semvec/pypi-packages huggingface dataset:
geventcron 1.5
Gevent Crontab Scheduler
License: MIT
Keywords: gevent cron scheduler,fengyun

# GeventCron

### 原理:
gevent有个spawn_later()函数，专为定时任务打造...  

他的`缺点`就是，别让gevent调度堵塞了....尽量让你业务逻辑，采用gevent patch模块

正在尝试下，借助spawn_later周期功能，解决堵塞的问题, [查看更多GeventCron相关信息](http://xiaorui.cc)

### 安装方法:
```
pip install geventcron

or

python setup.py install
```

### 使用方法:

```
import time
import requests
import threading
import functools
from datetime import datetime


import geventcron


def async(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        my_thread = threading.Thread(target=func, args=args, kwargs=kwargs)
        my_thread.start()
    return wrapper


@async
def func_1():
    print 'Call func_1'


def func_2():
    print 'Call func_2'


def func_3():
    print 'Call func_3'


#尽量别用堵塞的模块,可以用grequests
def block():
    requests.get("http://www.google.com/")


if __name__ == "__main__":
    scheduler = geventcron.Scheduler(logger_name='task_scheduler')
    scheduler.schedule('task_1', geventcron.Interval("*/1 * * * *"), func_1)
    scheduler.schedule('task_2', geventcron.Interval(2), func_2)
    scheduler.schedule('task_3', geventcron.Interval(3), func_3)
    # scheduler.run_forever()
    scheduler.daemon(flag=True)
    print "daemon"
    time.sleep(100)
```
asynctasks 0.1.4
Asynchronous task scheduler and runner for Python
License: MIT
Keywords: asyncio plan task scheduler async concurrent

asynctasks
==========

|BuildStatus| |Coverage|

Task scheduler based on asyncio. Allows max-concurrency and granularity to be specified. Execution results are printed as a Gantt chart.

Sample Usage
------------

.. code-block:: python

   from asynctasks.executor import Executor
   from asynctasks.executionplan import ExecutionPlan
   import asyncio

   #create an array of tasks
   tasks_dict_array = [{"dependencies": [], "name": "task0"}, {"dependencies": [0], "name": "task1"},
                           {"dependencies": [1], "name": "task2"}, {"dependencies": [0], "name": "task3"},
                           {"dependencies": [], "name": "task4"}, {"dependencies": [], "name": "task5"}]

   #define an async function that should be executed for each task
   async def print_task(loop, task):
      print("Executing task {}".format(task))
      await asyncio.sleep(0.1)

   #create an execution plan for the tasks
   plan = ExecutionPlan().from_dict_array(tasks_dict_array)
   print("\nBEFORE EXECUTION\n{}".format(plan))

   #execute the plan with print_task function and max_concurrency as 2
   Executor(plan, 2, 0.01, print_task).trigger_execution()
   print("\nAFTER EXECUTION\n{}".format(plan.as_gantt()))

Sample Output
-------------

.. code-block:: python

    BEFORE EXECUTION
    task0 Ready
        task1
            task2
        task3
    task4 Ready
    task5 Ready

    Executing task {'dependencies': [], 'name': 'task0', 'start_time': 1497528994.1246588}
    Executing task {'dependencies': [], 'name': 'task4', 'start_time': 1497528994.1246588}
    Executing task {'dependencies': [0], 'name': 'task1', 'start_time': 1497528994.236074}
    Executing task {'dependencies': [0], 'name': 'task3', 'start_time': 1497528994.2370954}
    Executing task {'dependencies': [1], 'name': 'task2', 'start_time': 1497528994.340769}
    Executing task {'dependencies': [], 'name': 'task5', 'start_time': 1497528994.3417716}

    AFTER EXECUTION
          ....................................................................................................
    task0 ...............................
    task1                                   .............................
    task2                                                                    ................................
    task3                                    .............................
    task4 ...............................
    task5                                                                     ...............................


.. |BuildStatus| image:: https://travis-ci.org/vramakin/asynctasks.svg?branch=master
   :target: https://travis-ci.org/vramakin/asynctasks
.. |Coverage| image:: https://coveralls.io/repos/github/vramakin/asynctasks/badge.svg?branch=master
   :target: https://coveralls.io/github/vramakin/asynctasks?branch=master
gdxpy 0.1.2
Manipulate GAMS GDX files as Pandas data structures for convenient computation and visualization
License: MIT
Keywords: gdx gams pandas




1 Purpose
---------

This module provides some convenient functions to process the data contained in a GAMS Data eXchange file (GDX). A GDX stores the values of one or more GAMS symbols, such as sets, parameters variables and equations. With this module, one can load and manipulate GDX symbols as Pandas data structures.

2 Requirements
--------------

- Python (tested with version 3.6).

- up-to-date `numpy <http://www.numpy.org/%E2%80%8E>`_ module.

- up-to-date `pandas <http://pandas.pydata.org/>`_ module.

- GAMS installation with the same architecture of Python (e.g. 32bit Python will work with 32bit GAMS).

Note that if you have a Python version not directly supported by your GAMS distribution, you will need to recompile the GDX-Python bindings. This module will warn you if this is the case and provide you with instructions on how to proceed.

3 Usage
-------

.. code:: python

    # Import modules
    import gdxpy as gp
    import os

    # Create a GDX object
    gdxpath = os.path.join(gp.get_gams_root(), 'testlib_ml', 'trnsport.gdx')
    tdata = gp.GdxFile(gdxpath)

    # Suggested way to programmatically load symbols
    a = tdata.a() # or...
    b = tdata.query('b') # or...
    c = gp.gload('@c', gdxpath)

    # Suggested way to interactively load symbols
    gp.gload('a b c', gdxpath)

    # You can have multiple GDX, arbitrary labels, and domain filtering, e.g.
    # gp.gload('big_symbol', 'long_gdx_file1 long_gdx_file_2','g1 g2',filt='interesting_element')

- By default, a Pandas Series is returned for data, a Pandas Index for sets, and a float for scalars. To change this behavior, choose an appropriate ``reshape`` value to either obtain a tidy DataFrame (``RESHAPE_NONE``), an unstacked Series (``RESHAPE_FRAME``) or a multi-dimensional Panel (``RESHAPE_PANEL``).

- Note the ``@`` in ``gload`` as a shortcut for returning data instead of loading it in the environment.

4 License
---------

The MIT License (MIT)

Copyright (c) 2014-2017 Giacomo Marangoni

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.