2020.06.2
  - Added constants `FREQ` and `FREQ_FREQ`

### 2020.06
  - Release on [PYPI](https://pypi.org/project/chime-frb-constants/)
  - All constants are now uppercase
  - All physical constants from `scipy` are not availaible anymore under constants.
datareduction 0.1.0
Package for data reduction, especially using instance selection algorithms.
License: MIT
Requires Python: >=3.6, <3.8

# InstanceSelection

InstanceSelection is a Python module for reducing number of instances in datasets used in classification problems.
The module is implemented as part of an engineering project.

# Instalation
```
    pip install data_reduction
```

# Usage
## Data loading and preparation
The first step is to load and prepare data using DataPreparation:
```
    data = DataPreparation('iris')
```
## Instance selection with selected algoritm
For all algorithms required parameter is instance of DataPreparation. Then you can reduce instances and prepare raport.
```
    alg = DROP1(data, k=3)
    alg.reduce_instances()
```
## Creating raport
After reduction with selected algorithm you can create raport:
```
    rap = Raport(data, alg.red_data, alg.red_lab)
    rap.print_raport(c_type = 'knn')
```

# Results of raporting
```
=============
Classifier:   knn
=============
Raport for original dataset
Count of instances:  105
                 precision    recall  f1-score   support

    Iris-setosa     1.0000    1.0000    1.0000        19
Iris-versicolor     1.0000    1.0000    1.0000        13
 Iris-virginica     1.0000    1.0000    1.0000        13

       accuracy                         1.0000        45
      macro avg     1.0000    1.0000    1.0000        45
   weighted avg     1.0000    1.0000    1.0000        45

Cohen's Kappa: 1.00
===
Training time:  0.0008822999999997805
Predicting time:  0.003322799999999848

Raport for reduced dataset
Count of instances:  21
                 precision    recall  f1-score   support

    Iris-setosa     1.0000    1.0000    1.0000        19
Iris-versicolor     0.7647    1.0000    0.8667        13
 Iris-virginica     1.0000    0.6923    0.8182        13

       accuracy                         0.9111        45
      macro avg     0.9216    0.8974    0.8949        45
   weighted avg     0.9320    0.9111    0.9090        45

Cohen's Kappa: 0.86
===
Training time:  0.0006775000000001086
Predicting time:  0.0024793999999999095

Reduction factor: 80.00 %
```