Donne Martin donnemartin

Comprehensions in Python the Jedi way

Beautiful is better than ugly. Explicit is better than implicit.

I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

This re-styles your sublime text sidebar to be dark, it fits default Monokai theme.

Save the Default.sublime-theme file into packages/Theme - Default, make a backup of your original if you want to be able to go back easily.

Based on:

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

An active AWS account. First time sign-ups are eligible for the free tier for a year
One Micro Tier EC2 Instance
With AWS we will use the stock Ubuntu Server AMI and customize it.
Anaconda for Python.
Coffee/Beer/Time

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

Introduction

An introduction to curl using GitHub's API.

The Basics

Makes a basic GET request to the specifed URI

curl https://api.github.com/users/caspyin

	setlocal tabstop=4
	" column highlight for pep8; thou shall not pass 79 columns
	highlight ColorColumn ctermbg=090
	let &colorcolumn=join(range(80,80),",")

	" set text width, tabs, and other goodies for Python
	setlocal softtabstop=4
	setlocal shiftwidth=4
	setlocal textwidth=79
	setlocal smarttab

	##################################################################
	## file: .bash_profile ##
	## version: 1.1 ##
	#---------------------------------------------------------------##
	## Changlog ##
	## ##
	## 1.1 ##
	## -colorized ps1 ##
	## - added table of contents ##
	## 1.0 ##