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| """ | |
| Partial Correlation in Python (clone of Matlab's partialcorr) | |
| This uses the linear regression approach to compute the partial | |
| correlation (might be slow for a huge number of variables). The | |
| algorithm is detailed here: | |
| http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression | |
| Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y}, |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| Borrowed from: http://stackoverflow.com/questions/2573135/python-progression-path-from-apprentice-to-guru | |
| I've been learning, working, and playing with Python for a year and a half now. As a biologist slowly making the turn to bio-informatics, this language has been at the very core of all the major contributions I have made in the lab. I more or less fell in love with the way Python permits me to express beautiful solutions and also with the semantics of the language that allows such a natural flow from thoughts to workable code. | |
| What I would like to know is your answer to a kind of question I have seldom seen in this or other forums. This question seems central to me for anyone on the path to Python improvement but who wonders what his next steps should be. | |
| Let me sum up what I do NOT want to ask first ;) | |
| I don't want to know how to QUICKLY learn Python | |
| Nor do I want to find out the best way to get acquainted with the language |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # | |
| # The MIT License (MIT) | |
| # Copyright (c) 2019 Duncan Macleod | |
| # | |
| # 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 |
UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli
Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.
Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.
Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!
| #! usr/bin/python | |
| #coding=utf-8 | |
| # http://gree2.github.io/python/2016/05/14/python-with-docker-redis | |
| from __future__ import print_function | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import sys, os, time |
All your notes, scripts, config files and snippets deserve version control and tagging!
gist is a simple bash script for gist management.
It is lightweight(~700LOC) and dependency-free! Helps you to boost coding workflow.
