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.. _quickstart:
User Guide
###############
There is no tutorial *per-se* in **easydev** since it is a set of
versatile and independent functionalities.
Here below, we describe some of available tools.
.. contents::
Progress bar
==============
Many tasks may take a while to finish especially when using a loop. A progress bar is usually
quite handy to keep track of the computation. **easydev** provides a Progress bar
that can be used in python, IPython, and IPython notebooks::
from easydev import Progress
p = Progress(1000)
for i in range(0, 1000):
# do something.
p.animate(i+1)
Swapping key and value in a dictionary with swapdict
=======================================================
In Python, dictionaries are used everywhere. Sometimes, it is convenient
to swap the keys and values (values become keys and vice-versa).
The :func:`~easydev.tools.swapdict` can be used for that purpose:
.. doctest::
>>> from easydev import swapdict
>>> d = {'a':1, 'b':2}
>>> inv = swapdict(d)
>>> inv
{1: 'a', 2: 'b'}
.. note:: values must be unique
The tools module
======================
In addition to the *swapdict* function, the :mod:`easydev.tools` module has
many more functionalities.
.. autosummary::
:nosignatures:
~easydev.tools.precision
~easydev.tools.check_param_in_list
~easydev.tools.shellcmd
~easydev.tools.execute
~easydev.tools.touch
~easydev.tools.swapdict
~easydev.tools.mkdirs
~easydev.tools.AttrDict
~easydev.tools.DevTools
For :class:`~easydev.tools.DevTools` and :class:`~easydev.tools.AttrDict`,
please see :ref:`devtools` and :ref:`attrdict` sections, respetively.
Check validity of a values
----------------------------
The :func:`~easydev.tools.check_param_in_list` is used to check the validity of a parameter::
>>> mode = "on"
>>> check_param_in_list(mode, ["on", "off"])
True
.. _attrdict:
AttrDict
-------------
This is a very convenient class to expose keys of a dictionary-like object as
attributes:
.. code-block:: python
>>> from easydev import AttrDict
>>> d = AttrDict({'val1':1})
>>> d.val1
1
This works also if you want to set a value::
d.val2 = 2
.. _devtools:
The DevTools class
-------------------------
Little by little, small tools have been added in **easydev**. To make life easier such tools
have been gatherered within a single class called :class:`easydev.tools.DevTools`.
Usually, we can create just an instance and add it in a class as an accessible
set of functionalities. Consider the following example:
.. code-block:: python
:linenos:
from easydev import DevTools
class MyTest(object):
def __init__(self):
self._devtools = Devtools()
def plot_in_range(self, x):
self._devtools.check_range(x, -2,2)
# do something
def sum(self, x):
# sometimes it is a value, sometimes a list but
# the function to be used accepts only list
x = self._devtools.to_list(x)
# do something with the list
def switch(self, x):
# this function will only understand x if it is a
# value between 1 and 3 so let us check that
self._devtools.check_param_in_list(x, [1,2,3])
if x == 1:
#do something
else:
#do something
The same code without DevTools would be twice as long. Consider for example the
line 7. You would need to type::
if x < -2:
raise ValueError('the value provided is incorrect....')
if x >2:
raise ValueError('the value provided is incorrect....')
logging
=========
The logging module uses the standard Python logging module and colorlog package
to provide a simple interface to include in your own library. For instance:
::
>>> from easydev import Logging
>>> log = Logging("easydev", "WARNING")
>>> log.warning("using a logging")
>>> log.debug("debug message not shown")
>>> log.level = "DEBUG"
>>> log.debug("debug message")
WARNING [easydev]: using a logging
DEBUG [easydev]: debug message
Timer
=========
Timer populate a list variable with time spent in **with** statements
::
from easydev import Timer
import time
times = []
with Timer(times):
time.sleep(0.1)
with Timer(imes):
time.sleep(0.2)
sum(times)
Profiling
================
A quick way to check the profiling of a specific function or method is to use
the do_profile decorator (requires the package line_profiler)::
from easydev import do_profile
@do_profile()
def test(a, b):
a **2
a + b
a*b
import time
time.sleep(0.1)
test(1,2)
Data related
==================
You can split a list into chunks using
:func:`~easydev.chunks.split_into_chunks`:
.. doctest::
>>> from easydev import split_into_chunks
>>> data = [1,1,2,2,3,3]
>>> list(split_into_chunks(data, 3))
[[1, 2], [1, 3], [2, 3]]
Note that it is an iterator (hence the list cast).
Sphinx tools
===============
Sphinx is a framework that ease the development of HTML documentation. I personally use Sphinx for all kind of projects, not only documentation of software. In order to have a uniform documentation a theme called **standard** is provided in the share/ directory of **easydev**. Moreover, **easydev** provides an easy way to obtained the path of this theme::
>>> from easydev import *
>>> p = get_path_sphinx_themes()
you can then check the presence of the themes::
>>> import os
>>> themes = os.listdir(p)
>>> 'standard' in themes
True
You can then use this path in your sphinx configuration file (conf.py). Here is a
piece of code extracted from the **conf.py** of this package::
>>> import easydev
>>> html_theme = 'standard' # one theme provided in easydev
>>> html_theme_path = [easydev.get_path_sphinx_themes()]
Sphinx configuration file comes with lot of extensions from Sphinx itself or other packages (e.g., numpy or matplotlib). A useful extension is called copybutton (Found on Python official documentation website). As far as I known, it is not an official sphinx extension. It is available within **easydev**.
To use it, in your sphinx configuration file (conf.py), just add::
import easydev
extensions.append('easydev.copybutton')
It will copy the file in source/_static so your configuration file should set::
html_static_path = ["source/_static"]
Multisetup
=============
Imagine you have tree structure with a bunch of projects::
|-- pypiview
| |-- setup.py
| |-- pypiview
| | |-- __init__.py
| | |-- pypiview.py
`-- spectrum
| |-- setup.py
| |-- spectrum
| | |-- __init__.py
If you decided to install all those packages, you need to type::
cd pypiview
python setup.py install
cd ../
cd spectrum
python setup.py install
cd ../
This can be cumbersome if you've got lots of packages are do those operations
regularly. The module :mod:`~easydev.multisetup` provides a tool to simplify the
building of several python packages that are within the same directory. Arguments are
the same as those of setup.py.
Simply create a python file that contains the following code::
from easydev import Multisetup
if __name__ == '__main__':
import sys
packages = ['spectrum', 'pypiview']
mysetup = Multisetup(curdir='.', commands=sys.argv[1:], packages=packages)
mysetup.run()
Create a package layout in one command
=======================================
The package :mod:`~easydev.package` can build a package layout automatically. The type of layout is quite simple but alloas a quickstart:
.. code-block:: python
>>> from easydev import PackageBuilder
>>> p = PackageBuilder("myPackage")
>>> p.buildPackage()
a package is built in the directory "myPackage". You can go in it and type::
python setup.py install
Of course, no modules are to be found but it is a valid package. Besides, you should edit the setup.py file to set the version, author, email and so on.
You can also use the executable **easydev_buildPackage** provided with easydev.
Multiprocessing
====================
A :mod:`~easydev.multicore` class is provided to perform multiprocessing tasks. It allows to create
a list of jobs to be run in an asynchronous way. In other words your jobs do not need to communicate
between them.
Each job must be a function with arguments and optional arguments but must return an object (that will be stored in the results attribute). Typically, you will use this class as follows:
.. code-block:: python
>>> from easydev.multicore import MultiProcessing
>>> def test_func(n):
... import time
... time.sleep(n)
... return n
>>> t = MultiProcessing(maxcpu=4) # default is the number of CPU (returned by cpucount function)
>>> t.add_job(test_func, 2)
>>> t.add_job(test_func, 1)
>>> t.run()
The :meth:`add_job` takes as input a function name followed by a lost of arguments. You can then introspect individual results::
t.results
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