lognflow

Log and flow tracking made easy with Python.

Installation

You can install it by:

pip install lognflow

How to use

A simple program to use lognflow would be similar to the following , if the root for all logs is "c:\all_logs\":

	
	from lognflow import lognflow
	import numpy as np
	
	data = np.random.rand(100)
	
	logger = lognflow(r'c:\all_logs\\', time_tag = False)
	logger('This is a test for lognflow save and load')
	logger.save('data', data)
	data = logger.load('data.npy')
	logger.plot('data', data)

Apart form lots of easy logging and loading functionalities, lognflow provides a very nice progress bar that does not make a mess in terminals that do not support \r::

	from lognflow import printprogress
	N = 100
	pbar = printprogress(N)
	for _ in range(N):
		# do_something()
		pbar()

There is also a conviniant way to use multiprocessing in Python. You wish to provide a function name, iterable inputs and shared inputs. Then ask to run the function over the iterable inputs using multiprcessing. Then The multiprocessor is for you. The following is a cross-correlation of two masked verctors using a given ststistical function

	from lognflow import multiprocessor
	
	def masked_cross_correlation(inputs_to_iter_sliced, inputs_to_share):
		""" Calculate the correlation of two masked vectors, then use a given
			statsitcal function to get the stat of the correlation.
		"""
	    vec1, vec2 = inputs_to_iter_sliced
	    mask, statistics_func = inputs_to_share
	    vec1 = vec1[mask==1]
	    vec2 = vec2[mask==1]
	    vec1 -= vec1.mean()
	    vec1_std = vec1.std()
	    if vec1_std > 0:
	        vec1 /= vec1_std
	    vec2 -= vec2.mean()
	    vec2_std = vec2.std()
	    if vec2_std > 0:
	        vec2 /= vec2_std
	    correlation = vec1 * vec2
	    to_return = statistics_func(correlation)
	    return(to_return)
	    
	
	if __name__ == '__main__':
	    data_shape = (1000, 2000)
	    data1 = np.random.randn(*data_shape)
	    data2 = 2 + 5 * np.random.randn(*data_shape)
	    mask = (2*np.random.rand(data_shape[1])).astype('int')
	    statistics_func = np.median
	    
	    inputs_to_iter = (data1, data2)
	    inputs_to_share = (mask, statistics_func)
	    ccorr = multiprocessor(masked_cross_correlation, 
	    					   inputs_to_iter, 
	    					   inputs_to_share)
	    print(f'ccorr: {ccorr}')

Introduction

In this package we use a folder on the HDD to generate files and folders in typical formats such as numpy npy and npz, png, ... to log. A log viewer is also availble to turn an already logged flow into variables. Obviously, it will read the folders and map them for you, which is something you could spend hours to do by yourself. Also there is the nicest progress bar, that you can easily understand and use or implement yourself when you have the time.

Looking at most logging packages online, you see that you need to spend a lot of time learning how to use them and realizing how they work. Especially when you have to deal with http servers and ... which will be a huge pain when working for companies who have their own HPC.

This is why lognflow is handy and straight forward.

Many tests are avialable in the tests directory.

• Free software: GNU General Public License v3
• Documentation: https://lognflow.readthedocs.io.

Features

• lognflow puts all the logs into a directory on your pc
• lognflow makes it easy to log text or simple plots.
• lognflow makes it easy to load variables or directories
• printprogress is one of the best progress bars in Python.
• multiprocessor makes it easy to use multiprocessing in Python.

Author

• Alireza Sadri <Alireza[Dot]Sadri[At]Monash[Dot]edu>

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.