semvec/pypi-packages huggingface dataset:
pickpeak 0.0.10
PickPeak: a PyQtGraph-based tool for fast peak selections of spectrum ploats.
License: MIT
Keywords: spectra peak selection GUI

Pick Peak
=======================

Pick Peak is a `PyQtGraph <http://www.pyqtgraph.org/>`_-based tool. It aims
to provide a graphical user interface to allow the user easily selecting peaks of a
spectrum ploat.


----

README
""""""""""""""""" 

**Introduction**

To do.

**Installation**

For standard Python installations, install pickpeak using pip:

.. code:: bash

    pip install -U pip setuptools
    pip install pickpeak

**Usage**

.. code:: python

	from pickpeak.pickpeak import pickpeak as pp
	import numpy as np
	spectrum = np.random.rand(20) #sample of spectrum
	peaks = pp(spectrum) #spectrum shoud be type numpy.ndarray 1D
	print peaks # list of peaks


**Requirements**

* `Python 2.7 <https://www.python.org/downloads/>`_.
* `Setuptools <https://setuptools.readthedocs.io/en/latest/>`_.
* `PyQtGraph <http://www.pyqtgraph.org/>`_.
* `PyQt5 <https://pypi.python.org/pypi/PyQt5/5.9>`_.
* `NumPy <http://www.numpy.org/>`_.

**Status**

* We choose to move to PyQtGraph to have more GUI resources without losing simplicity.
cvloop 0.3.5
cvloop allows online video transformation and evaluation with OpenCV. Designed for jupyter notebooks.
License: MIT
Keywords: OpenCV,cv2,video,loop,jupyter,notebook

cvloop
======

Provides cvloop, a way to show opencv video loops. Designed for jupyter notebooks.

**Simple example**: Show webcam feed.

.. code-block:: python

    from cvloop import cvloop
    cvloop()

**More complex example**: Show webcam feed side by side with inverted image.

.. code-block:: python

    from cvloop import cvloop
    cvloop(function=lambda frame: 255 - frame, side_by_side=True)

**Complex example**: Show video file with background extraction (See `OpenCV Documentation`_; `Video`_).

.. code-block:: python

    from cvloop import cvloop
    import cv2

    def mog2(frame):
        return mog2.fgbg.apply(frame)
    mog2.fgbg = cv2.createBackgroundSubtractorMOG2()

    cvloop('768x576.avi', function=mog2, side_by_side=True)

**More examples**: For more examples check out the `examples notebook`_.


Install
-------

You can simply install cvloop using pip (make sure to install matplotlib, numpy, OpenCV and Jupyter):

.. code-block::

    pip install cvloop

Or, if you are using conda and don't want to worry about requirements, just use conda-forge:

.. code-block::

    conda config --add channels conda-forge
    conda install cvloop


Requirements
------------

(Recommended versions, additionally tested versions in parentheses.)

-  Python 3.6
-  OpenCV 3.2
-  Jupyter 4.3.1
-  matplotlib 2.0.0
-  numpy 1.12.0


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

To contribute, just fork the repository and create pull requests.

To publish, you need a couple of additional tools:
    - `gpg` to sign the packages
    - `twine` to upload them
    - `shasum` to calculate the checksum for conda-forge
    - `hub` to create the pull request for conda-forge

.. _`OpenCV Documentation`: http://docs.opencv.org/3.1.0/db/d5c/tutorial_py_bg_subtraction.html
.. _`Video`: https://github.com/opencv/opencv_extra/tree/master/testdata/cv/video
.. _`examples notebook`: https://github.com/shoeffner/cvloop/blob/master/examples/cvloop_examples.ipynb
DTWpy 0.1.0
Module for Computing and Visualizing Dynamic Time Warping Alignment
License: MIT
Keywords: DTWpy,Module for Computing and Visualizing Dynamic Time Warping Alignment,Hafiz Muhammad Gulzar

Comprehensive Python module for computing and visualizing dynamic time warping alignment: DTWPy
Gulzar, Hafiz Muhammad

Abstract:
Dynamic Time Warping (DTW) is a well-known technique used to determine alignment between two temporal sequences. DTW has been used in wide range of applications and it can be applied on any data which can be represented as linear sequence. Existing DTW libraries have out dated implementation of core DTW algorithm, which result in low performance or are inapplicable for big sequences. The aim of this thesis is to present, a comprehensive DTW library, encapsulating t implementation of DTW variants with recently proposed efficient algorithms. In this thesis I presented a python module for computing and visualizing DTW alignment: DTWPy. DTWPy has implementation of classical DTW, almost all the DTW variants proposed in literature to date, with recently proposed performance efficient algorithms i.e. fastDTW and DDTW. Algorithms implemented in DTWPy are tested thoroughly and give correct results. Correctness is verified by comparing it with existing R implementation. Furthermore, I have compared algorithms implemented, and performance of DTWPy with existing libraries. DTWPy have the most comprehensive implementation of DTW algorithms present in literature to date, and is applicable on large temporal sequence. The architecture of DTWPy is designed to be flexible, to scale and accommodate possible future improvements.


read more on: https://brage.bibsys.no/xmlui/handle/11250/299598
toggl-client 0.1.2
Cliente para a toggl API
License: MIT
Keywords: toggl toggl-api toggl-client

Toggle API Client
=======================

Um cliente simples para algumas funcionalidades básicas da api da [TOGGL](https://toggl.com)

#### Configuração inicial

Para o cliente funcionar ele precisa da sua token de acesso, que você encontra nessa [URL](https://toggl.com/app/profile)

***TODO - IMAGEM DO SITE***

Com a token em mãos digita no terminal:
```
    $: tgc --config
```


#### Exemplo

Iniciar uma nova tarefa sem nome:

```
    $: tgc
```
Iniciar uma nova tarefa com nome:

```
    $: tgc 'nome da tarefa'
```
Iniciar uma nova tarefa com a branch ativa como nome:

```
    $: tgc --branch
```

Iniciar uma nova tarefa com tags:

```
    $: tgc --tags 'novatag' 'tagtoog'
```
Finalizar uma tarefa:

```
    $: tgc --finalizar
    $: tgc --f
```

#### TODO

- [x] Iniciar tarefa
- [x] Finalizar tarefa
- [x] Tarefa com nome
- [x] Branch ativa
- [x] tags
- [ ] tests
- [ ] Cache(Diminuir alguns requests e aumentar o desempenho)
    * Foi implementado de algumas funcionalidades
- [x] Arquivo de configuração em pasta do sistema
- [x] SETUP
- [x] PIP install