semvec/pypi-packages huggingface dataset:
python-dynamodb-timeseries 0.2.2
Library for working with timeseries data on dynamodb
License: MIT

# DynamoDB TimeSeries

[![Build Status](https://api.travis-ci.com/sotaog/python-dynamodb-timeseries.svg?branch=master)](https://travis-ci.com/sotaog/python-dynamodb-timeseries)

A set of libraries that make it easier to use DynamoDB as a timeseries datastore.

# Installation
You can install DynamoDB TimeSeries from PyPI:

```
pip install python-dynamodb-timeseries
```

DynamoDB TimeSeries is supported on Python 3.6 and above.

## Table Resolver

Helps partition your data into tables by hour, day, month, or year.

## Timeseries

Put datapoints singly or in batches, query the tags in parallel across tags and assemble the results. Uses Table Resolver to partition data.

## Datapoints

Datapoints are "narrow", meaning a datapoint consists of a tag, timestamp (ms since the epoch), and value. These are stored as individual items in DynamoDB.

## Examples

```python
from dynamodb_timeseries import MONTHLY, Timeseries

table_name_prefix = 'timeseries-testing'
timeseries = Timeseries(table_name_prefix, interval=MONTHLY, regions=['us-west-2', 'us-east-2'])
tag = 'example-tag'
timestamp = 1555722540000
value = 123.45
timeseries.put(tag, timestamp, value)
timeseries.put_batch([[tag, timestamp, value]])
datapoints = timeseries.query(['example-tag'])
```

## Environment Variables

`DYNAMODB_TIMESERIES_REGIONS` Regions to create tables in. You can set this rather than passing the argument to each instance of TimeSeries.
pyMSA 0.8.1
Scoring Multiple Sequence Alignments with Python
License: MIT
Requires Python: >=3

<p align="center">
  <br/>
  <img src=resources/pymsa.png alt="pyMSA">
  <br/>
</p>

# Scoring Multiple Sequence Alignments with Python
[![Build Status](https://img.shields.io/travis/benhid/pyMSA.svg?style=flat-square)](https://travis-ci.org/benhid/pyMSA)
[![PyPI License](https://img.shields.io/pypi/l/pyMSA.svg?style=flat-square)]()
[![PyPI Python version](https://img.shields.io/pypi/pyversions/pyMSA.svg?style=flat-square)](https://pypi.org/project/pyMSA/)

pyMSA is an open source software tool aimed at providing a number of scores for
multiple sequence alignment (MSA) problems. A [tutorial](resources/tutorial-pymsa.pdf) about pyMSA is available in the resources folder of the proyect.

## Features

Score functions implemented:

* Sum of pairs,
* Star,
* Minimum entropy,
* Percentage of non-gaps,
* Percentage of totally conserved columns *and*
* STRIKE (**S**ingle s**TR**ucture **I**nduced **E**valuation).

## Downloading

To download PyMSA just clone the Git repository hosted in GitHub:
```bash
$ git clone https://github.com/benhid/pyMSA.git
$ python setup.py install
```

Alternatively, you can install it with `pip`:
```bash
$ pip install pyMSA
```

## Usage
An example of running all the included scores is located in the [`example`](examples/) folder.

<p align="center">
  <br/>
  <img src=resources/terminal.png width=600 alt="Terminal session">
  <br/>
</p>

## Authors
### Active development team
* [Antonio Benítez-Hidalgo](https://benhid.com/) <antonio.b@uma.es>
* [Antonio J. Nebro](http://www.lcc.uma.es/~antonio) <antonio@lcc.uma.es>

## License
This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.
stwberlin-menus 0.1.0
No description has been added so far.
License: MIT
Requires Python: >=3.6
Keywords: stwberlin_menus

===============================
stwberlin menus
===============================

.. image:: https://travis-ci.com/romnnn/stwberlin_menus.svg?branch=master
        :target: https://travis-ci.com/romnnn/stwberlin_menus
        :alt: Build Status

.. image:: https://img.shields.io/pypi/v/stwberlin_menus.svg
        :target: https://pypi.python.org/pypi/stwberlin_menus
        :alt: PyPI version

.. image:: https://img.shields.io/github/license/romnnn/stwberlin_menus
        :target: https://github.com/romnnn/stwberlin_menus
        :alt: License

.. image:: https://readthedocs.org/projects/stwberlin-menus/badge/?version=latest
        :target: https://stwberlin-menus.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status

.. image:: https://codecov.io/gh/romnnn/stwberlin_menus/branch/master/graph/badge.svg
        :target: https://codecov.io/gh/romnnn/stwberlin_menus
        :alt: Test Coverage

""""""""

Your short description here. `romnnn.github.io/stwberlin_menus <https://romnnn.github.io/stwberlin_menus>`_

.. code-block:: console

    $ pip install stwberlin_menus

See the `official documentation`_ for more information.

.. _official documentation: https://stwberlin-menus.readthedocs.io

.. code-block:: python

    import stwberlin_menus

Development
-----------

For detailed instructions see `CONTRIBUTING <CONTRIBUTING.rst>`_.

Tests
~~~~~~~
You can run tests with

.. code-block:: console

    $ invoke test
    $ invoke test --min-coverage=90     # Fail when code coverage is below 90%
    $ invoke type-check                 # Run mypy type checks

Linting and formatting
~~~~~~~~~~~~~~~~~~~~~~~~
Lint and format the code with

.. code-block:: console

    $ invoke format
    $ invoke lint

All of this happens when you run ``invoke pre-commit``.

Note
-----

This project is still in the alpha stage and should not be considered production ready.
imagescore 1.1.0
Calculates the similarity score between images
License: MIT

## Bjorn score
[![Build Status](https://travis-ci.com/sandjaie/image_score.svg?branch=master)](https://travis-ci.com/sandjaie/image_score)

![PyPI](https://img.shields.io/pypi/v/imagescore.svg)

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/imagescore.svg)

### Calculate image similarity score

#### Tools used:
```
Language: Python
Dev Modules Used: 
- opencv-python
- scikit-image
- click
Test Modules Used:
- tox
- pytest
- pylint
- pytest-cov
Build and Deployment: Travis-ci
App Repository: PyPi 
```
#### Installation:<br>
`pip3 install imagescore`

#### How to run:<br>
`imagescore --infile 'input.csv' --outfile 'output.csv'`

#### The options are:<br>

        --infile  | -i [str]    -- [Input file path]
        --outfile | -o [str]    -- [Output file path]
        --height  | -h [int]    -- [Optional: height to be resized, default = 4096]
        --width   | -w [int]    -- [Optional: width to be resized, default = 4096]

Expected Input: csv file with images and its absolute path <br>
Expected Output: csv file with images and its absolute path, image score and elapsed time in secs. <br>


#### Developer mode: <br>
The application is written in python3. Hence create a virtualenv with python3 and install the dependencies from `requirements.txt`. 
```
virtualenv --python=python3 venv
pip install -e .
```

The unit test cases are located in tests directory. Install the dependencies `requrements-dev.txt`. 
```
pip install -r requirements-dev.txt
tox
```

#### Deployment: <br>
The python package is build with the travis.yml script when there are changes on the master branch. 
To push a new version:
Make the changes in the application, push it to any feature branch and merge with master.<br>
To deploy a new version tag it. e.g 
```
git tag -a v1.1.0
git push origin v1.1.0
```
When a tag is pushed, travis starts building and uploads the application to pypi<br>
Pypi Release: https://pypi.org/project/imagescore/<br>
Github Release: https://github.com/sandjaie/image_score/releases<br>
Travis Builds: https://travis-ci.com/sandjaie/image_score/<br>

Manual Deployment:<br>
We are using twine to upload the artifact to Pypi registry.
Pypi registry needs an account to be created. Enter the credentials in the cli when prompted.
```
pip3 install twine
python3 setup.py sdist bdist_wheel
twine upload dist/*
```