semvec/pypi-packages huggingface dataset:
tempocli 0.2.5.dev4
Command line interface for interacting with Tempo.
License: MIT
Requires Python: >=3.5

# TempoCLI

Command line interface for interacting with Tempo.

[![PyPI version](https://img.shields.io/pypi/v/tempocli.svg)](https://pypi.org/project/tempocli)
[![Python Versions](https://img.shields.io/pypi/pyversions/tempocli.svg)](https://pypi.org/project/tempocli)
[![Build Status](https://github.com/awiddersheim/tempocli/actions/workflows/cicd.yml/badge.svg)](https://github.com/awiddersheim/tempocli/actions/workflows/cicd.yml)
[![License](https://img.shields.io/pypi/l/tempocli.svg)](https://github.com/awiddersheim/tempocli/blob/main/LICENSE)
[![Docker Pulls](https://img.shields.io/docker/pulls/awiddersheim/tempocli.svg)](https://hub.docker.com/r/awiddersheim/tempocli)

## Introduction

Ease repetitive Tempo tasks by using templates to fill in recurring
items without having to use web interface. Templates are yaml formatted
files that are semi-flexible in allowing what can be created.

The `author_account_id` can be obtained by going to your profile in
JIRA and copying the ID from the URL.

```
----
author_account_id: foo

issues:
  # Will use current date if one can't be determined.
  - issue: INT-8
    time_spent: 30m
    start_time: "9:30AM"

  # Can specify day of week easily.
  - issue: INT-10
    time_spent: 1h
    start_time: Monday at 9AM

  # Full on datetime with override.
  - issue: INT-11
    time_spent: 90s
    start_time: "2018-08-05 11:00:00"
    author_account_id: bar

  # Pass in extras that aren't exposed in DSL.
  # https://tempo-io.github.io/tempo-api-docs/#worklogs
  - issue: INT-11
    time_spent: 1h
    start_time: 8am
    extras:
      remainingEstimateSeconds: 300
```

## Installation

```
$ pip install tempocli
```

## Running

```
$ tempocli --config <config> create --template <template>
```

## Configuration

By default, `~/.tempocli.yml` is the path used for the configuration
file but that can be changed with the `--config` option during
invocation. The configuration file should look like this:

```
---
token: <token>
```

It is also possible to specify the token using the `TEMPOCLI_TOKEN`
environment variable.

## Docker

### DockerHub

```
$ docker run \
    --rm \
    --tty \
    --interactive \
    --volume ~/.tempocli.yml:/home/tempocli/.tempocli.yml:ro \
    --volume /some/dir/with/templates:/templates:ro \
    awiddersheim/tempocli \
    create --template /templates/template.yml
```

### Building Locally

```
$ docker build --tag tempocli --target prod .
```

## Development

```
$ pip install --editable .
$ tempocli --help
```

## Testing

```
# Install development packages (preferably in a virtualenv)
$ pip install --editable .[dev]

# Run tests
$ pytest

# Run tests for available Python interpreters
$ tox

# Linting
$ flake8

# Can also lint in tox as well
$ tox -e flake8
```
cur 0.0.2
CUR Decomposition
License: MIT
Requires Python: >=3.6

# CUR Decomposition

[<img src="https://travis-ci.com/blester125/CUR_Decomposition.svg?branch=master" alt="Build Status">](https://travis-ci.com/blester125/CUR_Decomposition)

CUR Decomposition as described in [Mining of Massive Datasets](http://www.mmds.org/), page 406.

Currently it only works with Numpy arrays but the point of CUR is to keep C and R sparse if M is sparse so I plan to add support for Scipy Sparse arrays.

### Usage

```
M = np.array([ ... ])
r = int
from cur import cur_decomposition
C, U, R = cur_decomposition(M, r)
```