semvec/pypi-packages huggingface dataset:
jiracall 0.0.3
JAPI is jira API

Linux python package for getting info from task on jira server
Home-page: https://github.com/CoolCoderCarl/JAPI
Author: Kir
Author-email: 123qwekir2wl@gmail.com
License: MIT Licensse
Description: UNKNOWN
Platform: UNKNOWN
Requires-Python: >=3.7
rl-api-client 1.0.dev2
RedisLabs Redis Enterprise Python API Client

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.


# Overview

RLAPIClient is meant to provide an easy method of using the RS REST API.

Install:         `pip install rl-api-client`
Build:           `make build`
Publish to PyPi: `make publish`
sufarray-kkto 0.1.1
Suffix Array for all-substring search
Requires Python: ~=3.5

# sufarray: implementation of suffix array

It uses prefix doubling for calculating the suffix array, which is O(n
log n) time.  When searching for all occurrences of a string, it takes
O(log n) time.  Further occurrences takes constant time.

At present the implementation is Python only.  This has the drawback
that the speed is not comparable with those of other languages.

## Usage

To construct a suffix array of string `s`:

    import sufarray
    sarray = sufarray.SufArray(s)

After that, you can get the actual array with:

    sarray.get_array()

This produces a permutation `a` of `range(len(s))`, where the values
`a[0]`, `a[1]`, etc. are such that the suffixes `s[a[i]:]` is sorted.

Other than that, you can find the positions where a string `n` appears
within `s` using:

    sarray.find_all(n)

This is a generator function, yielding all the possible positions.

## Implementations

Two implementations are provided.  The simple version is called
`SufArrayBruteForce`, and a version `SufArrayPD` is provided which
calculates the suffix array using prefix doubling, and augment the
result with a LCP (Longest common prefix) array for use with
searching.  The latter has better bounded performance guarantees, but
with arrays of small text it is slower.  The default is `SufArrayPD`.
You can use the brute force version using
`sufarray.SufArrayBruteForce`.
cdf-ifs-file-extractor 0.1.2
Python package for extracting files from IFS to CDF. Copyright 2020 Cognite AS
Requires Python: >=3.5

<a href="https://cognite.com/">
    <img src="https://raw.githubusercontent.com/cognitedata/cognite-python-docs/master/img/cognite_logo.png" alt="Cognite logo" title="Cognite" align="right" height="80" />
</a>

# CDF IFS file extractor
[![build](https://webhooks.dev.cognite.ai/build/buildStatus/icon?job=github-builds/cdf-ifs-file-extractor/master)](https://jenkins.cognite.ai/job/github-builds/job/cdf-ifs-file-extractor/job/master/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)

**In progress**

## General
The cdf-ifs-file-extractor allows continuous uploading, changing and deletion of files stored in a local directory
and connect those files to the corresponding assets with some metadata.
Running on a continuous schedule the extractor moves old mapping_files into an Archive and uploads new files if it is
fed with a new mapping file. Feedback of unsuccessful upload attempts is given via the two files unknown_files.csv and
unknown_assets.csv.

## Structure of the Mapping file
The extractor expects the mapping file to have a specific format. This requires the following columns:
- FILE_NAME
- DOC_NO
- DOC_REV
- DOC_SHEET
- DOC_CLASS
- DOC_CLASS_NAME
- DOC_FORMAT
- DOC_FORMAT_NAME
- DOC_TITLE
- DOC_RESP_SIGN
- LETTER_DATE
- LAST_UPDATED: to be checked if this column is really included
- DOC_REFERENCE_OBJECT
- OPERATION: allows one of the three values N, C, D to upload a new file or change or delete an existing one

## Requirements local directory
The local directory needs to contain
- the desired files to operate on
- a folder with the name Archive
- the mapping file

## Parameters
The parameters for the function are
- project:
- api_key_name: name of the environment variable storing the api key
- base_url
- client_name
- client_timeout
- log_path
- log_level
- path: path of the local directory
- mapping_file: file name

## Feedback of the extractor
For non developing users the extractor provides feedback to the user for unsuccessful file operation attempts in two
cases
- The asset_id specified in the mapping file does not exist: these ids are stored together with the corresponding file
name in unknown_assets.csv
- The specified file does not exist: unknown_files.csv

Both files will be created in the end of a run of the extractor and archived as soon as the extractor is running the
next time.
SCPSyncer 0.1
SCP syncer
Keywords: scp sync syncer

# SCPSyncer
SCPSyncer is a python module that syncs files to a remote machine.
It does this by calculating file hashes on both sides, and pushing the files that have different hashes to the remote machine.

## Configuration files
There are 2 files that configure the behaviour of syncing

### .syncconfig
`.syncconfig` is a YAML file with the sync configuration
```yaml
CONFIG_NAME:
  inherit: CONFIG_NAME OF OTHER CONFIGURATION TO INHERIT MISSING KEYS FROM
  host: IP_OR_HOSTNAME
  user: USERNAME
  password: PASSWORD
  target_path: /PATH/ON/REMOTE
  commands:
    sync_start: COMMAND TO EXECUTE BEFORE SYNCING
    post_hash: COMMAND TO EXECUTE AFTER HASHES ARE CALCULATED
    sync_end: COMMAND TO EXECUTE AFTER SYNCING
  diff_extensions: ['.py', '.json']   # EXTENSIONS OF FILES THAT SHOULD HAVE DIFFS PRINTED
  diff_only: SET TO TRUE IF IT SHOULD JUST CALCULATE HASHES AND PRINT DIFFS
```

### .syncignore
A text file with globs of files to ignore when syncing - same structure as .gitignore file.
Multiple `.syncignore` files can be in (sub)directories