semvec/pypi-packages huggingface dataset:
threefoldlogin 0.0.1
Python package for the threebot authenticator app
Requires Python: >=3.6

# Python package for interacting with threefold login

## Usage

### Initialize the authenticator
```python
from ThreefoldLoginPkg import ThreefoldLogin
import string 
import random 

api_host = 'https://login.staging.jimber.org'
app_id = 'testapp'
seed_phrase = seed_phrase = 'calm science teach foil burst until ' \
              'next mango hole sponsor fold bottom ' \
              'cousin push focus track truly tornado ' \
              'turtle over tornado teach large fiscal'
redirect_url = "/callback"
kyc_backend_url = 'https://openkyc.staging.jimber.org'
authenticator = ThreefoldLogin (api_host,
    app_id,
    seed_phrase,
    redirect_url,
    kyc_backend_url
)
```

### Generate a login request
```python 
allowed = string.ascii_letters + string.digits
state = ''.join(random.SystemRandom().choice(allowed) for _ in range(32))
url = authenticator.generate_login_url(state)
```

### Redirect the user to the giving URL
Redirect the user to `url` 

### Callback
The callback will be send to `https://{app_id}/{redirect_url}`

```python
try:
    authenticator.parse_and_validate_redirect_url(callback_url, state)
    print('successfully validated login attempt')
    if authenticator.is_email_verified():
        print('email is verified')
    else:
        print('email is not verified')
except ValueError:
    print('failed to validate login attempt')
```
detox-bridge 2.0.1a19
A python bridge to the detox greybox testing library

Detox Python Bridge
===========================

.. image:: https://github.com/kpn-digital/py-detox-bridge/workflows/Build%20&%20test%20py-detox-bridge-workflow/badge.svg
    :target:  https://github.com/kpn-digital/py-detox-bridge/actions

.. image:: https://img.shields.io/codecov/c/github/kpn-digital/py-detox-bridge/master.svg
    :target: http://codecov.io/github/kpn-digital/py-detox-bridge?branch=master

.. image:: https://img.shields.io/pypi/v/detox-bridge.svg
    :target: https://pypi.python.org/pypi/detox-bridge

.. image:: https://readthedocs.org/projects/detox-bridge/badge/?version=latest
    :target: http://detox-bridge.readthedocs.org/en/latest/?badge=latest

A bridge to enable python code to use the detox grey-box testing API ( https://github.com/wix/detox )


Requirements
============

NVM
---

The package requires nvm to be installed. Either the NVM environment variable needs to contain the full path of the nvm.sh script, or
the NVM_DIR environment variable needs to point at the root directory of nvm containing the nvm.sh script.

NODE
----

The code emitted by this bridge requires node 7.6.0 or higher.


Python
------

3.7 is required to use the package.

Detox
-----

Follow the getting started guide from detox ( https://github.com/wix/detox/blob/master/docs/Introduction.GettingStarted.md )


Usage
=====

.. code:: python

   from detox_bridge import jsawait, by, detox, device, element, expect, node_with_detox

   app_path = "detox/examples/demo-react-native"

   # Start Node server in app_path root folder that contains node_modules

   with node_with_detox(app_path=app_path, default_timeout=10) as appserver:

       # Detox Config (we could also load this from package.json)

       ios_sim_release = {
           "binaryPath": "ios/build/Build/Products/Release-iphonesimulator/example.app",
           "type": "ios.simulator",
           "name": "iPhone 7 Plus"
       }

       configurations_obj = {"configurations": {"ios.sim.release": ios_sim_release}}

       # Longer timeout since the app may be installed

       appserver(jsawait(detox.init(configurations_obj)), timeout=360)

       # Reload react native

       appserver(jsawait(device.reloadReactNative()))

       # Expectation

       appserver(jsawait(expect(element(by.id('welcome'))).toBeVisible()))

       # Cleanup

       appserver(jsawait(detox.cleanup()))

Development
===========


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

Python 3.7 are required to run the suite suite.

Checkout
--------

After checkout run::

   make app_local_requirements


Running tests
-------------

To run the test suite::

    make test


Once the venv is there you can also run some tests via tox::

    venv/bin/tox -e py35 -- -k <regex>
pywe-subscribe-message 1.0.1
Wechat Subscribe Message Module for Python.
Keywords: Wechat Weixin Subscribe Message

======================
pywe-subscribe-message
======================

Wechat Subscribe Message Module for Python.

Sandbox
=======

* https://mp.weixin.qq.com/debug/cgi-bin/sandbox?t=sandbox/login

Installation
============

::

    pip install pywe-subscribe-message


Usage
=====

::

    from pywe_subscribe_message.subscribemsg import SubscribeMessage, send_subscribe_template_message, send_wxa_subscribe_template_message


Method
======

::

    class SubscribeMessage(BaseToken):
        def __init__(self, appid=None, secret=None, token=None, storage=None):
            super(SubscribeMessage, self).__init__(appid=appid, secret=secret, token=token, storage=storage)

    def send_subscribe_template_message(self, user_id, template_id, data, url=None, scene=None, title=None, miniprogram=None, mini_program=None, miniappid=None, minipagepath=None, appid=None, secret=None, token=None, storage=None):

    def send_wxa_subscribe_template_message(self, user_id, template_id, data, page=None, miniprogram_state=None, lang=None, appid=None, secret=None, token=None, storage=None):
libtimetag 0.9.2
Library for storage and processing of time-correlated single-photon counting (TCSPC) data.
Requires Python: >=3.6

# About
Libtimetag is a library for opening time-correlated single-photon counting data stored in the "small simple time-tagged" (SSTT) file format, and also provides algorithms to process these data, such as fast cross-correlation functions. These functions are fast enough that anti-bunching and fluorescence-correlation spectroscopy (FCS) cross-correlation curves can be visualized live; no hardware correlator is required.

The library was made for two use cases: (i) as a supporting library for other (C/C++/...) applications (e.g., [PHAST](https://www.github.com/rabouwlab/phast)), and (ii) as Python library for data processing and analysis. It is used on a daily basis for data acquisition, processing, and analysis at the lab of [Freddy Rabouw](https://www.uu.nl/medewerkers/FTRabouw) at Utrecht University. The library was originally developed by Stijn Hinterding.


# Installing as Python module
Simply install from pypi using pip:

    pip install libtimetag


# Usage as Python module
Please see the [python notebook](https://github.com/rabouwlab/libtimetag/blob/master/examples/libtimetag_example-notebook.ipynb).


# Installing as C++ library
Libtimetag was developed on Windows using [MSYS2](https://www.msys2.org) (more specifically, the MSYS2 MinGW 64-bit toolchain). The following should work under Windows (MSYS2), and Linux-like OS's. Inside a shell, situated in the project root directory, execute:

	mkdir build
	cd build
	cmake ..
	make
	make install


# Acknowledgements
This work was supported by The Netherlands Center for Multiscale Catalytic Energy Conversion (MCEC), an NWO Gravitation Programme funded by the Ministry of Education, Culture and Science of the government of The Netherlands.


# License
This project is licensed under the MIT license. See the LICENSE file in the project root.